Add NSSP secondary source

nssp/DETAILS.md

@@ -2,6 +2,11 @@
 
 We import the NSSP Emergency Department Visit data, including percentage and smoothed percentage of ER visits attributable to a given pathogen, from the CDC website. The data is provided at the county level, state level and national level; we do a population-weighted mean to aggregate from county data up to the HRR and MSA levels.
 
+There are 2 sources we grab data from for nssp:
+- Primary source: https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/data_preview
+- Secondary source: https://data.cdc.gov/Public-Health-Surveillance/2023-Respiratory-Virus-Response-NSSP-Emergency-Dep/7mra-9cq9/data_preview
+There are 8 signals output from the primary source and 4 output from secondary. Secondary source data is only available from state-level geos and above, though secondary data might be updated more often.
+
 ## Geographical Levels
 * `state`: reported using two-letter postal code
 * `county`: reported using fips code
@@ -10,4 +15,6 @@ We import the NSSP Emergency Department Visit data, including percentage and smo
 *  `percent_visits_covid`, `percent_visits_rsv`, `percent_visits_influenza`: percentage of emergency department patient visits for specified pathogen.
 *  `percent_visits_combined`: sum of the three percentages of visits for flu, rsv and covid.
 *  `smoothed_percent_visits_covid`, `smoothed_percent_visits_rsv`, `smoothed_percent_visits_influenza`: 3 week moving average of the percentage of emergency department patient visits for specified pathogen.
-*  `smoothed_percent_visits_combined`: 3 week moving average of the sum of the three percentages of visits for flu, rsv and covid.
+*  `smoothed_percent_visits_combined`: 3 week moving average of the sum of the three percentages of visits for flu, rsv and covid.
+*  `percent_visits_covid_secondary`, `percent_visits_rsv_secondary`, `percent_visits_influenza_secondary`: Taken from secondary source, percentage of emergency department patient visits for specified pathogen.
+*  `percent_visits_combined_secondary`: Taken from secondary source, sum of the three percentages of visits for flu, rsv and covid.

nssp/README.md

@@ -1,6 +1,11 @@
 # NSSP Emergency Department Visit data
 
 We import the NSSP Emergency Department Visit data, currently only the smoothed concentration, from the CDC website, aggregate to the state and national level from the wastewater sample site level, and export the aggregated data.
+
+There are 2 sources we grab data from for nssp:
+- Primary source: https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/data_preview
+- Secondary source: https://data.cdc.gov/Public-Health-Surveillance/2023-Respiratory-Virus-Response-NSSP-Emergency-Dep/7mra-9cq9/data_preview
+
 For details see the `DETAILS.md` file in this directory.
 
 ## Create a MyAppToken

nssp/delphi_nssp/constants.py

@@ -41,3 +41,29 @@
         "fips": str,
     }
 )
+
+SECONDARY_COLS_MAP = {
+    "week_end": "timestamp",
+    "geography": "geo_value",
+    "percent_visits": "val",
+    "pathogen": "signal",
+}
+
+SECONDARY_SIGNALS_MAP = {
+    "COVID-19": "pct_ed_visits_covid_secondary",
+    "Influenza": "pct_ed_visits_influenza_secondary",
+    "RSV": "pct_ed_visits_rsv_secondary",
+    "Combined": "pct_ed_visits_combined_secondary",
+}
+
+SECONDARY_SIGNALS = [val for (key, val) in SECONDARY_SIGNALS_MAP.items()]
+SECONDARY_GEOS = ["state", "nation", "hhs"]
+
+SECONDARY_TYPE_DICT = {
+    "timestamp": "datetime64[ns]",
+    "geo_value": str,
+    "val": float,
+    "geo_type": str,
+    "signal": str,
+}
+SECONDARY_KEEP_COLS = [key for (key, val) in SECONDARY_TYPE_DICT.items()]

nssp/delphi_nssp/pull.py

@@ -8,7 +8,16 @@
 from delphi_utils import create_backup_csv
 from sodapy import Socrata
 
-from .constants import NEWLINE, SIGNALS, SIGNALS_MAP, TYPE_DICT
+from .constants import (
+    NEWLINE,
+    SECONDARY_COLS_MAP,
+    SECONDARY_KEEP_COLS,
+    SECONDARY_SIGNALS_MAP,
+    SECONDARY_TYPE_DICT,
+    SIGNALS,
+    SIGNALS_MAP,
+    TYPE_DICT,
+)
 
 
 def warn_string(df, type_dict):
@@ -29,42 +38,50 @@ def warn_string(df, type_dict):
     return warn
 
 
-def pull_nssp_data(socrata_token: str, backup_dir: str, custom_run: bool, logger: Optional[logging.Logger] = None):
-    """Pull the latest NSSP ER visits 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
+def pull_with_socrata_api(socrata_token: str, dataset_id: str):
+    """Pull data from Socrata API.
 
