Fix nchs timestamp

nchs_mortality/DETAILS.md

@@ -10,20 +10,20 @@ consistency how NCHS reports the data, please refer to [Exceptions](#Exceptions)
 * `state`: reported using two-letter postal code
 
 ## Metrics, Level 1 (`m1`)
-* `covid_deaths`: All Deaths with confirmed or presumed COVID-19, 
+* `deaths_covid_incidence`: All Deaths with confirmed or presumed COVID-19, 
                   coded to ICD–10 code U07.1
-* `total_deaths`: Deaths from all causes.
-* `percent_of_expected_deaths`:  the number of deaths for all causes for this 
+* `deaths_allcause_incidence`: Deaths from all causes.
+* `deaths_percent_of_expected`:  the number of deaths for all causes for this 
                                  week in 2020 compared to the average number 
                                  across the same week in 2017–2019.
-* `pneumonia_deaths`: Counts of deaths involving Pneumonia, with or without
+* `deaths_pneumonia_notflu_incidence`: Counts of deaths involving Pneumonia, with or without
                       COVID-19, excluding Influenza deaths(J12.0-J18.9).
-* `pneumonia_and_covid_deaths`: Counts of deaths involving COVID-19 and Pneumonia,
+* `deaths_covid_and_pneumonia_notflu_incidence`: Counts of deaths involving COVID-19 and Pneumonia,
                                 excluding Influenza (U07.1 and J12.0-J18.9).
-* `influenza_deaths`: Counts of deaths involving Influenza, with or without 
+* `deaths_flu_incidence`: Counts of deaths involving Influenza, with or without 
                       COVID-19 or Pneumonia (J09-J11), includes COVID-19 or 
                       Pneumonia.
-* `pneumonia_influenza_or_covid_19_deaths`: Counts of deaths involving Pneumonia, 
+* `deaths_pneumonia_or_flu_or_covid_incidence`: Counts of deaths involving Pneumonia, 
                                             Influenza, or COVID-19, coded to ICD–10 
                                             codes U07.1 or J09–J18.9
 
@@ -32,7 +32,7 @@ Detailed descriptions are provided in the notes under Table 1 [here](https://www
 ## Metrics, Level 2 (`m2`)
 * `num`: number of new deaths on a given week
 * `prop`: `num` / population * 100,000
-* _**No** `m2` for signal `percent_of_expected_deaths`._
+* _**No** `m2` for signal `deaths_percent_of_expected`_.
 
 ## Exceptions
 
@@ -49,10 +49,7 @@ but we don't consider NYC separately. The death counts for NYC would be included
 ### Report Using Epiweeks 
 
 We report the NCHS Mortality data in a weekly format (`weekly_YYYYWW`, where `YYYYWW`
-refers to an epiweek). However, NCHS reports their weekly data from Saturday to 
-Saturday. We assume there is a one day shift. For example, they report a death counts 
-for Alaska in a week starting from date D, we will report the timestamp of this report 
-as the corresponding epiweek of date(D + 1).
+refers to an epiweek). As defined by CDC, [epiweeks](https://wwwn.cdc.gov/nndss/document/MMWR_Week_overview.pdf) are seven days from Sunday to Saturday. We use Python package [epiweeks](https://pypi.org/project/epiweeks/) to convert the week-ending dates in the raw dataset into epiweek format.
 
 ### Data Versioning
 Data versions are tracked on both a daily and weekly level.

nchs_mortality/delphi_nchs_mortality/export.py

@@ -26,7 +26,7 @@ def export_csv(df, geo_name, sensor, export_dir, start_date):
 
     for date in df["timestamp"].unique():
         t = Week.fromdate(pd.to_datetime(str(date)))
-        date_short = "weekly_" + str(t.year) + str(t.week + 1).zfill(2)
+        date_short = "weekly_" + str(t.year) + str(t.week).zfill(2)
         export_fn = f"{date_short}_{geo_name}_{sensor}.csv"
         result_df = df[df["timestamp"] == date][["geo_id", "val", "se", "sample_size"]]
         result_df.to_csv(f"{export_dir}/{export_fn}",

nchs_mortality/tests/test_export.py

@@ -29,7 +29,7 @@ def test_export(self):
         )
 
         # check data for 2020-06-02
-        expected_name = f"weekly_202024_state_region_thing.csv"
+        expected_name = f"weekly_202023_state_region_thing.csv"
         assert exists(join("./receiving", expected_name))
 
         output_data = pd.read_csv(join("./receiving", expected_name))
@@ -40,7 +40,7 @@ def test_export(self):
         assert (output_data.sample_size.values == [100, 500, 80]).all()
 
         # check data for 2020-06-03
-        expected_name = f"weekly_202025_state_region_thing.csv"
+        expected_name = f"weekly_202024_state_region_thing.csv"
         assert exists(join("./receiving", expected_name))
 
         output_data = pd.read_csv(join("./receiving", expected_name))