diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 698741c02..b9f9c3763 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 4.1.22
+current_version = 4.1.23
 commit = False
 tag = False
 
diff --git a/dev/local/setup.cfg b/dev/local/setup.cfg
index 602c4ff5e..56dadceb4 100644
--- a/dev/local/setup.cfg
+++ b/dev/local/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = Delphi Development
-version = 4.1.22
+version = 4.1.23
 
 [options]
 packages =
diff --git a/docs/api/client_libraries.md b/docs/api/client_libraries.md
index 8bea7f667..5d4abb956 100644
--- a/docs/api/client_libraries.md
+++ b/docs/api/client_libraries.md
@@ -8,39 +8,39 @@ nav_order: 1
 
 For anyone looking for COVIDCast data, please visit our [COVIDCast Libraries](covidcast_clients.md).
 
-We are currently working on fully-featured Epidata clients for R and Python. They are not ready
-for release yet, but you can track our development progress and help us test them out at:
+A full-featured Epidata client for R is available at
+[epidatr](https://github.com/cmu-delphi/epidatr) and
+[also on CRAN](https://cran.r-project.org/web/packages/epidatr/index.html).
 
-* [epidatr](https://github.com/cmu-delphi/epidatr)
-* [epidatpy](https://github.com/cmu-delphi/epidatpy)
+We are currently working on a new full-featured Epidata client for Python. It is not ready
+for release yet, but you can track our development progress and help us test it out at
+[epidatpy](https://github.com/cmu-delphi/epidatpy).
 
 In the meantime, minimalist Epidata clients remain available for
-[JavaScript](https://github.com/cmu-delphi/delphi-epidata/blob/master/src/client/delphi_epidata.js),
 [Python](https://github.com/cmu-delphi/delphi-epidata/blob/master/src/client/delphi_epidata.py),
+[JavaScript](https://github.com/cmu-delphi/delphi-epidata/blob/master/src/client/delphi_epidata.js),
 and
-[R](https://github.com/cmu-delphi/delphi-epidata/blob/master/src/client/delphi_epidata.R).
+[R (legacy)](https://github.com/cmu-delphi/delphi-epidata/blob/master/src/client/delphi_epidata.R).
 The following samples show how to import the library and fetch Delphi's COVID-19
-Surveillance Streams from Facebook Survey CLI for county 06001 and days
+Surveillance Streams from Facebook Survey CLI for county 06001, and days
 `20200401` and `20200405-20200414` (11 days total).
 
-### JavaScript (in a web browser)
-
-The minimalist JavaScript client does not currently support API keys. If you need API key support in JavaScript, contact delphi-support+privacy@andrew.cmu.edu.
+### R
 
-````html
-<!-- Imports -->
-<script src="delphi_epidata.js"></script>
-<!-- Fetch data -->
-<script>
-  EpidataAsync.covidcast('fb-survey', 'smoothed_cli', 'day', 'county', [20200401, EpidataAsync.range(20200405, 20200414)], '06001').then((res) => {
-    console.log(res.result, res.message, res.epidata != null ? res.epidata.length : 0);
-  });
-</script>
+````R
+# [Optional] configure your API key, if desired
+# Interactive. See https://cmu-delphi.github.io/epidatr/articles/epidatr.html#api-keys for details.
+#save_api_key()
+# Import
+library(epidatr)
+# Fetch data
+res <- pub_covidcast('fb-survey', 'smoothed_cli', 'county', 'day', geo_values = '06001',
+                     time_values = c(20200401, 20200405:20200414))
+cat(res)
 ````
 
 ### Python
 
-
 Optionally install the [package from PyPI](https://pypi.org/project/delphi-epidata/) using pip(env):
 ````bash
 pip install delphi-epidata
@@ -60,10 +60,24 @@ res = Epidata.covidcast('fb-survey', 'smoothed_cli', 'day', 'county', [20200401,
 print(res['result'], res['message'], len(res['epidata']))
 ````
 
-### R
+### JavaScript (in a web browser)
 
+The minimalist JavaScript client does not currently support API keys. If you need API key support in JavaScript, contact delphi-support+privacy@andrew.cmu.edu.
 
-````R
+````html
+<!-- Imports -->
+<script src="delphi_epidata.js"></script>
+<!-- Fetch data -->
+<script>
+  EpidataAsync.covidcast('fb-survey', 'smoothed_cli', 'day', 'county', [20200401, EpidataAsync.range(20200405, 20200414)], '06001').then((res) => {
+    console.log(res.result, res.message, res.epidata != null ? res.epidata.length : 0);
+  });
+</script>
+````
+
+### R (legacy)
+
+```R
 # [Optional] configure your API key, if desired
 #option('epidata.auth', <your API key>)
 # Import
@@ -71,4 +85,4 @@ source('delphi_epidata.R')
 # Fetch data
 res <- Epidata$covidcast('fb-survey', 'smoothed_cli', 'day', 'county', list(20200401, Epidata$range(20200405, 20200414)), '06001')
 cat(paste(res$result, res$message, length(res$epidata), "\n"))
-````
+```
diff --git a/requirements.api.txt b/requirements.api.txt
index cb5195367..f9a46b113 100644
--- a/requirements.api.txt
+++ b/requirements.api.txt
@@ -1,4 +1,4 @@
-delphi_utils==0.3.15
+delphi_utils
 epiweeks==2.1.2
 Flask==2.2.5
 Flask-Limiter==3.3.0
diff --git a/requirements.dev.txt b/requirements.dev.txt
index 840d885f5..ff7da9923 100644
--- a/requirements.dev.txt
+++ b/requirements.dev.txt
@@ -2,7 +2,7 @@ aiohttp==3.9.4
 black>=20.8b1
 bump2version==1.0.1
 covidcast==0.1.5
-delphi_utils==0.3.15
+delphi_utils
 docker==6.0.1
 dropbox==11.36.0
 freezegun==1.2.2
diff --git a/scripts/signal_spreadsheet_updater.R b/scripts/signal_spreadsheet_updater.R
new file mode 100644
index 000000000..a109ccf16
--- /dev/null
+++ b/scripts/signal_spreadsheet_updater.R
@@ -0,0 +1,1172 @@
+# This is a helper script for updating the signal spreadsheet "Signals" tab
+# (https://docs.google.com/spreadsheets/d/1zb7ItJzY5oq1n-2xtvnPBiJu2L3AqmCKubrLkKJZVHs/edit#gid=329338228)
+# semi-programmatically.
+#
+# To run this, you need to have the Signals tab and the Sources tab saved
+# locally as CSVs. The script loads and modifies the data from the Signals tab.
+#
+# To update a given field, we define a map between data source names and a set
+# of values, use the data source column to index into the map, and save the
+# result to the spreadsheet. There is some additional logic, depending on the
+# field to be updated, to handle certain signals and cases (active/inactive
+# signals) separately.
+#
+# The updated spreadsheet is saved to disk as a CSV. Any updated columns must be
+# manually pasted into the online spreadsheet. This script checks that the
+# original sort order is the same as that of the updated spreadsheet.
+
+
+# Load packages
+suppressPackageStartupMessages({
+  library(epidatr) # Access Delphi API
+  library(dplyr) # Data handling
+  library(readr) # Import csv sheet
+  library(pipeR) # special pipe %>>%
+  library(glue) # f-string formatting
+})
+
+options(warn = 1)
+
+
+# TODO all info for youtube-survey. Information is hard to find. Filled out some fields based on https://github.com/cmu-delphi/covid-19/tree/main/youtube
+
+# COVIDcast metadata
+# Metadata documentation: https://cmu-delphi.github.io/delphi-epidata/api/covidcast_meta.html
+
+metadata <- pub_covidcast_meta()
+# Convert `last_update` into a datetime.
+# metadata$last_update <- as.POSIXct(metadata$last_update, origin = "1970-01-01")
+## If don't want the hours, etc, truncate with `as.Date`
+metadata$last_update <- as.Date(as.POSIXct(metadata$last_update, origin = "1970-01-01"))
+
+
+# Patch NCHS-mortality max_time data into metadata, and min_time
+source_name = "nchs-mortality"
+nchs_row_nums = which(metadata$data_source == source_name)
+
+metadata$min_time <- as.character(metadata$min_time)
+metadata$max_time <- as.character(metadata$max_time)
+metadata$max_issue <- as.character(metadata$max_issue)
+
+for (index in nchs_row_nums) {
+  row = metadata[index, ]
+    epidata <- pub_covidcast(   # epidata is corresponding data set, ie) all counties and dates between min max
+    source = source_name,
+    signals = row$signal,
+    time_type = as.character(row$time_type),
+    geo_type = as.character(row$geo_type),
+    geo_values = "*",
+    time_values = epirange(199001, 203001)
+  )
+  column = epidata$time_value
+  metadata[index, "min_time"] = epidatr:::date_to_epiweek(min(column)) %>% 
+    as.character() %>>%
+    { paste0(substr(., 1, 4), "-", substr(., 5, 6)) }
+  metadata[index, "max_time"] = epidatr:::date_to_epiweek(max(column)) %>% 
+    as.character() %>>%
+    { paste0(substr(., 1, 4), "-", substr(., 5, 6)) }
+  metadata[index, "max_issue"] = epidatr:::date_to_epiweek(max(epidata$issue)) %>% 
+    as.character() %>>%
+    { paste0(substr(., 1, 4), "-", substr(., 5, 6)) }
+}
+
+# keep only unique rows [dplyr::distinct]
+# only keeps listed columns....does select as well
+metadata_subset <- distinct(
+  metadata,
+  data_source,
+  signal,
+  min_time,
+  max_time
+)
+
+# check that geo_types for a given signal have the same start/end dates
+# counts number of rows with unique data_source-signal combination [base::anyDuplicated] for duplicates
+# warning [stop] will indicate manual correction is required
+
+if (anyDuplicated(select(metadata_subset, data_source, signal)) != 0){
+  warning(
+    "discovered geos for the same signal with different metadata values. ",
+    "Currently, we are keeping the earliest min_time and the lastest max_time. ",
+    "We also create a note column where dates differ by geo and list all geos ",
+    "and all scope start/end dates"
+  )
+}
+
+metadata_subset <- group_by(
+  metadata,
+  data_source,
+  signal
+) %>%
+  summarize(
+    min_time_notes = paste0("Start dates vary by geo: ", paste(geo_type, min_time, collapse = ", ")),
+    n_unique_min_time = length(unique(min_time)),
+    min_time = min(min_time),
+    max_time_notes = paste0("End dates vary by geo: ", paste(geo_type, max_time, collapse = ", ")),
+    n_unique_max_time = length(unique(max_time)),
+    max_time = max(max_time),
+    .groups = "keep"
+  ) %>%
+  ungroup()
+
+metadata_subset[metadata_subset$n_unique_min_time <= 1, "min_time_notes"] <- NA_character_
+metadata_subset[metadata_subset$n_unique_max_time <= 1, "max_time_notes"] <- NA_character_
+
+
+# read in SIGNALS table as csv from https://docs.google.com/spreadsheets/d/1zb7ItJzY5oq1n-2xtvnPBiJu2L3AqmCKubrLkKJZVHs/edit#gid=329338228
+#   source subdivision is in "source_sub" col
+#   data signal is in "signal" col
+#   start date is in "temp_start" col
+#   end date is in "temp_end" col
+signal_sheet <- suppressMessages(read_csv("delphi-eng-covidcast-data-sources-signals_Signals.csv")) %>%
+  # Drop empty rows
+  filter(!is.na(`Source Subdivision`) & !is.na(Signal)) # (Signa) makes extra certain data is missing
+
+# Fields we want to add.
