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Add NSSP secondary source (#2074)
* base changes * lint * lint * add test * fix hhs bug + doc to readme + fix signal grouping * fix weird nan * logging + error details * test data sync * typo in pull.py * region to lower * add log * fix str bug * add backup data mechanism * adjust details.md * appease linter * add tests for secondary source backup * Update signal names to _2023RVR in constants.py * clarify _2023RVR signals in DETAILS.md
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‎nssp/DETAILS.md

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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.
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There are 2 sources we grab data from for nssp:
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- Primary source: https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/data_preview
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- Secondary (2023RVR) source: https://data.cdc.gov/Public-Health-Surveillance/2023-Respiratory-Virus-Response-NSSP-Emergency-Dep/7mra-9cq9/data_preview
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There are 8 signals output from the primary source and 4 output from secondary. There are no smoothed signals from secondary source.
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Note that the data produced from secondary source are mostly the same as their primary source equivalent, with past analysis shows around 95% of datapoints having less than 0.1 value difference and the other 5% having a 0.1 to 1.2 value difference.
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## Geographical Levels
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* `state`: reported using two-letter postal code
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* `county`: reported using fips code
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* `national`: just `us` for now
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Primary source:
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* `state`: reported from source using two-letter postal code
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* `county`: reported from source using fips code
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* `national`: just `us` for now, reported from source
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* `hhs`, `hrr`, `msa`: not reported from source, so we computed them from county-level data using a weighted mean. Each county is assigned a weight equal to its population in the last census (2020).
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Secondary (2023RVR) source:
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* `state`: reported from source
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* `hhs`: reported from source
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* `national`: reported from source
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## Metrics
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* `percent_visits_covid`, `percent_visits_rsv`, `percent_visits_influenza`: percentage of emergency department patient visits for specified pathogen.
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* `percent_visits_combined`: sum of the three percentages of visits for flu, rsv and covid.
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* `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.
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* `smoothed_percent_visits_combined`: 3 week moving average of the sum of the three percentages of visits for flu, rsv and covid.
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* `smoothed_percent_visits_combined`: 3 week moving average of the sum of the three percentages of visits for flu, rsv and covid.
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* `percent_visits_covid_2023RVR`, `percent_visits_rsv_2023RVR`, `percent_visits_influenza_2023RVR`: Taken from secondary source, percentage of emergency department patient visits for specified pathogen.
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* `percent_visits_combined_2023RVR`: Taken from secondary source, sum of the three percentages of visits for flu, rsv and covid.

‎nssp/README.md

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# NSSP Emergency Department Visit data
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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.
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There are 2 sources we grab data from for nssp:
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- Primary source: https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/data_preview
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- Secondary source: https://data.cdc.gov/Public-Health-Surveillance/2023-Respiratory-Virus-Response-NSSP-Emergency-Dep/7mra-9cq9/data_preview
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For details see the `DETAILS.md` file in this directory.
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## Create a MyAppToken

‎nssp/delphi_nssp/constants.py

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@@ -41,3 +41,29 @@
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"fips": str,
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}
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)
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SECONDARY_COLS_MAP = {
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"week_end": "timestamp",
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"geography": "geo_value",
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"percent_visits": "val",
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"pathogen": "signal",
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}
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SECONDARY_SIGNALS_MAP = {
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"COVID-19": "pct_ed_visits_covid_2023RVR",
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"Influenza": "pct_ed_visits_influenza_2023RVR",
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"RSV": "pct_ed_visits_rsv_2023RVR",
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"Combined": "pct_ed_visits_combined_2023RVR",
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}
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SECONDARY_SIGNALS = [val for (key, val) in SECONDARY_SIGNALS_MAP.items()]
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SECONDARY_GEOS = ["state", "nation", "hhs"]
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SECONDARY_TYPE_DICT = {
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"timestamp": "datetime64[ns]",
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"geo_value": str,
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"val": float,
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"geo_type": str,
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"signal": str,
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}
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SECONDARY_KEEP_COLS = [key for (key, val) in SECONDARY_TYPE_DICT.items()]