     Parameters
     ----------
     socrata_token: str
         My App Token for pulling the NSSP data (could be the same as the nchs 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
+    dataset_id: str
+        The dataset id to pull data from
 
     Returns
     -------
-    pd.DataFrame
-        Dataframe as described above.
+    list of dictionaries, each representing a row in the dataset
     """
-    # Pull data from Socrata API
     client = Socrata("data.cdc.gov", socrata_token)
     results = []
     offset = 0
     limit = 50000  # maximum limit allowed by SODA 2.0
     while True:
-        page = client.get("rdmq-nq56", limit=limit, offset=offset)
+        page = client.get(dataset_id, limit=limit, offset=offset)
         if not page:
             break  # exit the loop if no more results
         results.extend(page)
         offset += limit
-    df_ervisits = pd.DataFrame.from_records(results)
+    return results
+
+
+def pull_nssp_data(socrata_token: str, backup_dir: str, custom_run: bool, logger: Optional[logging.Logger] = None):
+    """Pull the latest NSSP ER visits primary dataset.
+
+    https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/data_preview
+
+    Parameters
+    ----------
+    socrata_token: str
+        My App Token for pulling the NSSP data (could be the same as the nchs data)
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe as described above.
+    """
+    socrata_results = pull_with_socrata_api(socrata_token, "rdmq-nq56")
+    df_ervisits = pd.DataFrame.from_records(socrata_results)
     create_backup_csv(df_ervisits, backup_dir, custom_run, logger=logger)
     df_ervisits = df_ervisits.rename(columns={"week_end": "timestamp"})
     df_ervisits = df_ervisits.rename(columns=SIGNALS_MAP)
@@ -79,3 +96,50 @@ def pull_nssp_data(socrata_token: str, backup_dir: str, custom_run: bool, logger
 
     keep_columns = ["timestamp", "geography", "county", "fips"]
     return df_ervisits[SIGNALS + keep_columns]
+
+
+def secondary_pull_nssp_data(socrata_token: str):
+    """Pull the latest NSSP ER visits secondary dataset.
+
+    https://data.cdc.gov/Public-Health-Surveillance/2023-Respiratory-Virus-Response-NSSP-Emergency-Dep/7mra-9cq9/data_preview
+
+    The output dataset has:
+
+    - Each row corresponds to a single observation
+
+    Parameters
+    ----------
+    socrata_token: str
+        My App Token for pulling the NSSP data (could be the same as the nchs data)
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe as described above.
+    """
+    socrata_results = pull_with_socrata_api(socrata_token, "7mra-9cq9")
+    df_ervisits = pd.DataFrame.from_records(socrata_results)
+    df_ervisits = df_ervisits.rename(columns=SECONDARY_COLS_MAP)
+
+    # geo_type is not provided in the dataset, so we infer it from the geo_value
+    # which is either state names, "National" or hhs region numbers
+    df_ervisits["geo_type"] = "state"
+
+    df_ervisits.loc[df_ervisits["geo_value"] == "National", "geo_type"] = "nation"
+
+    hhs_region_mask = df_ervisits["geo_value"].str.startswith("Region ")
+    df_ervisits.loc[hhs_region_mask, "geo_value"] = df_ervisits.loc[hhs_region_mask, "geo_value"].str.replace(
+        "Region ", ""
+    )
+    df_ervisits.loc[hhs_region_mask, "geo_type"] = "hhs"
+
+    df_ervisits["signal"] = df_ervisits["signal"].map(SECONDARY_SIGNALS_MAP)
+
+    df_ervisits = df_ervisits[SECONDARY_KEEP_COLS]
+
+    try:
+        df_ervisits = df_ervisits.astype(SECONDARY_TYPE_DICT)
+    except KeyError as exc:
+        raise ValueError(warn_string(df_ervisits, SECONDARY_TYPE_DICT)) from exc
+
+    return df_ervisits

nssp/delphi_nssp/run.py

@@ -31,8 +31,8 @@
 from delphi_utils.geomap import GeoMapper
 from delphi_utils.nancodes import add_default_nancodes
 
-from .constants import AUXILIARY_COLS, CSV_COLS, GEOS, SIGNALS
-from .pull import pull_nssp_data
+from .constants import AUXILIARY_COLS, CSV_COLS, GEOS, SECONDARY_GEOS, SECONDARY_SIGNALS, SIGNALS
+from .pull import pull_nssp_data, secondary_pull_nssp_data
 
 
 def add_needed_columns(df, col_names=None):
@@ -83,6 +83,8 @@ def run_module(params):
     socrata_token = params["indicator"]["socrata_token"]
 
     run_stats = []
+
+    logger.info("Generating primary signals")
     ## build the base version of the signal at the most detailed geo level you can get.
     ## compute stuff here or farm out to another function or file
     df_pull = pull_nssp_data(socrata_token, backup_dir, custom_run=custom_run, logger=logger)
@@ -139,5 +141,51 @@ def run_module(params):
             if len(dates) > 0:
                 run_stats.append((max(dates), len(dates)))
 