+new_fields <- c(
+  "Geographic Scope",
+  "Delphi-Aggregated Geography",
+  "Temporal Scope Start",
+  "Temporal Scope End",
+  "Reporting Cadence",
+  "Typical Reporting Lag", #originally Reporting Lag
+  "Typical Revision Cadence", #originally Revision Cadence
+  "Demographic Scope",
+  "Demographic Breakdowns",
+  "Severity Pyramid Rungs",
+  "Data Censoring",
+  "Missingness",
+  "Who may access this signal?",
+  "Who may be told about this signal?",
+  "Use Restrictions",
+  "Link to DUA"
+)
+names(new_fields) <- new_fields
+
+# Which ones have missing values and need to be filled in?
+new_fields_with_missings <- lapply(new_fields, function(col) {
+  any(is.na(signal_sheet[, col]))
+})
+new_fields_with_missings <- names(new_fields_with_missings[unlist(new_fields_with_missings)])
+
+message(
+  paste(new_fields_with_missings, collapse = ", "),
+  " columns contain missing values and need to be filled in"
+)
+
+
+# read in SOURCES table as csv from https://docs.google.com/spreadsheets/d/1zb7ItJzY5oq1n-2xtvnPBiJu2L3AqmCKubrLkKJZVHs/edit#gid=0
+# shows how real data source names map to "source subdivisions"
+#   data source is in "DB Source" col
+#   source subdivision is in "source_sub" col
+source_map <- suppressMessages(read_csv("delphi-eng-covidcast-data-sources-signals_Sources.csv")) %>%
+  # Drop empty rows
+  filter(!is.na(`Source Subdivision`) & !is.na(`DB Source`)) %>%
+  rename(data_source =`DB Source`) %>%
+  select(data_source, "Source Subdivision")
+
+# left join metadata_subset with source_map
+source2 <- left_join(
+  signal_sheet,
+  source_map,
+  by = "Source Subdivision"
+)
+
+# left join signal_sheet with source2
+# result: table with source subdivision, signal, scope start, scope end, min date, max date
+source3 <- left_join(
+  source2,
+  metadata_subset,
+  by = c("Signal" = "signal", "data_source")
+)
+
+# Assume new values for Temporal Scope Start/End are correct, but use previous
+# (manual) values as backup.
+source3$min_time <- coalesce(source3$min_time, source3$`Temporal Scope Start`)
+source3$max_time <- coalesce(source3$max_time, source3$`Temporal Scope End`)
+
+# Select relevant columns
+# first reformat max_time col to character for compatibility
+# also convert min_time col to character (easier to move times over to google spreadsheet without corrupting)
+# *only in dplyr can you use col names without quotations, as.character is base function
+# *min_time, we can just use the earliest date available and not specify each geo's different dates
+source4 <- mutate(
+  source3,
+  `Temporal Scope Start Note` = min_time_notes,
+  `Temporal Scope End Note` = max_time_notes,
+  max_time = as.character(max_time),
+  min_time = as.character(min_time)
+)
+
+
+# overwrite scope start with min_time [dplyr::mutate]. Set all scope end values
+# to "Ongoing" as default. Make copies of manually-filled in columns so we can
+# compare our programmatic results.
+source5 <- source4 %>%
+  mutate(
+    `Temporal Scope Start manual` = `Temporal Scope Start`,
+    `Temporal Scope End manual` = `Temporal Scope End`,
+    `Temporal Scope Start` = min_time,
+    `Temporal Scope End` = max_time
+  )
+
+
+# Inactive data_sources list
+inactive_sources <- c(
+  "jhu-csse", "dsew-cpr", "fb-survey", "covid-act-now", "ght", "google-survey",
+  "indicator-combination", "safegraph", "usa-facts", "youtube-survey"
+)
+
+# Inactive signals list, where some signals for a given data source are active
+# and some are inactive.
+inactive_signals <- tibble::tribble(
+  ~data_source, ~signal,
+
+  "quidel", "raw_pct_negative",
+  "quidel", "smoothed_pct_negative",
+  "quidel", "raw_tests_per_device",
+  "quidel", "smoothed_tests_per_device",
+
+  "hospital-admissions", "smoothed_covid19",
+  "hospital-admissions", "smoothed_adj_covid19",
+ 
+  "google-symptoms", "anosmia_raw_search",
+  "google-symptoms", "anosmia_smoothed_search",
+  "google-symptoms", "ageusia_raw_search",
+  "google-symptoms", "ageusia_smoothed_search",
+  "google-symptoms", "sum_anosmia_ageusia_raw_search",
+  "google-symptoms", "sum_anosmia_ageusia_smoothed_search"
+)
+inactive_signals$active <- FALSE
+
+source55 <- left_join(
+  source5, inactive_signals,
+  by = c("Signal" = "signal", "data_source")
+) %>%
+  mutate(active = coalesce(active, !(data_source %in% inactive_sources)))
+
+if (filter(source55, data_source %in% inactive_sources) %>% pull(active) %>% any()) {
+  stop("Some data sources that should be fully inactive list active signals",
+       "Data handling above probably has a bug.")
+}
+
+# overwrite scope end with max_time where signal/data source is active [dplyr::mutate]
+active_mask <- source55$active
+source55$`Temporal Scope End`[active_mask] <- "Ongoing"
+
+
+# delete helper columns (min and max_time, active) [dplyr::select]
+source6 <- select(
+  source55,
+  -min_time,
+  -max_time,
+  -active
+)
+
+# # Check that our programmatically-derived and manually-filled temporal scope columns match
+# compare_start_dates <- filter(source6, !is.na(`Temporal Scope Start manual`)) %>%
+#   mutate(compare = `Temporal Scope Start manual` == `Temporal Scope Start`)
+# if (!all(compare_start_dates$compare)) {
+#   warning("Not all start dates match between programmatic and manual versions. ",
+#           "See rows ", paste(which(!compare_start_dates$compare), collapse = ", "))
+# } 
+# # Examine the ones that don't match
+# # These differences are acceptable
+# source6[which(!compare_start_dates$compare), c("data_source", "Signal", "Temporal Scope Start manual", "Temporal Scope Start", "Temporal Scope Start Note")]
+
+# compare_end_dates <- filter(source6, !is.na(`Temporal Scope End manual`)) %>%
+#   mutate(compare = `Temporal Scope End manual` == `Temporal Scope End`)
+# if (!all(compare_end_dates$compare)) {
+#   warning("Not all end dates match between programmatic and manual versions. ",
+#           "See rows ", paste(which(!compare_end_dates$compare), collapse = ", "))
+# }
+# # Examine the ones that don't match
+# # These differences are acceptable
+# source6[which(!compare_end_dates$compare), c("data_source", "Signal", "Temporal Scope End manual", "Temporal Scope End", "Temporal Scope End Note")]
+
+# our new df MUST have the same row order as the signal spreadsheet
+sort_order_is_the_same <- identical(
+  select(source6, `Source Subdivision`, Signal),
+  select(signal_sheet, `Source Subdivision`, Signal)
+)
+
+if (!sort_order_is_the_same) {
+  stop("new signal fields are sorted differently than signal spreadsheet. ",
+       "Columns cannot be pasted in as-is")
+}
+
+source_updated <- select(
+  source6,
+  -`Temporal Scope Start manual`,
+  -`Temporal Scope End manual`,
+  -min_time_notes,
+  -max_time_notes,
+  -n_unique_min_time,
+  -n_unique_max_time
+)
+
+
+
+col <- "Geographic Scope"
+# List all the highest-level locations for which the signal is available.
+# Each location should b fully disambiguated as in the examples below.
+# Muliple locations, if any, should be separated by a semicolon.
+# E.g.:
+#    If it's available for all (or almost all) of the US (whether by county, by state or only nationally), enter "USA".
+#    If it's available only for the state of PA (whether by county, or only for the whole state), enter, "Pennsylvania, USA".
+#    If it's available only for the states of PA and OH, enter, "Pennsylvania, USA; Ohio, USA"
+#    If it's available only for Allegheny County, PA, enter "Allegheny, Pennsylvania, USA"
+#    etc.
+geo_scope <- c(
+  "chng" = "USA",
+  "covid-act-now" = "USA",
+  "doctor-visits" = "USA",
+  "dsew-cpr" = "USA",
+  "fb-survey" = "USA",
+  "ght" = "USA",
+  "google-survey" = "USA",
+  "google-symptoms" = "USA",
+  "hhs" = "USA",
+  "hospital-admissions" = "USA",
+  "indicator-combination" = "USA",
+  "jhu-csse" = "USA",
+  "nchs-mortality" = "USA",
+  "quidel" = "USA",
+  "safegraph" = "USA",
+  "usa-facts" = "USA",
+  "youtube-survey" = "USA"
+)
+source_updated[, col] <- geo_scope[source_updated$data_source]
+
+
+
+
+col <- "Available Geography"
+# List all available geo-levels, e.g:  county,state,nation
+
+# # Tool: Create lists of geos for each data source-signal combo based on what is
+# # reported in metadata (does not include quidel).
+# metadata_factorgeo <- metadata
+# metadata_factorgeo$geo_type <- factor(metadata_factorgeo$geo_type, levels = c("county", "hrr", "msa", "dma", "state", "hhs", "nation"))
+# auto_geo_list_by_signal <- arrange(
+#   metadata_factorgeo,
+#   geo_type
+# ) %>% 
+#   group_by(
+#     data_source,
+#     signal
+#   ) %>%
+#   summarize(
+#     geos_list = paste(geo_type, collapse = ", "),
+#     .groups = "keep"
+#   ) %>%
+#   ungroup()
+
+# # Tool: Are there any data sources where geos_list is different for different signal?
+# different_geos_by_signal <- count(auto_geo_list_by_signal, data_source, geos_list, name = "n_signals")
+# # different_geos_by_signal
+# # which(duplicated(select(different_geos_by_signal, data_source)))
+
+# # Keep most common geos_list for each data source.
