Release covidcast-indicators 0.3.61

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.60
+current_version = 0.3.61
 commit = True
 message = chore: bump covidcast-indicators to {new_version}
 tag = False

ansible/templates/sir_complainsalot-params-prod.json.j2

@@ -44,13 +44,7 @@
     },
     "nssp": {
       "max_age":19,
-      "maintainers": [],
-      "retired-signals": [
-        "pct_ed_visits_combined_2023rvr",
-        "pct_ed_visits_covid_2023rvr",
-        "pct_ed_visits_influenza_2023rvr",
-        "pct_ed_visits_rsv_2023rvr"
-      ]
+      "maintainers": []
     },
     "nhsn": {
       "max_age":19,

changehc/version.cfg

@@ -1 +1 @@
-current_version = 0.3.60
+current_version = 0.3.61

claims_hosp/version.cfg

@@ -1 +1 @@
-current_version = 0.3.60
+current_version = 0.3.61

doctor_visits/version.cfg

@@ -1 +1 @@
-current_version = 0.3.60
+current_version = 0.3.61

google_symptoms/version.cfg

@@ -1 +1 @@
-current_version = 0.3.60
+current_version = 0.3.61

hhs_hosp/version.cfg

@@ -1 +1 @@
-current_version = 0.3.60
+current_version = 0.3.61

nchs_mortality/version.cfg

@@ -1 +1 @@
-current_version = 0.3.60
+current_version = 0.3.61

nhsn/delphi_nhsn/constants.py

@@ -6,29 +6,59 @@
 PRELIM_DATASET_ID = "mpgq-jmmr"
 
 # column name from socrata
-TOTAL_ADMISSION_COVID_API = "totalconfc19newadm"
-TOTAL_ADMISSION_FLU_API = "totalconfflunewadm"
+TOTAL_ADMISSION_COVID_COL = "totalconfc19newadm"
+TOTAL_ADMISSION_FLU_COL = "totalconfflunewadm"
+TOTAL_ADMISSION_RSV_COL = "totalconfrsvnewadm"
+NUM_HOSP_REPORTING_COVID_COL = "totalconfc19newadmhosprep"
+NUM_HOSP_REPORTING_FLU_COL = "totalconfflunewadmhosprep"
+NUM_HOSP_REPORTING_RSV_COL = "totalconfrsvnewadmhosprep"
+# signal name
+TOTAL_ADMISSION_COVID = "confirmed_admissions_covid_ew"
+TOTAL_ADMISSION_FLU = "confirmed_admissions_flu_ew"
+TOTAL_ADMISSION_RSV = "confirmed_admissions_rsv_ew"
+NUM_HOSP_REPORTING_COVID = "hosprep_confirmed_admissions_covid_ew"
+NUM_HOSP_REPORTING_FLU = "hosprep_confirmed_admissions_flu_ew"
+NUM_HOSP_REPORTING_RSV = "hosprep_confirmed_admissions_rsv_ew"
 
 SIGNALS_MAP = {
-    "confirmed_admissions_covid_ew": TOTAL_ADMISSION_COVID_API,
-    "confirmed_admissions_flu_ew": TOTAL_ADMISSION_FLU_API,
+    TOTAL_ADMISSION_COVID: TOTAL_ADMISSION_COVID_COL,
+    TOTAL_ADMISSION_FLU: TOTAL_ADMISSION_FLU_COL,
+    TOTAL_ADMISSION_RSV: TOTAL_ADMISSION_RSV_COL,
+    NUM_HOSP_REPORTING_COVID: NUM_HOSP_REPORTING_COVID_COL,
+    NUM_HOSP_REPORTING_FLU: NUM_HOSP_REPORTING_FLU_COL,
+    NUM_HOSP_REPORTING_RSV: NUM_HOSP_REPORTING_RSV_COL,
 }
 
 TYPE_DICT = {
     "timestamp": "datetime64[ns]",
     "geo_id": str,
-    "confirmed_admissions_covid_ew": float,
-    "confirmed_admissions_flu_ew": float,
+    TOTAL_ADMISSION_COVID: float,
+    TOTAL_ADMISSION_FLU: float,
+    TOTAL_ADMISSION_RSV: float,
+    NUM_HOSP_REPORTING_COVID: float,
+    NUM_HOSP_REPORTING_FLU: float,
+    NUM_HOSP_REPORTING_RSV: float,
 }
 
