first implimentation of rsv

nhsn/delphi_nhsn/constants.py

@@ -8,29 +8,36 @@
 # column name from socrata
 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 = {
     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,
     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
@@ -39,15 +46,19 @@
 PRELIM_SIGNALS_MAP = {
     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,
     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

@@ -58,7 +58,10 @@ def check_last_updated(socrata_token, dataset_id, logger):
 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("Pulling data from Socrata API")
+    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
@@ -80,7 +83,8 @@ def pull_data(socrata_token: str, dataset_id: str, backup_dir: str, logger):
 
     if results:
         df = pd.DataFrame.from_records(results)
-        create_backup_csv(df, backup_dir, False, logger=logger)
+        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
@@ -120,6 +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.
@@ -137,6 +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
 
@@ -145,22 +154,26 @@ 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, MAIN_DATASET_ID, backup_dir, logger)
+        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)
     )
 
-    recently_updated = True if custom_run else check_last_updated(socrata_token, MAIN_DATASET_ID, logger)
+    recently_updated = True if custom_run else check_last_updated(socrata_token, dataset_id, logger)
+
+    type_dict = PRELIM_TYPE_DICT if preliminary else TYPE_DICT
+    keep_columns = list(type_dict.keys())
+    filtered_type_dict = copy.deepcopy(type_dict)
 
-    keep_columns = list(TYPE_DICT.keys())
+    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"})
-        filtered_type_dict = copy.deepcopy(TYPE_DICT)
 
-        for signal, col_name in SIGNALS_MAP.items():
+        for signal, col_name in signal_map.items():
             # older backups don't have certain columns
             try:
                 df[signal] = df[col_name]
@@ -178,66 +191,3 @@ def pull_nhsn_data(
         df = pd.DataFrame(columns=keep_columns)
 
     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.
-    """
-    # Pull data from Socrata API
-    df = (
-        pull_data(socrata_token, PRELIM_DATASET_ID, backup_dir, logger)
-        if not custom_run
-        else pull_data_from_file(backup_dir, issue_date, logger, prelim_flag=True)
-    )
-
-    keep_columns = list(PRELIM_TYPE_DICT.keys())
-    recently_updated = True if custom_run else check_last_updated(socrata_token, PRELIM_DATASET_ID, logger)
-
-    if not df.empty and recently_updated:
-        df = df.rename(columns={"weekendingdate": "timestamp", "jurisdiction": "geo_id"})
-        filtered_type_dict = copy.deepcopy(PRELIM_TYPE_DICT)
-
-        for signal, col_name in PRELIM_SIGNALS_MAP.items():
-            try:
-                df[signal] = df[col_name]
-            except KeyError:
-                logger.info("column not available in data", col_name=col_name, signal=signal)
-                keep_columns.remove(signal)
-                del filtered_type_dict[signal]
-
-        df = df[keep_columns]
-        df = df.astype(filtered_type_dict)
-
-        df["geo_id"] = df["geo_id"].str.lower()
-        df.loc[df["geo_id"] == "usa", "geo_id"] = "us"
-    else:
-        df = pd.DataFrame(columns=keep_columns)
-
-    return df

nhsn/delphi_nhsn/run.py

@@ -24,7 +24,7 @@
 from delphi_utils.export import create_export_csv
 
 from .constants import GEOS, PRELIM_SIGNALS_MAP, SIGNALS_MAP
-from .pull import pull_nhsn_data, pull_preliminary_nhsn_data
+from .pull import pull_nhsn_data
 
 
 def run_module(params, logger=None):
@@ -56,8 +56,8 @@ def run_module(params, logger=None):
         export_start_date = export_start_date.strftime("%Y-%m-%d")
 
     nhsn_df = pull_nhsn_data(socrata_token, backup_dir, custom_run=custom_run, issue_date=issue_date, logger=logger)
-    preliminary_nhsn_df = pull_preliminary_nhsn_data(
-        socrata_token, backup_dir, custom_run=custom_run, issue_date=issue_date, logger=logger
+    preliminary_nhsn_df = pull_nhsn_data(
+        socrata_token, backup_dir, custom_run=custom_run, issue_date=issue_date, logger=logger, preliminary=True
     )
 
     geo_mapper = GeoMapper()
@@ -92,6 +92,7 @@ def run_module(params, logger=None):
 