+# most_common_geos_list <- group_by(different_geos_by_signal, data_source) %>% 
+#   slice_max(n_signals, with_ties = FALSE)
+# # most_common_geos_list
+# leftover_datasource_geos <- anti_join(different_geos_by_signal, most_common_geos_list)
+# # leftover_datasource_geos
+# leftover_signal_geos <- semi_join(auto_geo_list_by_signal, leftover_datasource_geos)
+# # leftover_signal_geos
+
+# These values are applied first. They are the default (most common) geos for each data source.
+avail_geos <- c(
+  "chng" = glue("county,hrr,msa,state,hhs,nation"),
+  "covid-act-now" = glue("county,hrr,msa,state,hhs,nation"),
+  "doctor-visits" = glue("county,hrr,msa,state,hhs,nation"),
+  "dsew-cpr" = glue("county,msa,state,hhs,nation"),
+  "fb-survey" = glue("county,hrr,msa,state,nation"),
+  "ght" = glue("hrr,msa,dma,state"),
+  "google-survey" = glue("county,hrr,msa,state"),
+  "google-symptoms" = glue("county,hrr,msa,state,hhs,nation"),
+  "hhs" = glue("state,hhs,nation"),
+  "hospital-admissions" = glue("county,hrr,msa,state,hhs,nation"),
+  "indicator-combination" = glue("county,hrr,msa,state,hhs,nation"),
+  "jhu-csse" = glue("county,hrr,msa,state,hhs,nation"),
+  "nchs-mortality" = glue("state,nation"),
+  # Quidel non-flu signals
+  "quidel" = glue("county,hrr,msa,state,hhs,nation"),
+  "safegraph" = glue("county,hrr,msa,state,hhs,nation"),
+  "usa-facts" = glue("county,hrr,msa,state,hhs,nation"),
+  "youtube-survey" = "state"
+)
+
+# These are signal-specific geo lists. These are less common and are applied as a patch.
+dsew_geos <- glue("state,hhs,nation")
+fb_geos1 <- glue("county,state,nation")
+fb_geos2 <- glue("county,msa,state,nation")
+hosp_geos <- glue("county,hrr,msa,state")
+combo_geos <- glue("county,msa,state")
+quidel_geos <- glue("msa,state")
+leftover_signal_geos_manual <- tibble::tribble(
+  ~data_source, ~signal, ~geos_list,
+  "chng", "7dav_inpatient_covid", "state",
+  "chng", "7dav_outpatient_covid", "state",
+  
+  "dsew-cpr", "booster_doses_admin_7dav", dsew_geos,
+  "dsew-cpr", "doses_admin_7dav", dsew_geos,
+  "dsew-cpr", "people_booster_doses", dsew_geos,
+  
+  "fb-survey", "smoothed_vaccine_barrier_appointment_location_tried", fb_geos1,
+  "fb-survey", "smoothed_vaccine_barrier_other_tried", fb_geos1,
+  "fb-survey", "smoothed_wvaccine_barrier_appointment_location_tried", fb_geos1,
+  "fb-survey", "smoothed_wvaccine_barrier_other_tried", fb_geos1,
+  
+  "fb-survey", "smoothed_vaccine_barrier_appointment_time_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_childcare_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_document_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_eligible_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_language_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_no_appointments_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_none_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_technical_difficulties_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_technology_access_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_time_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_travel_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_type_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_appointment_time_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_childcare_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_document_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_eligible_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_language_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_no_appointments_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_none_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_technical_difficulties_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_technology_access_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_time_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_travel_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_type_tried", fb_geos2,
+  
+  "hospital-admissions", "smoothed_adj_covid19", hosp_geos,
+  "hospital-admissions", "smoothed_covid19", hosp_geos,
+  
+  "indicator-combination", "nmf_day_doc_fbc_fbs_ght", combo_geos,
+  "indicator-combination", "nmf_day_doc_fbs_ght", combo_geos,
+  
+  # Quidel flu signals
+  "quidel", "raw_pct_negative", quidel_geos,
+  "quidel", "smoothed_pct_negative", quidel_geos,
+  "quidel", "raw_tests_per_device", quidel_geos,
+  "quidel", "smoothed_tests_per_device", quidel_geos
+)
+
+source_updated[, col] <- coalesce(avail_geos[source_updated$data_source], source_updated[[col]])
+
+source_updated <- left_join(
+  source_updated, leftover_signal_geos_manual,
+  by = c("Signal" = "signal", "data_source")
+) %>%
+  mutate(`Available Geography` = coalesce(geos_list, `Available Geography`)) %>%
+  select(-geos_list)
+
+
+col <- "Delphi-Aggregated Geography"
+# List available geo-levels that were created by Delphi (as opposed to being
+# ingested directly from the data source), e.g. if available at the county,
+# state, and nation levels but state and nation were aggregated by us from
+# provided county data:  state,nation
+
+# These values are applied first. They are the default (most common) geos for each data source.
+avail_geos <- c(
+  "chng" = glue("hrr,msa,state,hhs,nation"),
+  "covid-act-now" = glue("hrr,msa,state,hhs,nation"),
+  "doctor-visits" = glue("hrr,msa,state,hhs,nation"),
+  "dsew-cpr" = glue("nation"),
+  "fb-survey" = glue("county,hrr,msa,state,nation"),
+  "ght" = glue("hrr,msa"),
+  "google-survey" = glue("county,hrr,msa,state"),
+  "google-symptoms" = glue("hrr,msa,hhs,nation"),
+  "hhs" = glue("hhs,nation"),
+  "hospital-admissions" = glue("county,hrr,msa,state,hhs,nation"),
+  "indicator-combination" = glue("county,hrr,msa,state,hhs,nation"),
+  "jhu-csse" = glue("hrr,msa,state,hhs,nation"),
+  "nchs-mortality" = NA_character_,
+  # Quidel non-flu signals
+  "quidel" = glue("county,hrr,msa,state,hhs,nation"),
+  "safegraph" = glue("county,hrr,msa,state,hhs,nation"),
+  "usa-facts" = glue("hrr,msa,state,hhs,nation"),
+  "youtube-survey" = "state"
+)
+
+# These are signal-specific geo lists. These are less common and are applied as a patch.
+dsew_geos <- glue("nation")
+fb_geos1 <- glue("county,state,nation")
+fb_geos2 <- glue("county,msa,state,nation")
+hosp_geos <- glue("county,hrr,msa,state")
+combo_geos <- glue("county,msa,state")
+quidel_geos <- glue("msa,state")
+leftover_signal_geos_manual <- tibble::tribble(
+  ~data_source, ~signal, ~geos_list,
+  "chng", "7dav_inpatient_covid", "state",
+  "chng", "7dav_outpatient_covid", "state",
+  
+  "dsew-cpr", "booster_doses_admin_7dav", dsew_geos,
+  "dsew-cpr", "doses_admin_7dav", dsew_geos,
+  "dsew-cpr", "people_booster_doses", dsew_geos,
+  
+  "fb-survey", "smoothed_vaccine_barrier_appointment_location_tried", fb_geos1,
+  "fb-survey", "smoothed_vaccine_barrier_other_tried", fb_geos1,
+  "fb-survey", "smoothed_wvaccine_barrier_appointment_location_tried", fb_geos1,
+  "fb-survey", "smoothed_wvaccine_barrier_other_tried", fb_geos1,
+  
+  "fb-survey", "smoothed_vaccine_barrier_appointment_time_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_childcare_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_document_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_eligible_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_language_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_no_appointments_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_none_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_technical_difficulties_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_technology_access_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_time_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_travel_tried", fb_geos2,
+  "fb-survey", "smoothed_vaccine_barrier_type_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_appointment_time_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_childcare_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_document_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_eligible_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_language_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_no_appointments_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_none_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_technical_difficulties_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_technology_access_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_time_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_travel_tried", fb_geos2,
+  "fb-survey", "smoothed_wvaccine_barrier_type_tried", fb_geos2,
+  
+  "hospital-admissions", "smoothed_adj_covid19", hosp_geos,
+  "hospital-admissions", "smoothed_covid19", hosp_geos,
+  
+  "indicator-combination", "nmf_day_doc_fbc_fbs_ght", combo_geos,
+  "indicator-combination", "nmf_day_doc_fbs_ght", combo_geos,
+  
+  # Quidel flu signals
+  "quidel", "raw_pct_negative", quidel_geos,
+  "quidel", "smoothed_pct_negative", quidel_geos,
+  "quidel", "raw_tests_per_device", quidel_geos,
+  "quidel", "smoothed_tests_per_device", quidel_geos
+)
+
+source_updated[, col] <- coalesce(avail_geos[source_updated$data_source], source_updated[[col]])
+
+source_updated <- left_join(
+  source_updated, leftover_signal_geos_manual,
+  by = c("Signal" = "signal", "data_source")
+) %>%
+  mutate(`Delphi-Aggregated Geography` = geos_list) %>%
+  select(-geos_list)
+
+
+
+# Temporal Scope Start
+# Above. YYYY-MM-DD, with epiweeks as YYYY-WW. Formatted as a string
+
+# Temporal Scope End
+# Above. YYYY-MM-DD, with epiweeks as YYYY-WW, or "Ongoing" if still ongoing.
+# Formatted as a string
+
+
+col <- "Reporting Cadence"
+# E.g. daily, weekly, etc. Might not be the same as Temporal Granularity
+cadence_map <- c(
+  "chng" = "daily",
+  "covid-act-now" = "daily",
+  "doctor-visits" = "daily",
+  "dsew-cpr" = "daily",
+  "fb-survey" = "daily",
+  "ght" = "daily",
+  "google-survey" = "daily",
+  "google-symptoms" = "daily",
+  "hhs" = "weekly",
+  "hospital-admissions" = "daily",
+  "indicator-combination" = "daily",
+  "jhu-csse" = "daily",
+  "nchs-mortality" = "weekly",
+  "quidel" = "daily",
+  "safegraph" = "weekly",
+  "usa-facts" = "weekly",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- cadence_map[source_updated$data_source]
+
+# # Tool: Investigate reporting lag and revision cadence
+# source <- "indicator-combination-nmf"
+# signal <- "nmf_day_doc_fbc_fbs_ght"
+# # Not available for all indicators. Try nation. Avoid smaller geos because
+# # processing later will take a while.
+# geo_type <- "state"
+# 
+# # Consider a range of issues. About 2 weeks is probably fine. Not all indicators
+# # are available in this time range, so you may need to make another range of
+# # dates that is years or months different.