 # signal mapping for secondary, preliminary source
+# made copy incase things would diverge
+
 PRELIM_SIGNALS_MAP = {
-    "confirmed_admissions_covid_ew_prelim": TOTAL_ADMISSION_COVID_API,
-    "confirmed_admissions_flu_ew_prelim": TOTAL_ADMISSION_FLU_API,
+    f"{TOTAL_ADMISSION_COVID}_prelim": TOTAL_ADMISSION_COVID_COL,
+    f"{TOTAL_ADMISSION_FLU}_prelim": TOTAL_ADMISSION_FLU_COL,
+    f"{TOTAL_ADMISSION_RSV}_prelim": TOTAL_ADMISSION_RSV_COL,
+    f"{NUM_HOSP_REPORTING_COVID}_prelim": NUM_HOSP_REPORTING_COVID_COL,
+    f"{NUM_HOSP_REPORTING_FLU}_prelim": NUM_HOSP_REPORTING_FLU_COL,
+    f"{NUM_HOSP_REPORTING_RSV}_prelim": NUM_HOSP_REPORTING_RSV_COL,
 }
+
 PRELIM_TYPE_DICT = {
     "timestamp": "datetime64[ns]",
     "geo_id": str,
-    "confirmed_admissions_covid_ew_prelim": float,
-    "confirmed_admissions_flu_ew_prelim": float,
+    f"{TOTAL_ADMISSION_COVID}_prelim": float,
+    f"{TOTAL_ADMISSION_FLU}_prelim": float,
+    f"{TOTAL_ADMISSION_RSV}_prelim": float,
+    f"{NUM_HOSP_REPORTING_COVID}_prelim": float,
+    f"{NUM_HOSP_REPORTING_FLU}_prelim": float,
+    f"{NUM_HOSP_REPORTING_RSV}_prelim": float,
 }

nhsn/delphi_nhsn/pull.py

@@ -1,8 +1,13 @@
 # -*- coding: utf-8 -*-
 """Functions for pulling NSSP ER data."""
+import copy
 import logging
+import random
+import time
+from datetime import datetime, timedelta
 from pathlib import Path
 from typing import Optional
+from urllib.error import HTTPError
 
 import pandas as pd
 from delphi_utils import create_backup_csv
@@ -11,20 +16,77 @@
 from .constants import MAIN_DATASET_ID, PRELIM_DATASET_ID, PRELIM_SIGNALS_MAP, PRELIM_TYPE_DICT, SIGNALS_MAP, TYPE_DICT
 
 
-def pull_data(socrata_token: str, dataset_id: str):
+def check_last_updated(socrata_token, dataset_id, logger):
+    """
+    Check last updated timestamp to determine if data should be pulled or not.
+
+    Note -- if the call to the API fails, the behavior is to treat the data as stale,
+    as possibly having duplicate is preferable to missing data
+
+    Parameters
+    ----------
+    socrata_token
+    dataset_id
+    logger
+
+    Returns bool
+    -------
+
+    """
+    recently_updated_source = True
+    try:
+        client = Socrata("data.cdc.gov", socrata_token)
+        response = client.get_metadata(dataset_id)
+
+        updated_timestamp = datetime.utcfromtimestamp(int(response["rowsUpdatedAt"]))
+        now = datetime.utcnow()
+        recently_updated_source = (now - updated_timestamp) < timedelta(days=1)
+
+        prelim_prefix = "Preliminary " if dataset_id == PRELIM_DATASET_ID else ""
+        if recently_updated_source:
+            logger.info(
+                f"{prelim_prefix}NHSN data was recently updated; Pulling data", updated_timestamp=updated_timestamp
+            )
+        else:
+            logger.info(f"{prelim_prefix}NHSN data is stale; Skipping", updated_timestamp=updated_timestamp)
+    # pylint: disable=W0703
+    except Exception as e:
+        logger.info("error while processing socrata metadata; treating data as stale", error=str(e))
+    return recently_updated_source
+
+
+def pull_data(socrata_token: str, dataset_id: str, backup_dir: str, logger):
     """Pull data from Socrata API."""
     client = Socrata("data.cdc.gov", socrata_token)
+    logger.info(
+        f"Pulling {'main' if dataset_id == MAIN_DATASET_ID else 'preliminary'} data from Socrata API",
+        dataset_id=dataset_id,
+    )
     results = []
     offset = 0
     limit = 50000  # maximum limit allowed by SODA 2.0
-    while True:
+    # retry logic for 500 error
+    try:
         page = client.get(dataset_id, limit=limit, offset=offset)
-        if not page:
-            break  # exit the loop if no more results
+    except HTTPError as err:
+        if err.code == 503:
+            time.sleep(2 + random.randint(0, 1000) / 1000.0)
+            page = client.get(dataset_id, limit=limit, offset=offset)
+        else:
+            logger.info("Error pulling data from Socrata API", error=str(err))
+            raise err
+
+    while len(page) > 0:
         results.extend(page)
         offset += limit
+        page = client.get(dataset_id, limit=limit, offset=offset)
 
-    df = pd.DataFrame.from_records(results)
+    if results:
+        df = pd.DataFrame.from_records(results)
+        sensor = "prelim" if dataset_id == PRELIM_DATASET_ID else None
+        create_backup_csv(df, backup_dir, False, sensor=sensor, logger=logger)
+    else:
+        df = pd.DataFrame()
     return df
 