+    logger.info("Generating secondary signals")
+    secondary_df_pull = secondary_pull_nssp_data(socrata_token)
+    for signal in SECONDARY_SIGNALS:
+        secondary_df_pull_signal = secondary_df_pull[secondary_df_pull["signal"] == signal]
+        if secondary_df_pull_signal.empty:
+            continue
+        for geo in SECONDARY_GEOS:
+            df = secondary_df_pull_signal.copy()
+            logger.info("Generating signal and exporting to CSV", geo_type=geo, signal=signal)
+            if geo == "state":
+                df = df[(df["geo_type"] == "state")]
+                df["geo_id"] = df["geo_value"].apply(
+                    lambda x: (
+                        us.states.lookup(x).abbr.lower()
+                        if us.states.lookup(x)
+                        else ("dc" if x == "District of Columbia" else x)
+                    )
+                )
+                unexpected_state_names = df[df["geo_id"] == df["geo_value"]]
+                if unexpected_state_names.shape[0] > 0:
+                    logger.error(
+                        "Unexpected state names",
+                        unexpected_state_names=unexpected_state_names["geo_value"].unique(),
+                    )
+                    raise RuntimeError
+            elif geo == "nation":
+                df = df[(df["geo_type"] == "nation")]
+                df["geo_id"] = "us"
+            elif geo == "hhs":
+                df = df[(df["geo_type"] == "hhs")]
+                df["geo_id"] = df["geo_value"]
+            # add se, sample_size, and na codes
+            missing_cols = set(CSV_COLS) - set(df.columns)
+            df = add_needed_columns(df, col_names=list(missing_cols))
+            df_csv = df[CSV_COLS + ["timestamp"]]
+            # actual export
+            dates = create_export_csv(
+                df_csv,
+                geo_res=geo,
+                export_dir=export_dir,
+                sensor=signal,
+                weekly_dates=True,
+            )
+            if len(dates) > 0:
+                run_stats.append((max(dates), len(dates)))
+
     ## log this indicator run
     logging(start_time, run_stats, logger)

nssp/tests/test_data/secondary_page.txt

@@ -0,0 +1,73 @@
+[
+    {
+        "week_end": "2022-10-01T00:00:00.000",
+        "pathogen": "COVID-19", "geography": "National",
+        "percent_visits": "1.8",
+        "status": "Reporting",
+        "trend_on_date": "Decreasing",
+        "recent_trend": "Decreasing"
+    },
+    {
+        "week_end": "2022-10-01T00:00:00.000",
+        "pathogen": "Influenza",
+        "geography": "National",
+        "percent_visits": "0.5",
+        "status": "Reporting",
+        "trend_on_date": "Increasing",
+        "recent_trend": "Increasing"
+    },
+    {
+        "week_end": "2022-10-01T00:00:00.000",
+        "pathogen": "RSV",
+        "geography": "National",
+        "percent_visits": "0.5",
+        "status": "Reporting",
+        "trend_on_date": "Increasing",
+        "recent_trend": "Increasing"
+    },
+    {
+        "week_end": "2022-10-01T00:00:00.000",
+        "pathogen": "Combined",
+        "geography": "National",
+        "percent_visits": "2.8",
+        "status": "Reporting",
+        "trend_on_date": "Decreasing",
+        "recent_trend": "Decreasing"
+    },
+    {
+        "week_end": "2022-10-15T00:00:00.000",
+        "pathogen": "COVID-19",
+        "geography": "National",
+        "percent_visits": "1.6",
+        "status": "Reporting",
+        "trend_on_date": "Decreasing",
+        "recent_trend": "Decreasing"
+    },
+    {
+        "week_end": "2022-10-15T00:00:00.000",
+        "pathogen": "Influenza",
+        "geography": "National",
+        "percent_visits": "0.9",
+        "status": "Reporting",
+        "trend_on_date": "Increasing",
+        "recent_trend": "Increasing"
+    },
+    {
+        "week_end": "2022-10-15T00:00:00.000",
+        "pathogen": "RSV",
+        "geography": "National",
+        "percent_visits": "0.7",
+        "status": "Reporting",
+        "trend_on_date": "Increasing",
+        "recent_trend": "Increasing"
+    },
+    {
+        "week_end": "2022-10-15T00:00:00.000",
+        "pathogen": "Combined",
+        "geography": "National",
+        "percent_visits": "3.2",
+        "status": "Reporting",
+        "trend_on_date": "Increasing",
+        "recent_trend": "Decreasing"
+    }
+]