+# about_2weeks_issues <- c(
+#   "2021-02-01",
+#   "2021-02-02",
+#   "2021-02-04",
+#   "2021-02-05",
+#   "2021-02-06",
+#   "2021-02-07",
+#   "2021-02-08",
+#   "2021-02-09",
+#   "2021-02-10",
+#   "2021-02-11",
+#   "2021-02-12",
+#   "2021-02-13",
+#   "2021-02-14",
+#   "2021-02-15",
+#   "2021-02-16"
+# )
+# 
+# 
+# epidata <- pub_covidcast(
+#   source,
+#   signal,
+#   geo_type = geo_type,
+#   geo_values = "*",
+#   time_type = "day",
+#   issues = about_2weeks_issues
+# )
+# 
+# 
+# # Make sure data is looking reasonable
+# # Number of reference dates reported in each issue
+# count(epidata, issue)
+# 
+# # Number of locations reported for each issue and reference date
+# count(epidata, issue, time_value)
+# 
+# 
+# ## Revision cadence
+# # For each location and reference date, are all reported values the same across
+# # all lags we're checking?
+# revision_comparison <- epidata %>%
+#   group_by(time_value, geo_value) %>%
+#   summarize(
+#     no_backfill = case_when(
+#       length(unique(value)) == 1 ~ "TRUE",
+#       # If only two different values, are they approximately the same?
+#       length(unique(value)) == 2 ~ all.equal(unique(value)[1], unique(value)[2]) %>% as.character(),
+#       # If three different values, list them
+#       length(unique(value)) > 2 ~ paste(unique(value), collapse = ", "),
+#     )
+#   )
+# # Are all reference dates without any lag?
+# all(revision_comparison$no_backfill == "TRUE")
+# #all(revision_comparison[revision_comparison$no_backfill != "TRUE", ])
+# revision_comparison[revision_comparison$no_backfill != "TRUE", ]
+# View(revision_comparison)
+
+
+## Reporting lag
+# Find how lagged the newest reported value is for each issue.
+epidata_slice <- epidata %>% group_by(issue) %>% slice_min(lag)
+# Find the most common min lag. We expect a relatively narrow range of lags. At
+# most, a data source should be updated weekly such that it has a range of lags
+# of 7 days (e.g. 5-12 days). For data updated daily, we expect a range of lags
+# of only a few days (e.g. 2-4 days or even 2-3 days).
+table(epidata_slice$lag)
+
+
+col <- "Typical Reporting Lag"
+# The number of days as an unstructured field, e.g. "3-5 days", from the last
+# day of a reported period until the first reported value for that period is
+# usually available in Epidata. E.g. if reporting U.S. epiweeks
+# (Sunday through Saturday), and the first report is usually available in
+# Epidata on the following Friday, enter 6.
+#
+# By "usually available" we mean when it's "supposed to be" available based on
+# our current understanding of the data provider's operations and Delphi's
+# ingestion pipeline. That would be the date on which we think of the data
+# as showing up "on time", and relative to which we will track unusual
+# delays.
+#
+# values are from production params files, e.g.
+# https://github.com/cmu-delphi/covidcast-indicators/blob/d36352b/ansible/templates/changehc-params-prod.json.j2#L42-L43,
+# and sirCAL params
+# https://github.com/cmu-delphi/covidcast-indicators/blob/main/ansible/templates/sir_complainsalot-params-prod.json.j2#L16
+
+# Make a map between each data source (or source subdivision) and value. The
+# value can be numeric, string, etc.
+reporting_lag <- c(
+  "chng" = "4-5 days",
+  "covid-act-now" = "2-9 days",
+  "doctor-visits" = "3-6 days",
+  "dsew-cpr" = "3-9 days",
+  "fb-survey" = "1 day",
+  "ght" = "4-5 days",
+  "google-survey" = "1-2 days",
+  "google-symptoms" = "4-7 days",
+  "hhs" = "5-11 days",
+  "hospital-admissions" = "3-4 days",
+  "indicator-combination" = "1-3 days",
+  "jhu-csse" = "1 day",
+  "nchs-mortality" = "11-17 days",
+  "quidel" = "5-6 days",
+  "safegraph" = "3-11 days",
+  "usa-facts" = "2-8 days",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+
+# Index (using `[]`) into the map using the data_source (or source division)
+# column and save to the relevant field.
+# Using the data_source to index into the map puts the output into the same
+# order as the dataframe.
+source_updated[, col] <- reporting_lag[source_updated$data_source]
+
+col <- "Typical Revision Cadence"
+# How frequently are revised values (AKA backfill) usually made available as
+# an unstructured field, e.g. "Weekly (usually Fridays)", "daily", etc. If
+# there are no reporting revisions, enter "None".
+revision_cadence <- c(
+  "chng" = "Daily. The source experiences heavy backfill with data delayed for a couple of weeks. We expect estimates available for the most recent 4-6 days to change substantially in later data revisions (having a median delta of 10% or more). Estimates for dates more than 45 days in the past are expected to remain fairly static (having a median delta of 1% or less), as most major revisions have already occurred.",
+  "covid-act-now" = "Daily. Most recent test positivity rates do not change substantially (having a median delta of close to 0). However, most recent total tests performed are expected to increase in later data revisions (having a median increase of 7%). Values more than 5 days in the past are expected to remain fairly static (with total tests performed having a median increase of 1% of less), as most major revisions have already occurred.",
+  "doctor-visits" = "Daily. The source experiences heavy backfill with data delayed for a couple of weeks. We expect estimates available for the most recent 5-7 days to change substantially in later data revisions (having a median delta of 10% or more). Estimates for dates more than 50 days in the past are expected to remain fairly static (having a median delta of 1% or less), as most major revisions have already occurred.",
+  "dsew-cpr" = "Daily. This data source is susceptible to large corrections that can create strange data effects such as negative counts and sudden changes of 1M+ counts from one day to the next.",
+  "fb-survey" = "Daily, for 5 consecutive issues for each report date",
+  "ght" = "None",
+  "google-survey" = "Daily, for 3 consecutive issues for each report date",
+  "google-symptoms" = "None",
+  "hhs" = "Monthly. Backfill is relatively uncommon in this dataset (80% of dates from November 1, 2020 onward are never touched after their first issue) and most such updates occur one to two weeks after information about a date is first published. In rare instances, a value may be updated 10 weeks or more after it is first published.",
+  "hospital-admissions" = "Daily. The source experiences heavy backfill with data delayed for a couple of weeks. We expect estimates available for the most recent 7-13 days to change substantially in later data revisions (having a median delta of 10% or more). Estimates for dates more than 57 days in the past are expected to remain fairly static (having a median delta of 1% or less), as most major revisions have already occurred.",
+  "indicator-combination" = "Daily",
+  "jhu-csse" = "None. The raw data reports cumulative cases and deaths, which
+     Delphi diffs to compute incidence. Raw cumulative figures are sometimes
+     corrected by adjusting the reported value for a single day, but revisions
+     do not affect past report dates.",
+  "nchs-mortality" = "Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7)",
+  "quidel" = "Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.",
+  "safegraph" = "None",
+  "usa-facts" = "None. The raw data reports cumulative cases and deaths, which Delphi diffs to compute incidence. Raw cumulative figures are sometimes corrected by adjusting the reported value for a single day, but revisions do not affect past report dates.",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- revision_cadence[source_updated$data_source]
+
+col <- "Demographic Scope"
+# The demographic group covered by the report.
+# E.g. "all", "Pediatric", "Adult", "Women", "adult facebook
+# users", "MSM", "Google search users", "VA Health system
+# members", "smartphone users", …
+demo_scope <- c(
+  "chng" = "Nationwide Change Healthcare network",
+  "covid-act-now" = "Hospital patients",
+  "doctor-visits" = "Nationwide Optum network",
+  "dsew-cpr" = "All",
+  "fb-survey" = "Adult Facebook users",
+  "ght" = "Google search users",
+  "google-survey" = "Google ad publisher website, Google's Opinions Reward app, and similar application users",
+  "google-symptoms" = "Google search users",
+  "hhs" = "All",
+  "hospital-admissions" = "Nationwide Optum network",
+  "indicator-combination" = "This source is a combination of several signals representing different populations, and doesn't correspond to a single demographic group",
+  "jhu-csse" = "All",
+  "nchs-mortality" = "All",
+  "quidel" = "Nationwide Quidel testing equipment network",
+  "safegraph" = "Safegraph panel members who use mobile devices",
+  "usa-facts" = "All",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- demo_scope[source_updated$data_source]
+
+
+col <- "Demographic Breakdowns"
+# What demographic breakdowns are available, e.g. "by age groups 0-17,
+# 18-64, 65+", "by race/ethnicity", "by gender".
+#
+# These might be used in filters, so the values need to be structured. E.g.
+# it could be a list of all available breakdowns, e.g. "gender, race",
+# or "age bands(0-17,18-64,65+)", or "None" if no breakdown. If it's easier,
+# we can separate it into three different columns: "Gender Breakdown"
+# (yes/no), "Race Breakdown" (yes/no), and "Age breakdown" (list of age
+# bands, or "none")
+demo_breakdowns <- c(
+  "chng" = "None",
+  "covid-act-now" = "None",
+  "doctor-visits" = "None",
+  "dsew-cpr" = "None",
+  "fb-survey" = "None. However, contingency tables containing demographic breakdowns of survey data are available for download (https://cmu-delphi.github.io/delphi-epidata/symptom-survey/contingency-tables.html).",
+  "ght" = "None",
+  "google-survey" = "None",
+  "google-symptoms" = "None",
+  "hhs" = "None",
+  "hospital-admissions" = "None",
+  "indicator-combination" = "None",
+  "jhu-csse" = "None",
+  "nchs-mortality" = "None",
+  "quidel" = "age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",
+  "safegraph" = "None",
+  "usa-facts" = "None",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- demo_breakdowns[source_updated$data_source]
+# Quidel covid has age bands, but quidel flu doesn't.
+source_updated[source_updated$`Source Subdivision` == "quidel-flu", col] <- "None"
+
+
+col <- "Severity Pyramid Rungs"
+# One or more rungs to which this signal best relates:
+# https://docs.google.com/presentation/d/1K458kZsncwwjNMOnlkaqHA_0Vm7PEz6g43fYy4GII10/edit#slide=id.g10e023ed748_0_163
+# Added manually to signal spreadsheet.
+
+
+col <- "Data Censoring"
+# Has any of the data been censored (e.g. small counts)? If so how, and how
+# much impact does it have (e.g. approximate fraction of counts affected).
+# This is an unstructured text field.
+data_censoring <- c(
+  "chng" = "Discarded if over a given 7-day period an estimate is computed with 100 or fewer observations",
+  "covid-act-now" = "Discarded if sample size (total tests performed) is 0",
+  "doctor-visits" = "Discarded if over a given 7-day period an estimate is computed with 500 or fewer observations",
+  "dsew-cpr" = "None",
+  "fb-survey" = "Discarded if an estimate is based on fewer than 100 survey responses. For signals reported using a 7-day average (those beginning with 'smoothed_'), this means a geographic area must have at least 100 responses in 7 days to be reported.