             df["se"] = np.nan
             df["sample_size"] = np.nan
+
             dates = create_export_csv(
                 df,
                 geo_res=geo,

nhsn/tests/test_data/expected_df.csv

@@ -0,0 +1,21 @@
+timestamp,geo_id,confirmed_admissions_covid_ew,confirmed_admissions_flu_ew,confirmed_admissions_rsv_ew,hosprep_confirmed_admissions_covid_ew,hosprep_confirmed_admissions_flu_ew,hosprep_confirmed_admissions_rsv_ew
+2021-08-21,md,53.0,2.0,0.0,13.0,13.0,1.0
+2021-08-21,co,852.0,0.0,,92.0,78.0,0.0
+2021-08-21,us,10384.0,6049.0,84.0,5426.0,5426.0,469.0
+2021-08-28,co,835.0,1.0,,92.0,78.0,0.0
+2021-08-28,us,94596.0,262.0,,5391.0,4397.0,0.0
+2021-09-04,co,1000.0,3.0,,92.0,78.0,0.0
+2021-09-04,us,93241.0,282.0,,5392.0,4396.0,0.0
+2021-09-11,co,982.0,2.0,,92.0,78.0,0.0
+2021-09-11,us,88162.0,247.0,,5391.0,4377.0,0.0
+2021-09-18,co,955.0,0.0,,92.0,78.0,0.0
+2021-09-18,us,79169.0,261.0,,5394.0,4362.0,0.0
+2021-09-25,co,993.0,0.0,,92.0,78.0,0.0
+2021-09-25,us,67740.0,234.0,,5393.0,4368.0,0.0
+2021-10-02,co,970.0,0.0,,92.0,78.0,0.0
+2021-10-02,us,58076.0,253.0,,5395.0,4391.0,0.0
+2021-10-09,co,1079.0,1.0,,92.0,78.0,0.0
+2021-10-09,us,51744.0,341.0,,5396.0,4379.0,0.0
+2021-10-16,co,1231.0,0.0,,92.0,78.0,0.0
+2021-10-16,us,45978.0,266.0,,5394.0,4307.0,0.0
+2021-10-16,region 1,45978.0,266.0,,5394.0,4307.0,0.0

nhsn/tests/test_data/expected_df_prelim.csv

@@ -0,0 +1,20 @@
+timestamp,geo_id,confirmed_admissions_covid_ew_prelim,confirmed_admissions_flu_ew_prelim,confirmed_admissions_rsv_ew_prelim,hosprep_confirmed_admissions_covid_ew_prelim,hosprep_confirmed_admissions_flu_ew_prelim,hosprep_confirmed_admissions_rsv_ew_prelim
+2021-08-21,mi,269.0,523.0,1.0,152.0,152.0,4.0
+2021-08-21,co,852.0,0.0,,92.0,78.0,0.0
+2021-08-21,us,8946.0,5576.0,61.0,5422.0,5422.0,485.0
+2021-08-28,co,835.0,1.0,,92.0,78.0,0.0
+2021-08-28,us,94596.0,262.0,,5391.0,4397.0,0.0
+2021-09-04,co,1000.0,3.0,,92.0,78.0,0.0
+2021-09-04,us,93241.0,282.0,,5392.0,4396.0,0.0
+2021-09-11,co,982.0,2.0,,92.0,78.0,0.0
+2021-09-11,us,88162.0,247.0,,5391.0,4377.0,0.0
+2021-09-18,co,955.0,0.0,,92.0,78.0,0.0
+2021-09-18,us,79169.0,261.0,,5394.0,4362.0,0.0
+2021-09-25,co,993.0,0.0,,92.0,78.0,0.0
+2021-09-25,us,67740.0,234.0,,5393.0,4368.0,0.0
+2021-10-02,co,970.0,0.0,,92.0,78.0,0.0
+2021-10-02,us,58076.0,253.0,,5395.0,4391.0,0.0
+2021-10-09,co,1079.0,1.0,,92.0,78.0,0.0
+2021-10-09,us,51744.0,341.0,,5396.0,4379.0,0.0
+2021-10-16,co,1231.0,0.0,,92.0,78.0,0.0
+2021-10-16,us,45978.0,266.0,,5394.0,4307.0,0.0