 
@@ -62,6 +124,7 @@ def pull_nhsn_data(
     backup_dir: str,
     custom_run: bool,
     issue_date: Optional[str],
+    preliminary: bool = False,
     logger: Optional[logging.Logger] = None,
 ):
     """Pull the latest NHSN hospital admission data, and conforms it into a dataset.
@@ -79,6 +142,10 @@ def pull_nhsn_data(
         Directory to which to save raw backup data
     custom_run: bool
         Flag indicating if the current run is a patch. If so, don't save any data to disk
+    preliminary: bool
+        Flag indicating if the grabbing main or preliminary data
+    issue_date:
+        date to indicate which backup file to pull for patching
     logger: Optional[logging.Logger]
         logger object
 
@@ -87,83 +154,39 @@ def pull_nhsn_data(
     pd.DataFrame
         Dataframe as described above.
     """
+    dataset_id = PRELIM_DATASET_ID if preliminary else MAIN_DATASET_ID
     # Pull data from Socrata API
     df = (
-        pull_data(socrata_token, dataset_id=MAIN_DATASET_ID)
+        pull_data(socrata_token, dataset_id, backup_dir, logger)
         if not custom_run
-        else pull_data_from_file(backup_dir, issue_date, logger, prelim_flag=False)
+        else pull_data_from_file(backup_dir, issue_date, logger, prelim_flag=preliminary)
     )
 
-    keep_columns = list(TYPE_DICT.keys())
-
-    if not df.empty:
-        create_backup_csv(df, backup_dir, custom_run, logger=logger)
-
-        df = df.rename(columns={"weekendingdate": "timestamp", "jurisdiction": "geo_id"})
-
-        for signal, col_name in SIGNALS_MAP.items():
-            df[signal] = df[col_name]
+    recently_updated = True if custom_run else check_last_updated(socrata_token, dataset_id, logger)
 
-        df = df[keep_columns]
-        df["geo_id"] = df["geo_id"].str.lower()
-        df.loc[df["geo_id"] == "usa", "geo_id"] = "us"
-        df = df.astype(TYPE_DICT)
-    else:
-        df = pd.DataFrame(columns=keep_columns)
+    type_dict = PRELIM_TYPE_DICT if preliminary else TYPE_DICT
+    keep_columns = list(type_dict.keys())
+    filtered_type_dict = copy.deepcopy(type_dict)
 
-    return df
-
-
-def pull_preliminary_nhsn_data(
-    socrata_token: str,
-    backup_dir: str,
-    custom_run: bool,
-    issue_date: Optional[str],
-    logger: Optional[logging.Logger] = None,
-):
-    """Pull the latest preliminary NHSN hospital admission data, and conforms it into a dataset.
-
-    The output dataset has:
-
-    - Each row corresponds to a single observation
-    - Each row additionally has columns for the signals in SIGNALS
-
-    Parameters
-    ----------
-    socrata_token: str
-        My App Token for pulling the NHSN data
-    backup_dir: str
-        Directory to which to save raw backup data
-    custom_run: bool
-        Flag indicating if the current run is a patch. If so, don't save any data to disk
-    logger: Optional[logging.Logger]
-        logger object
-
-    Returns
-    -------
-    pd.DataFrame
-        Dataframe as described above.
-    """
-    df = (
-        pull_data(socrata_token, dataset_id=PRELIM_DATASET_ID)
-        if not custom_run
-        else pull_data_from_file(backup_dir, issue_date, logger, prelim_flag=True)
-    )
-
-    keep_columns = list(PRELIM_TYPE_DICT.keys())
-
-    if not df.empty:
-        create_backup_csv(df, backup_dir, custom_run, sensor="prelim", logger=logger)
+    signal_map = PRELIM_SIGNALS_MAP if preliminary else SIGNALS_MAP
 
+    if not df.empty and recently_updated:
         df = df.rename(columns={"weekendingdate": "timestamp", "jurisdiction": "geo_id"})
 
-        for signal, col_name in PRELIM_SIGNALS_MAP.items():
-            df[signal] = df[col_name]
+        for signal, col_name in signal_map.items():
+            # older backups don't have certain columns
+            try:
+                df[signal] = df[col_name]
+            except KeyError:
+                logger.info("column not available in data", col_name=col_name)
+                keep_columns.remove(signal)
+                del filtered_type_dict[signal]
 
         df = df[keep_columns]
-        df = df.astype(PRELIM_TYPE_DICT)
         df["geo_id"] = df["geo_id"].str.lower()
         df.loc[df["geo_id"] == "usa", "geo_id"] = "us"
+
+        df = df.astype(filtered_type_dict)
     else:
         df = pd.DataFrame(columns=keep_columns)