+
+  This affects some items more than others. For instance, some survey items are only asked of a subset of survey respondents. It also affects some geographic areas more than others, particularly rural areas with low population densities. When doing analysis of county-level data, one should be aware that missing counties are typically more rural and less populous than those present in the data, which may introduce bias into the analysis.",
+  "ght" = "Reported as 0 query when search volume is below a certain threshold, as set by Google. Areas with low query volume hence exhibit jumps and zero-inflation, as small variations in the signal can cause it to be sometimes truncated to 0 and sometimes reported at its actual level",
+  "google-survey" = "Discarded when an estimate is based on fewer than 100 survey responses",
+  "google-symptoms" = "Unavailable when daily volume in a region does not meet quality or privacy thresholds, as set by Google. Google also uses differential privacy, which adds artificial noise to the incoming data",
+  "hhs" = "None",
+  "hospital-admissions" = "Discarded if over a given 7-day period an estimate is computed with 500 or fewer observations",
+  "indicator-combination" = "None. However underlying signals may perform their own censoring",
+  "jhu-csse" = "None",
+  "nchs-mortality" = "Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",
+  "quidel" = "Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",
+  "safegraph" = "None. However, Safegraph uses differential privacy, which adds artificial noise to the incoming data. See https://docs.safegraph.com/docs/social-distancing-metrics for details",
+  "usa-facts" = "None",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+signal_specific_censoring <- tibble::tribble(
+  ~data_source, ~signal, ~note,
+  "dsew-cpr", "covid_naat_pct_positive_7dav", "Discarded when the 7dav NAAT test volume provided in the same originating
+    spreadsheet, corresponding to a period ~4 days earlier, is 5 or fewer. This removes 10-20% of counties (https://github.com/cmu-delphi/covidcast-indicators/issues/1513)",
+
+)
+source_updated[, col] <- data_censoring[source_updated$data_source]
+
+# Add signal_specific_censoring info
+source_updated <- left_join(
+  source_updated, signal_specific_censoring,
+  by = c("Signal" = "signal", "data_source")
+) %>%
+  mutate(`Data Censoring` = coalesce(note, `Data Censoring`)) %>%
+  select(-note)
+
+
+# # Tool: Investigate state and county coverage
+# suppressPackageStartupMessages({
+#   library(epidatr) # Access Delphi API
+#   library(dplyr) # Data handling
+#   library(ggplot2)
+# })
+#
+#
+# # COVIDcast metadata
+# # Metadata documentation: https://cmu-delphi.github.io/delphi-epidata/api/covidcast_meta.html
+# metadata <- pub_covidcast_meta()
+# # Convert `last_update` into a datetime.
+# # metadata$last_update <- as.POSIXct(metadata$last_update, origin = "1970-01-01")
+# ## If don't want the hours, etc, truncate with `as.Date`
+# metadata$last_update <- as.Date(as.POSIXct(metadata$last_update, origin = "1970-01-01"))
+#
+# one_sig_per_source <- metadata %>%
+#   arrange(desc(signal)) %>%
+#   group_by(data_source) %>%
+#   slice_head(n = 1)
+#
+# state_filtered <- metadata %>%
+#   filter(geo_type == "state") %>%
+#   select(data_source, signal, geo_type, num_locations) %>%
+#   mutate(pct_locations = num_locations / 51 * 100)
+# first_sig_per_source_state <- state_filtered %>%
+#   group_by(data_source) %>%
+#   slice_head(n = 1)
+# first_sig_per_source_state
+#
+# ggplot(
+#   data = state_filtered,
+#   aes(x = data_source, y = pct_locations)
+# ) + geom_boxplot()
+#
+#
+# county_filtered <- metadata %>%
+#   filter(geo_type == "county") %>%
+#   select(data_source, signal, geo_type, num_locations) %>%
+#   mutate(pct_locations = num_locations / 3143 * 100)
+# first_sig_per_source_county <- county_filtered %>%
+#   group_by(data_source) %>%
+#   slice_head(n = 1)
+# first_sig_per_source_county
+#
+# ggplot(
+#   data = county_filtered,
+#   aes(x = data_source, y = pct_locations)
+# ) + geom_boxplot()
+
+
+col <- "Missingness"
+# How much missingness is there, and for what reasons? Is it possible to
+# distinguish a missing value from a true zero? This is an unstructured text
+# field.
+#
+# E.g. in a signal that's available at county level, how many of the US's
+# 3000+ counties are usually reported on? Not filter-related, so can be
+# unstructured text. The problem is that these numbers can change
+# dramatically over time, as has happened e.g. for the Facebook survey. I'm
+# not sure what to do. Maybe just summarize the current state, e.g. "85%
+# counties available in mid 2020, then gradually declined to 8% of counties
+# by April 2024", and leave it at that. We could occasionally update it.
+
+all_counties_terr <- "Data is available for all counties and some territorial county equivalents."
+all_states <- "Data is available for all states."
+all_states_terr <- "Data is available for all states and some territories."
+missingness <- c(
+  "chng" = paste("Data is available for nearly all (99%) of counties.", all_states_terr),
+  "covid-act-now" = paste("Data is available for nearly all (99%) of counties. A few counties, most notably in California, are not covered by this data source", all_states),
+  "doctor-visits" = paste("Data is available for about 80% of counties", all_states_terr),
+  "dsew-cpr" = paste(all_counties_terr, all_states_terr),
+  "fb-survey" = "Geographic coverage varies widely by signal, with anywhere from 0.4% to 25% of counties available and 15% to 100% of states. A handful of signals are available for 40-50% of counties, and all states and some territories. Signals based on questions that were asked to a subset of survey respondents are available for fewer locations. Availability declines over time as survey response rate decreases. A missing value indicates no valid data OR, for test positivity, that the value was censored due to small sample size (<= 5)",
+  "ght" = all_states,
+  "google-survey" = paste("Data is available for about 20% of counties", all_states),
+  "google-symptoms" = NA_character_, # below
+  "hhs" = all_states_terr,
+  "hospital-admissions" = paste("Data is available for about 35% of counties", all_states),
+  "indicator-combination" = paste(all_counties_terr, all_states_terr),
+  "jhu-csse" = paste(all_counties_terr, all_states_terr),
+  "nchs-mortality" = paste(all_states_terr),
+  "quidel" = NA_character_, # below
+  "safegraph" = paste(all_counties_terr, all_states_terr),
+  "usa-facts" = paste(all_counties_terr, all_states),
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube  # below
+)
+source_updated[, col] <- missingness[source_updated$data_source]
+
+google_symptoms_note <- "Signals associated with rarer symptoms (e.g. ageusia) will tend to have fewer locations available, due to upstream privacy censoring. Locations with lower populations will tend to be less available for the same reason"
+
+# Quidel
+smoothed_nonage_groups <- "Data is available for about 50% of counties, and all or nearly all states."
+raw_nonage_groups <- "Data is available for about 7% of counties, half that on weekends. Data is available for about 90% of states, about 70% on weekends"
+smoothed_age_groups <- "Data is available for about 5-15% of counties. Data is available for about 65-95% of states."
+raw_age_groups <- "Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels"
+
+signal_specific_missingness <- tibble::tribble(
+  ~data_source, ~signal, ~note,
+  "indicator-combination", "nmf_day_doc_fbc_fbs_ght", paste("Data is available for about 80% of counties", all_states_terr),
+  "indicator-combination", "nmf_day_doc_fbs_ght", paste("Data is available for about 70% of counties", all_states_terr),
+
+  "safegraph", "bars_visit_num", "Data is available for about 10% of counties. Data is available for about 90% of states",
+  "safegraph", "bars_visit_prop", "Data is available for about 10% of counties. Data is available for about 90% of states",
+  "safegraph", "restaurants_visit_num", paste("Data is available for about 80% of counties", all_states_terr),
+  "safegraph", "restaurants_visit_prop", paste("Data is available for about 80% of counties", all_states_terr),
+
+  "fb-survey", "smoothed_cli", paste("Data is available for about 50% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_ili", paste("Data is available for about 50% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_wcli", paste("Data is available for about 50% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_wili", paste("Data is available for about 50% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_travel_outside_state_5d", paste("Data is available for about 45% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_wtravel_outside_state_5d", paste("Data is available for about 45% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_nohh_cmnty_cli", paste("Data is available for about 40% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_hh_cmnty_cli", paste("Data is available for about 40% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_whh_cmnty_cli", paste("Data is available for about 35% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+  "fb-survey", "smoothed_wnohh_cmnty_cli", paste("Data is available for about 35% of counties.", all_states_terr, "Availability declines over time as survey response rate decreases"),
+
+  "youtube-survey", "raw_cli", "Data is available for about 40% of states",
+  "youtube-survey", "raw_ili", "Data is available for about 40% of states",
+  "youtube-survey", "smoothed_cli", "Data is available for about 80% of states",
+  "youtube-survey", "smoothed_ili", "Data is available for about 80% of states",
+
+  "google-symptoms", "ageusia_raw_search", paste("Data is available for about 3-4% of counties. Data is available for about 85% of states.", google_symptoms_note),
+  "google-symptoms", "ageusia_smoothed_search", paste("Data is available for about 3-4% of counties. Data is available for about 85% of states.", google_symptoms_note),
+  "google-symptoms", "anosmia_raw_search", paste("Data is available for about 3-4% of counties. Data is available for about 85% of states.", google_symptoms_note),
+  "google-symptoms", "anosmia_smoothed_search", paste("Data is available for about 3-4% of counties. Data is available for about 85% of states.", google_symptoms_note),
+  "google-symptoms", "s01_raw_search", paste("Data is available for about 50% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s01_smoothed_search", paste("Data is available for about 50% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s02_raw_search", paste("Data is available for about 65% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s02_smoothed_search", paste("Data is available for about 65% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s03_raw_search", paste("Data is available for about 50% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s03_smoothed_search", paste("Data is available for about 50% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s04_raw_search", paste("Data is available for about 30% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s04_smoothed_search", paste("Data is available for about 30% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s05_raw_search", paste("Data is available for about 3-4% of counties. Data is available for about 90% of states.", google_symptoms_note),
+  "google-symptoms", "s05_smoothed_search", paste("Data is available for about 3-4% of counties. Data is available for about 90% of states.", google_symptoms_note),
+  "google-symptoms", "s06_raw_search", paste("Data is available for about 30% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "s06_smoothed_search", paste("Data is available for about 30% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "scontrol_raw_search", paste("Data is available for about 45% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "scontrol_smoothed_search", paste("Data is available for about 45% of counties.", all_states, google_symptoms_note),
+  "google-symptoms", "sum_anosmia_ageusia_raw_search", paste("Data is available for about 3-4% of counties. Data is available for about 85% of states.", google_symptoms_note),
+  "google-symptoms", "sum_anosmia_ageusia_smoothed_search", paste("Data is available for about 3-4% of counties. Data is available for about 85% of states.", google_symptoms_note),
+  
+  # covid
+  "quidel", "covid_ag_raw_pct_positive", raw_nonage_groups,
+  "quidel", "covid_ag_smoothed_pct_positive", smoothed_nonage_groups,
+  "quidel", "covid_ag_raw_pct_positive_age_0_17", raw_age_groups,
+  "quidel", "covid_ag_smoothed_pct_positive_age_0_17", smoothed_age_groups,
+  "quidel", "covid_ag_raw_pct_positive_age_0_4", raw_age_groups,
+  "quidel", "covid_ag_smoothed_pct_positive_age_0_4", smoothed_age_groups,
+  "quidel", "covid_ag_raw_pct_positive_age_18_49", raw_age_groups,
+  "quidel", "covid_ag_smoothed_pct_positive_age_18_49", smoothed_age_groups,
+  "quidel", "covid_ag_raw_pct_positive_age_5_17", raw_age_groups,
+  "quidel", "covid_ag_smoothed_pct_positive_age_5_17", smoothed_age_groups,
+  "quidel", "covid_ag_raw_pct_positive_age_50_64", raw_age_groups,
+  "quidel", "covid_ag_smoothed_pct_positive_age_50_64", smoothed_age_groups,
+  "quidel", "covid_ag_raw_pct_positive_age_65plus", raw_age_groups,
+  "quidel", "covid_ag_smoothed_pct_positive_age_65plus", smoothed_age_groups,
+  
+  # flu
+  "quidel", "raw_pct_negative", raw_nonage_groups,
+  "quidel", "smoothed_pct_negative", smoothed_nonage_groups,
+  "quidel", "raw_tests_per_device", raw_nonage_groups,
+  "quidel", "smoothed_tests_per_device", smoothed_nonage_groups,
+)
+
+# Add signal-specific missingness
+source_updated <- left_join(
+  source_updated, signal_specific_missingness,
+  by = c("Signal" = "signal", "data_source")
+) %>%
+  mutate(`Missingness` = coalesce(note, `Missingness`)) %>%
+  select(-note)
+
+
+col <- "Who may access this signal?"
+# Who has the right to access this signal?  E.g. "Delphi, CDC" or "Delphi,
+# ACHD, PADOH", or "public". Separate different orgs by comma.
+orgs_allowed_access <- c(
+  "chng" = "public",
+  "covid-act-now" = "public",
+  "doctor-visits" = "public",
+  "dsew-cpr" = "public",
+  "fb-survey" = "public",
+  "ght" = "public",
+  "google-survey" = "public",
+  "google-symptoms" = "public",
+  "hhs" = "public",
+  "hospital-admissions" = "public",
+  "indicator-combination" = "public",
+  "jhu-csse" = "public",
+  "nchs-mortality" = "public",
+  "quidel" = "Delphi",
+  "safegraph" = "public",
+  "usa-facts" = "public",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- orgs_allowed_access[source_updated$data_source]
+
+
+col <- "Who may be told about this signal?"
+orgs_allowed_know <- c(
+  "chng" = "public",
+  "covid-act-now" = "public",
+  "doctor-visits" = "public",
+  "dsew-cpr" = "public",
+  "fb-survey" = "public",
+  "ght" = "public",
+  "google-survey" = "public",
+  "google-symptoms" = "public",
+  "hhs" = "public",
+  "hospital-admissions" = "public",
+  "indicator-combination" = "public",
+  "jhu-csse" = "public",
+  "nchs-mortality" = "public",
+  "quidel" = "public",
+  "safegraph" = "public",
+  "usa-facts" = "public",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- orgs_allowed_know[source_updated$data_source]
+
+
+col <- "License"
+license <- c(
+  "chng" = "CC BY-NC",
+  "covid-act-now" = "CC BY-NC",
+  "doctor-visits" = "CC BY-NC",
+  "dsew-cpr" = "Public Domain US Government (https://www.usa.gov/government-works)",
+  "fb-survey" = "CC BY",
+  "ght" = "Google Terms of Service (https://policies.google.com/terms)",
+  "google-survey" = "CC BY",
+  "google-symptoms" = "Google Terms of Service (https://policies.google.com/terms)",
+  "hhs" = "Public Domain US Government (https://www.usa.gov/government-works)",
+  "hospital-admissions" = "CC BY",
+  "indicator-combination" = "CC BY",
+  "jhu-csse" = "CC BY",
+  "nchs-mortality" = "NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm)",
+  "quidel" = "CC BY",
+  "safegraph" = "CC BY",
+  "usa-facts" = "CC BY",
+  "youtube-survey" = NA_character_ # See https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- license[source_updated$data_source]
+
+
+col <- "Use Restrictions"
+# Any important DUA restrictions on use, publication, sharing, linkage, etc.?
+use_restrictions <- c(
+  "chng" = "See license. DUA uses generic contract terms.", #DUA in confidential Google drive, generic contract terms
+  "covid-act-now" = "See license", #public
+  "doctor-visits" = "See license. DUA uses generic contract terms.", #optum DUA in confidential Google drive, generic contract terms
+  "dsew-cpr" = "See license", #public
+  "fb-survey" = "Aggregationed signals must be based on 100 or more survey responses. Delphi aggregated data has no use restrictions. Raw data users must sign DUA with Delphi, and the proposed research purpose must be consistent with the consent language used in Wave 1, regardless of which survey wave the data they're using comes from. Part- or full-time Facebook employees are not eligible to receive data access.", # @AlexR
+  "ght" = "See license", #public, no Delphi documentation, <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215636/>
+  "google-survey" = NA_character_,
+  "google-symptoms" = "See license",
+  "hhs" = "See license",
+  "hospital-admissions" = "See license. DUA uses generic contract terms.", #optum DUA in confidential Google drive, generic contract terms
+  "indicator-combination" = NA_character_,
+  "jhu-csse" = "See license",
+  "nchs-mortality" = "See license",
+  "quidel" = "Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).", #Quidel DUA in confidential Google drive,
+  "safegraph" = "Delphi is free to publish and otherwise disclose the results of its Research (including but not limited to reports and papers and other activities conducted under the Research), including analyses and/or aggregated reporting of the Data. However, the underlying raw Data may not be published
+without Licensor’s consent.",
+  "usa-facts" = "See license",
+  "youtube-survey" = NA_character_ #https://github.com/cmu-delphi/covid-19/tree/main/youtube
+)
+source_updated[, col] <- use_restrictions[source_updated$data_source]
+
+
+col <- "Link to DUA"
+dua_link <- c(
+  "chng" = "https://drive.google.com/drive/u/1/folders/1GKYSFb6C_8jSHTg-65eVzSOT433oIDrf", #"https://cmu.box.com/s/cto4to822zecr3oyq1kkk9xmzhtq9tl2"
+  "covid-act-now" = NA_character_, #public, maybe contract for other specific project #@Carlyn
+  "doctor-visits" = "https://drive.google.com/drive/u/1/folders/11kvTzVR5Yd3lVszxmPHxFZcAYjIpoLcf", #"https://cmu.box.com/s/l2tz6kmiws6jyty2azwb43poiepz0565"
+  "dsew-cpr" = NA_character_, #public
+  "fb-survey" = NA_character_, #@AlexR public aggregated by Delphi, but raw data requires DUA "https://cmu.box.com/s/qfxplcdrcn9retfzx4zniyugbd9h3bos",
+  "ght" = NA_character_, #public, has an API doesn't require password. No Delphi documentation. See <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215636/>
+  "google-survey" = NA_character_, #@Carlyn has requested DUA from Roni, waiting.
+  "google-symptoms" = NA_character_, #public
+  "hhs" = NA_character_, #public gov't
+  "hospital-admissions" = "https://drive.google.com/drive/u/1/folders/11kvTzVR5Yd3lVszxmPHxFZcAYjIpoLcf", #"https://cmu.box.com/s/l2tz6kmiws6jyty2azwb43poiepz0565"
+  "indicator-combination" = "See Doctor Visits, Facebook Survey, and Google Health Trends",
+  "jhu-csse" = NA_character_, #public
+  "nchs-mortality" = "https://www.cdc.gov/nchs/data_access/restrictions.htm",
+  "quidel" = "https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS",
+  "safegraph" = "https://drive.google.com/drive/u/1/folders/1qkcUpdkJOkbSBBszrSCA4n1vSQKSlv3x",
+  "usa-facts" = NA_character_, #public
+  "youtube-survey" = NA_character_ # contract expected, but not found
+)
+source_updated[, col] <- dua_link[source_updated$data_source]
+
+
+
+new_fields_with_missings <- lapply(new_fields, function(col) {
+  any(is.na(source_updated[, col]))
+})
+new_fields_with_missings <- names(new_fields_with_missings[unlist(new_fields_with_missings)])
+
+message(
+  paste(new_fields_with_missings, collapse = ", "),
+  " columns still contain missing values and need to be filled in"
+)
+
+
+
+# Save updated signals table to CSV [readr::write_csv]
+write_csv(source_updated, file = "updated_signal_spreadsheet.csv")
+
+
+# Final manual steps:
+# open CSV in a GUI editor (excel or google sheets). copy scope date columns and paste into original spreadsheet online [manual]
diff --git a/src/client/delphi_epidata.R b/src/client/delphi_epidata.R
index 946fceaa8..b35e1213e 100644
--- a/src/client/delphi_epidata.R
+++ b/src/client/delphi_epidata.R
@@ -15,7 +15,7 @@ Epidata <- (function() {
   # API base url
   BASE_URL <- getOption('epidata.url', default = 'https://api.delphi.cmu.edu/epidata/')
 
-  client_version <- '4.1.22'
+  client_version <- '4.1.23'
 
   auth <- getOption("epidata.auth", default = NA)
 
diff --git a/src/client/delphi_epidata.js b/src/client/delphi_epidata.js
index 5a5c57cae..7c94b4d87 100644
--- a/src/client/delphi_epidata.js
+++ b/src/client/delphi_epidata.js
@@ -22,7 +22,7 @@
   }
 })(this, function (exports, fetchImpl, jQuery) {
   const BASE_URL = "https://api.delphi.cmu.edu/epidata/";
-  const client_version = "4.1.22";
+  const client_version = "4.1.23";
 
   // Helper function to cast values and/or ranges to strings
   function _listitem(value) {
diff --git a/src/client/delphi_epidata.py b/src/client/delphi_epidata.py
index 7756f655c..6fb7ab1ef 100644
--- a/src/client/delphi_epidata.py
+++ b/src/client/delphi_epidata.py
@@ -16,9 +16,9 @@
 
 from aiohttp import ClientSession, TCPConnector, BasicAuth
 
-from delphi.epidata.common.logger import get_structured_logger
+from delphi_utils.logger import get_structured_logger
 
-__version__ = "4.1.22"
+__version__ = "4.1.23"
 
 _HEADERS = {"user-agent": "delphi_epidata/" + __version__ + " (Python)"}
 
diff --git a/src/client/packaging/npm/package.json b/src/client/packaging/npm/package.json
index dcdd981ea..c1b92738c 100644
--- a/src/client/packaging/npm/package.json
+++ b/src/client/packaging/npm/package.json
@@ -2,7 +2,7 @@
   "name": "delphi_epidata",
   "description": "Delphi Epidata API Client",
   "authors": "Delphi Group",
-  "version": "4.1.22",
+  "version": "4.1.23",
   "license": "MIT",
   "homepage": "https://github.com/cmu-delphi/delphi-epidata",
   "bugs": {
diff --git a/src/client/packaging/pypi/CHANGELOG.md b/src/client/packaging/pypi/CHANGELOG.md
index 7cefbf96c..4d3890175 100644
--- a/src/client/packaging/pypi/CHANGELOG.md
+++ b/src/client/packaging/pypi/CHANGELOG.md
@@ -3,6 +3,15 @@
 All notable future changes to the `delphi_epidata` python client will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/).
 
+## [4.2.23] - 2024-05-31
+
+### Includes
+- https://github.com/cmu-delphi/delphi-epidata/pull/1460
+
+### Fixed
+- Replaced bad internal logger package import with one from `delphi_utils` package instead.
+  - This bug affected releases 4.1.21 and 4.1.22
+
 ## [4.1.21] - 2024-05-20
 
 ### Includes
diff --git a/src/client/packaging/pypi/setup.py b/src/client/packaging/pypi/setup.py
index 1a49acd8d..d71ce3db9 100644
--- a/src/client/packaging/pypi/setup.py
+++ b/src/client/packaging/pypi/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="delphi_epidata",
-    version="4.1.22",
+    version="4.1.23",
     author="David Farrow",
     author_email="dfarrow0@gmail.com",
     description="A programmatic interface to Delphi's Epidata API.",
@@ -16,7 +16,7 @@
         "Changelog": "https://github.com/cmu-delphi/delphi-epidata/blob/main/src/client/packaging/pypi/CHANGELOG.md",
     },
     packages=setuptools.find_packages(),
-    install_requires=["aiohttp", "requests>=2.7.0", "tenacity"],
+    install_requires=["aiohttp", "delphi-utils", "requests>=2.7.0", "tenacity"],
     classifiers=[
         "Programming Language :: Python",
         "License :: OSI Approved :: MIT License",
diff --git a/src/server/_config.py b/src/server/_config.py
index 57e031b83..73b8f88f5 100644
--- a/src/server/_config.py
+++ b/src/server/_config.py
@@ -7,7 +7,7 @@
 
 load_dotenv()
 
-VERSION = "4.1.22"
+VERSION = "4.1.23"
 
 MAX_RESULTS = int(10e6)
 MAX_COMPATIBILITY_RESULTS = int(3650)
diff --git a/src/server/endpoints/covidcast_utils/db_signals.csv b/src/server/endpoints/covidcast_utils/db_signals.csv
index 905c8a10d..bcddbc442 100644
--- a/src/server/endpoints/covidcast_utils/db_signals.csv
+++ b/src/server/endpoints/covidcast_utils/db_signals.csv
@@ -1454,65 +1454,65 @@ jhu-csse,deaths_cumulative_num,TRUE,deaths_incidence_prop,FALSE,"Confirmed COVID
      do not affect past report dates.",All,None,dead,None,Data is available for all counties and some territorial county equivalents. Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,CC BY,See license,NA,
 nchs-mortality,deaths_allcause_incidence_num,FALSE,deaths_allcause_incidence_num,FALSE,All Causes Deaths (Weekly new),TRUE,Number of weekly new deaths from all causes,"Number of weekly new deaths from all causes.
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,n/a,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,n/a,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
 nchs-mortality,deaths_allcause_incidence_num,TRUE,deaths_allcause_incidence_prop,FALSE,"All Causes Deaths (Weekly new, per 100k people)",TRUE,"Number of weekly new deaths from all causes, per 100k people","Number of weekly new deaths from all causes, per 100k people.
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,n/a,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,n/a,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
 nchs-mortality,deaths_covid_and_pneumonia_notflu_incidence_num,FALSE,deaths_covid_and_pneumonia_notflu_incidence_num,FALSE,COVID and Pneumonia excl. Influenza Deaths (Weekly new),TRUE,"Number of weekly new deaths involving COVID-19 and Pneumonia, excluding Influenza ","Number of weekly new deaths involving COVID-19 and Pneumonia, excluding Influenza .
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,"covid, pneumonia",Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,"covid, pneumonia",Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
 nchs-mortality,deaths_covid_and_pneumonia_notflu_incidence_num,TRUE,deaths_covid_and_pneumonia_notflu_incidence_prop,FALSE,"COVID and Pneumonia excl. Influenza Deaths (Weekly new, per 100k people)",TRUE,"Number of weekly new deaths involving COVID-19 and Pneumonia, excluding Influenza, per 100k people","Number of weekly new deaths involving COVID-19 and Pneumonia, excluding Influenza, per 100k people.
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,covid,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,covid,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
 nchs-mortality,deaths_covid_incidence_num,FALSE,deaths_covid_incidence_num,FALSE,Confirmed or Presumed COVID Deaths (Weekly new),TRUE,Number of weekly new deaths with confirmed or presumed COVID-19 ,"Number of weekly new deaths with confirmed or presumed COVID-19 .
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,covid,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,covid,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
 nchs-mortality,deaths_covid_incidence_num,TRUE,deaths_covid_incidence_prop,FALSE,"Confirmed or Presumed COVID Deaths (Weekly new, per 100k people)",TRUE,"Number of weekly new deaths with confirmed or presumed COVID-19, per 100k people","Number of weekly new deaths with confirmed or presumed COVID-19, per 100k people.
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,covid,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,covid,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
 nchs-mortality,deaths_flu_incidence_num,FALSE,deaths_flu_incidence_num,FALSE,Influenza Deaths (Weekly new),TRUE,"Number of weekly new deaths involving Influenza and at least one of (Pneumonia, COVID-19)","Number of weekly new deaths involving Influenza and at least one of (Pneumonia, COVID-19).
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,flu,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,flu,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
 nchs-mortality,deaths_flu_incidence_num,TRUE,deaths_flu_incidence_prop,FALSE,"Influenza Deaths (Weekly new, per 100k people)",TRUE,"Number of weekly new deaths involving Influenza and at least one of (Pneumonia, COVID-19), per 100k people","Number of weekly new deaths involving Influenza and at least one of (Pneumonia, COVID-19), per 100k people.
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,flu,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,flu,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
 nchs-mortality,deaths_percent_of_expected,FALSE,deaths_percent_of_expected,FALSE,Percentage of Expected Deaths,TRUE,Weekly new deaths for all causes in 2020 as a percentage of the average number across the same week in 2017-2019.,"Weekly new deaths for all causes in 2020 as a percentage of the average number across the same week in 2017-2019..
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,n/a,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Percentage,percent,late,neutral,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,n/a,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Percentage,percent,late,neutral,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
 nchs-mortality,deaths_pneumonia_notflu_incidence_num,FALSE,deaths_pneumonia_notflu_incidence_num,FALSE,Pneumonia excl. Influenza Deaths (Weekly new),TRUE,"Number of weekly new deaths involving Pneumonia, excluding Influenza deaths ","Number of weekly new deaths involving Pneumonia, excluding Influenza deaths .
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,pneumonia,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,pneumonia,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
 nchs-mortality,deaths_pneumonia_notflu_incidence_num,TRUE,deaths_pneumonia_notflu_incidence_prop,FALSE,"Pneumonia excl. Influenza Deaths (Weekly new, per 100k people)",TRUE,"Number of weekly new deaths involving Pneumonia, excluding Influenza deaths, per 100k people","Number of weekly new deaths involving Pneumonia, excluding Influenza deaths, per 100k people.
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,pneumonia,Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,pneumonia,Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
 nchs-mortality,deaths_pneumonia_or_flu_or_covid_incidence_num,FALSE,deaths_pneumonia_or_flu_or_covid_incidence_num,FALSE,"COVID, Pneumonia or Influenza Deaths (Weekly new)",TRUE,"Number of weekly new deaths involving Pneumonia, Influenza, or COVID-19 ","Number of weekly new deaths involving Pneumonia, Influenza, or COVID-19 .
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,"pneumonia, flu, covid",Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,"pneumonia, flu, covid",Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,count,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/nchs-mortality.html
 nchs-mortality,deaths_pneumonia_or_flu_or_covid_incidence_num,TRUE,deaths_pneumonia_or_flu_or_covid_incidence_prop,FALSE,"COVID, Pneumonia or Influenza Deaths (Weekly new, per 100k people)",TRUE,"Number of weekly new deaths involving Pneumonia, Influenza, or COVID-19, per 100k people","Number of weekly new deaths involving Pneumonia, Influenza, or COVID-19, per 100k people.
 
-National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,"pneumonia, flu, covid",Deaths,USA,"state,nation",nation,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
-quidel-covid-ag,covid_ag_raw_pct_positive,FALSE,covid_ag_raw_pct_positive,FALSE,COVID-19 Antigen Tests: Percent Positive,TRUE,Percentage of antigen tests that were positive for COVID-19,"When a patient (whether at a doctor's office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19.",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 7% of counties, half that on weekends. Data is available for about 90% of states, about 70% on weekends",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
-quidel-covid-ag,covid_ag_raw_pct_positive,TRUE,covid_ag_smoothed_pct_positive,FALSE,COVID-19 Antigen Tests: Percent Positive (7-day average),TRUE,,,Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 50% of counties, and all or nearly all states.",Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
-quidel-covid-ag,covid_ag_raw_pct_positive_age_0_17,FALSE,covid_ag_raw_pct_positive_age_0_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-17",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 0-17,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-17",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
-quidel-covid-ag,covid_ag_raw_pct_positive_age_0_17,TRUE,covid_ag_smoothed_pct_positive_age_0_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-17 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-17, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
-quidel-covid-ag,covid_ag_raw_pct_positive_age_0_4,FALSE,covid_ag_raw_pct_positive_age_0_4,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-4",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 0-4,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-4",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
-quidel-covid-ag,covid_ag_raw_pct_positive_age_0_4,TRUE,covid_ag_smoothed_pct_positive_age_0_4,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-4 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-4, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
-quidel-covid-ag,covid_ag_raw_pct_positive_age_18_49,FALSE,covid_ag_raw_pct_positive_age_18_49,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 18-49",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 18-49,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 18-49",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
-quidel-covid-ag,covid_ag_raw_pct_positive_age_18_49,TRUE,covid_ag_smoothed_pct_positive_age_18_49,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 18-49 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 18-49, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
-quidel-covid-ag,covid_ag_raw_pct_positive_age_5_17,FALSE,covid_ag_raw_pct_positive_age_5_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 5-17",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 5-17,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 5-17",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
-quidel-covid-ag,covid_ag_raw_pct_positive_age_5_17,TRUE,covid_ag_smoothed_pct_positive_age_5_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 5-17 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 5-17, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
-quidel-covid-ag,covid_ag_raw_pct_positive_age_50_64,FALSE,covid_ag_raw_pct_positive_age_50_64,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 50-64",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 50-64,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 50-64",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
-quidel-covid-ag,covid_ag_raw_pct_positive_age_50_64,TRUE,covid_ag_smoothed_pct_positive_age_50_64,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 50-64 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 50-64, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
-quidel-covid-ag,covid_ag_raw_pct_positive_age_65plus,FALSE,covid_ag_raw_pct_positive_age_65plus,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 65+",TRUE,Percentage of antigen tests that were positive for COVID-19 among people age 65 and above,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 65 and above",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
-quidel-covid-ag,covid_ag_raw_pct_positive_age_65plus,TRUE,covid_ag_smoothed_pct_positive_age_65plus,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 65+ (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 65 and above, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+National provisional death counts is based on death certificate data received and coded by the National Center for Health Statistics ([NCHS](https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm))",NCHS Mortality Data,"pneumonia, flu, covid",Deaths,USA,"state,nation",,2020-05,"Start dates vary by geo: nation 2020-06, state 2020-05",Ongoing,NA,week,Week,weekly,11-17 days,Weekly. All-cause mortality takes ~6 weeks on average to achieve 99% of its final value (https://link.springer.com/article/10.1057/s41271-021-00309-7),All,None,dead,"Unavailable by NCHS when counts are between 1 and 9, and for weeks where the counts are less than 50% of the expected number, since these provisional counts are highly incomplete and potentially misleading",Data is available for all states and some territories.,Value,per100k,late,bad,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,NCHS Data Use Agreement (https://www.cdc.gov/nchs/data_access/restrictions.htm),See license,https://www.cdc.gov/nchs/data_access/restrictions.htm,
+quidel-covid-ag,covid_ag_raw_pct_positive,FALSE,covid_ag_raw_pct_positive,FALSE,COVID-19 Antigen Tests: Percent Positive,TRUE,Percentage of antigen tests that were positive for COVID-19,"When a patient (whether at a doctor's office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19.",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 7% of counties, half that on weekends. Data is available for about 90% of states, about 70% on weekends",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
+quidel-covid-ag,covid_ag_raw_pct_positive,TRUE,covid_ag_smoothed_pct_positive,FALSE,COVID-19 Antigen Tests: Percent Positive (7-day average),TRUE,,,Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 50% of counties, and all or nearly all states.",Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+quidel-covid-ag,covid_ag_raw_pct_positive_age_0_17,FALSE,covid_ag_raw_pct_positive_age_0_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-17",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 0-17,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-17",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
+quidel-covid-ag,covid_ag_raw_pct_positive_age_0_17,TRUE,covid_ag_smoothed_pct_positive_age_0_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-17 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-17, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. Data is available for about 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+quidel-covid-ag,covid_ag_raw_pct_positive_age_0_4,FALSE,covid_ag_raw_pct_positive_age_0_4,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-4",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 0-4,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-4",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
+quidel-covid-ag,covid_ag_raw_pct_positive_age_0_4,TRUE,covid_ag_smoothed_pct_positive_age_0_4,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 0-4 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 0-4, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. Data is available for about 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+quidel-covid-ag,covid_ag_raw_pct_positive_age_18_49,FALSE,covid_ag_raw_pct_positive_age_18_49,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 18-49",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 18-49,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 18-49",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
+quidel-covid-ag,covid_ag_raw_pct_positive_age_18_49,TRUE,covid_ag_smoothed_pct_positive_age_18_49,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 18-49 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 18-49, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. Data is available for about 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+quidel-covid-ag,covid_ag_raw_pct_positive_age_5_17,FALSE,covid_ag_raw_pct_positive_age_5_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 5-17",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 5-17,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 5-17",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
+quidel-covid-ag,covid_ag_raw_pct_positive_age_5_17,TRUE,covid_ag_smoothed_pct_positive_age_5_17,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 5-17 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 5-17, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. Data is available for about 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+quidel-covid-ag,covid_ag_raw_pct_positive_age_50_64,FALSE,covid_ag_raw_pct_positive_age_50_64,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 50-64",TRUE,Percentage of antigen tests that were positive for COVID-19 among people ages 50-64,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 50-64",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
+quidel-covid-ag,covid_ag_raw_pct_positive_age_50_64,TRUE,covid_ag_smoothed_pct_positive_age_50_64,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 50-64 (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 50-64, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. Data is available for about 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+quidel-covid-ag,covid_ag_raw_pct_positive_age_65plus,FALSE,covid_ag_raw_pct_positive_age_65plus,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 65+",TRUE,Percentage of antigen tests that were positive for COVID-19 among people age 65 and above,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 65 and above",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 0.7-2% of counties, half that on weekends. Data is available for about 30-45% of states, half that on weekends. Geographic coverage for smaller age groups (age 0-4 and age 65+) are also extremely limited at the HRR and MSA levels",Percentage,percent,cases_testing,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,[Technical description](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#covid-19-tests)
+quidel-covid-ag,covid_ag_raw_pct_positive_age_65plus,TRUE,covid_ag_smoothed_pct_positive_age_65plus,FALSE,"COVID-19 Antigen Tests: Percent Positive, Ages 65+ (Smoothed)",TRUE,,"When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. For this signal, we compute the percentage of antigen tests performed that were positive for COVID-19 among people ages 65 and above, smoothed using a 7-day moving average and geographical pooling",Quidel Inc. (COVID),covid,Testing,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2020-05-26,NA,Ongoing,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,"age (0-17, 0-4, 5-17, 18-49, 50-64, 65+)",ascertained (case),"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests",Data is available for about 5-15% of counties. Data is available for about 65-95% of states.,Percentage,percent,cases_testing,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
 quidel-flu,raw_pct_negative,FALSE,raw_pct_negative,FALSE,Flu Tests: Percent Negative,FALSE,"The percentage of flu tests that are negative, suggesting the patient's illness has another cause, possibly COVID-19 ","The percentage of flu tests that are negative, suggesting the patient's illness has another cause, possibly COVID-19 .
 
-Discontinued May 19, 2020.",Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 7% of counties, half that on weekends. Data is available for about 90% of states, about 70% on weekends",Percentage,percent,late,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#flu-tests
-quidel-flu,raw_pct_negative,TRUE,smoothed_pct_negative,FALSE,Flu Tests: Percent Negative (7-day average),FALSE,,,Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 50% of counties, and all or nearly all states.",Percentage,percent,late,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+Discontinued May 19, 2020.",Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 7% of counties, half that on weekends. Data is available for about 90% of states, about 70% on weekends",Percentage,percent,late,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#flu-tests
+quidel-flu,raw_pct_negative,TRUE,smoothed_pct_negative,FALSE,Flu Tests: Percent Negative (7-day average),FALSE,,,Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 50% of counties, and all or nearly all states.",Percentage,percent,late,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
 quidel-flu,raw_tests_per_device,FALSE,raw_tests_per_device,FALSE,Flu Tests: Tests Per Device,FALSE,The average number of flu tests conducted by each testing device; measures volume of testing ,"The average number of flu tests conducted by each testing device; measures volume of testing .
 
-Discontinued May 19, 2020.",Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 7% of counties, half that on weekends. Data is available for about 90% of states, about 70% on weekends",Number of Tests,count,late,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#flu-tests
-quidel-flu,raw_tests_per_device,TRUE,smoothed_tests_per_device,FALSE,Flu Tests: Tests Per Device (7-day average),FALSE,,,Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,"Weekly. Happens, up to 6+ weeks after the report date.",Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 50% of counties, and all or nearly all states.",Number of Tests,count,late,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
+Discontinued May 19, 2020.",Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 7% of counties, half that on weekends. Data is available for about 90% of states, about 70% on weekends",Number of Tests,count,late,bad,FALSE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html#flu-tests
+quidel-flu,raw_tests_per_device,TRUE,smoothed_tests_per_device,FALSE,Flu Tests: Tests Per Device (7-day average),FALSE,,,Quidel Inc. (Flu),flu,Testing,USA,"msa,state","msa,state",2020-01-31,NA,2020-05-10,NA,day,Date,daily,5-6 days,Daily. Happens up to 6+ weeks after the report date. Revised values vary -3.5 to 1.8% around the final value for a given date and location.,Nationwide Quidel testing equipment network,None,population,"Discarded when an estimate is based on fewer than 50 tests. For smoothed signals at the county, MSA, and HRR levels with between 25 and 50 tests, the estimate is computed with the original N tests and 50-N synthetic tests that have the same test positivity rate as the parent state (state with the largest proportion of the population in this region); estimates are entirely discarded when based on fewer than 25 tests","Data is available for about 50% of counties, and all or nearly all states.",Number of Tests,count,late,bad,TRUE,FALSE,FALSE,TRUE,TRUE,Delphi,public,CC BY,Quidel provides Delphi data solely for internal research use and non-commercial research and analytics purposes for developing models for forecasting influenza-like epidemics and pandemics (CC BY).,https://drive.google.com/drive/u/1/folders/1HhOEbXlZXN9YpHBWOfrY7Wo2USVVfJVS,
 safegraph-daily,completely_home_prop,FALSE,completely_home_prop,FALSE,Completely Home,FALSE,The fraction of mobile devices that did not leave the immediate area of their home,"The fraction of mobile devices that did not leave the immediate area of their home. This is SafeGraph’s completely_home_device_count / device_count.
 
 Discontinued April 19th, 2021.",SafeGraph (Daily),n/a,Mobility,USA,"county,hrr,msa,state,hhs,nation","county,hrr,msa,state,hhs,nation",2019-01-01,"Start dates vary by geo: county 2019-01-01, hhs 2020-12-01, hrr 2019-01-01, msa 2019-01-01, nation 2020-12-01, state 2019-01-01",2021-04-16,NA,day,Date,weekly,3-11 days,None,Safegraph panel members who use mobile devices,None,population,"None. However, Safegraph uses differential privacy, which adds artificial noise to the incoming data. See https://docs.safegraph.com/docs/social-distancing-metrics for details",Data is available for all counties and some territorial county equivalents. Data is available for all states and some territories.,Value,per100k,public,neutral,FALSE,FALSE,FALSE,FALSE,FALSE,public,public,CC BY,"Delphi is free to publish and otherwise disclose the results of its Research (including but not limited to reports and papers and other activities conducted under the Research), including analyses and/or aggregated reporting of the Data. However, the underlying raw Data may not be published