‎nssp/delphi_nssp/pull.py

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from delphi_utils import create_backup_csv
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from sodapy import Socrata
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from .constants import NEWLINE, SIGNALS, SIGNALS_MAP, TYPE_DICT
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from .constants import (
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NEWLINE,
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SECONDARY_COLS_MAP,
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SECONDARY_KEEP_COLS,
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SECONDARY_SIGNALS_MAP,
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SECONDARY_TYPE_DICT,
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SIGNALS,
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SIGNALS_MAP,
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TYPE_DICT,
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)
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def warn_string(df, type_dict):
@@ -29,42 +38,50 @@ def warn_string(df, type_dict):
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return warn
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def pull_nssp_data(socrata_token: str, backup_dir: str, custom_run: bool, logger: Optional[logging.Logger] = None):
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"""Pull the latest NSSP ER visits data, and conforms it into a dataset.
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The output dataset has:
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- Each row corresponds to a single observation
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- Each row additionally has columns for the signals in SIGNALS
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def pull_with_socrata_api(socrata_token: str, dataset_id: str):
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"""Pull data from Socrata API.
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Parameters
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----------
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socrata_token: str
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My App Token for pulling the NSSP data (could be the same as the nchs data)
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backup_dir: str
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Directory to which to save raw backup data
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custom_run: bool
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Flag indicating if the current run is a patch. If so, don't save any data to disk
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logger: Optional[logging.Logger]
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logger object
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dataset_id: str
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The dataset id to pull data from
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Returns
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-------
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pd.DataFrame
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Dataframe as described above.
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list of dictionaries, each representing a row in the dataset
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"""
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# Pull data from Socrata API
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client = Socrata("data.cdc.gov", socrata_token)
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results = []
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offset = 0
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limit = 50000 # maximum limit allowed by SODA 2.0
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while True:
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page = client.get("rdmq-nq56", limit=limit, offset=offset)
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page = client.get(dataset_id, limit=limit, offset=offset)
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if not page:
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break # exit the loop if no more results
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results.extend(page)
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offset += limit
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df_ervisits = pd.DataFrame.from_records(results)
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return results
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def pull_nssp_data(socrata_token: str, backup_dir: str, custom_run: bool, logger: Optional[logging.Logger] = None):
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"""Pull the latest NSSP ER visits primary dataset.
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https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/data_preview
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Parameters
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----------
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socrata_token: str
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My App Token for pulling the NSSP data (could be the same as the nchs data)
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Returns
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-------
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pd.DataFrame
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Dataframe as described above.
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"""
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socrata_results = pull_with_socrata_api(socrata_token, "rdmq-nq56")
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df_ervisits = pd.DataFrame.from_records(socrata_results)
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create_backup_csv(df_ervisits, backup_dir, custom_run, logger=logger)
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df_ervisits = df_ervisits.rename(columns={"week_end": "timestamp"})
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df_ervisits = df_ervisits.rename(columns=SIGNALS_MAP)
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keep_columns = ["timestamp", "geography", "county", "fips"]
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return df_ervisits[SIGNALS + keep_columns]
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def secondary_pull_nssp_data(
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socrata_token: str, backup_dir: str, custom_run: bool, logger: Optional[logging.Logger] = None
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):
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"""Pull the latest NSSP ER visits secondary dataset.
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https://data.cdc.gov/Public-Health-Surveillance/2023-Respiratory-Virus-Response-NSSP-Emergency-Dep/7mra-9cq9/data_preview
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The output dataset has:
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- Each row corresponds to a single observation
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Parameters
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----------
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socrata_token: str
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My App Token for pulling the NSSP data (could be the same as the nchs data)
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Returns
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-------
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pd.DataFrame
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Dataframe as described above.
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"""
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socrata_results = pull_with_socrata_api(socrata_token, "7mra-9cq9")
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df_ervisits = pd.DataFrame.from_records(socrata_results)
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create_backup_csv(df_ervisits, backup_dir, custom_run, sensor="secondary", logger=logger)
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df_ervisits = df_ervisits.rename(columns=SECONDARY_COLS_MAP)
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# geo_type is not provided in the dataset, so we infer it from the geo_value
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# which is either state names, "National" or hhs region numbers
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df_ervisits["geo_type"] = "state"
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df_ervisits.loc[df_ervisits["geo_value"] == "National", "geo_type"] = "nation"
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hhs_region_mask = df_ervisits["geo_value"].str.lower().str.startswith("region ")
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df_ervisits.loc[hhs_region_mask, "geo_value"] = df_ervisits.loc[hhs_region_mask, "geo_value"].str.replace(
135+
"Region ", ""
136+
)
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df_ervisits.loc[hhs_region_mask, "geo_type"] = "hhs"
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df_ervisits["signal"] = df_ervisits["signal"].map(SECONDARY_SIGNALS_MAP)
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df_ervisits = df_ervisits[SECONDARY_KEEP_COLS]
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try:
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df_ervisits = df_ervisits.astype(SECONDARY_TYPE_DICT)
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except KeyError as exc:
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raise ValueError(warn_string(df_ervisits, SECONDARY_TYPE_DICT)) from exc
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return df_ervisits

‎nssp/delphi_nssp/run.py

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from delphi_utils.geomap import GeoMapper
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from delphi_utils.nancodes import add_default_nancodes
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from .constants import AUXILIARY_COLS, CSV_COLS, GEOS, SIGNALS
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from .pull import pull_nssp_data
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from .constants import AUXILIARY_COLS, CSV_COLS, GEOS, SECONDARY_GEOS, SECONDARY_SIGNALS, SIGNALS
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from .pull import pull_nssp_data, secondary_pull_nssp_data
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def add_needed_columns(df, col_names=None):
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socrata_token = params["indicator"]["socrata_token"]
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8585
run_stats = []
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logger.info("Generating primary signals")
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## build the base version of the signal at the most detailed geo level you can get.
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## compute stuff here or farm out to another function or file
8890
df_pull = pull_nssp_data(socrata_token, backup_dir, custom_run=custom_run, logger=logger)
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139141
if len(dates) > 0:
140142
run_stats.append((max(dates), len(dates)))
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144+
logger.info("Generating secondary signals")
145+
secondary_df_pull = secondary_pull_nssp_data(socrata_token, backup_dir, custom_run, logger)
146+
for signal in SECONDARY_SIGNALS:
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secondary_df_pull_signal = secondary_df_pull[secondary_df_pull["signal"] == signal]
148+
if secondary_df_pull_signal.empty:
149+
logger.warning("No data found for signal", signal=signal)
150+
continue
151+
for geo in SECONDARY_GEOS:
152+
df = secondary_df_pull_signal.copy()
153+
logger.info("Generating signal and exporting to CSV", geo_type=geo, signal=signal)
154+
if geo == "state":
155+
df = df[(df["geo_type"] == "state")]
156+
df["geo_id"] = df["geo_value"].apply(
157+
lambda x: (
158+
us.states.lookup(x).abbr.lower()
159+
if us.states.lookup(x)
160+
else ("dc" if x == "District of Columbia" else x)
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)
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)
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unexpected_state_names = df[df["geo_id"] == df["geo_value"]]
164+
if unexpected_state_names.shape[0] > 0:
165+
logger.error(
166+
"Unexpected state names",
167+
unexpected_state_names=unexpected_state_names["geo_value"].unique(),
168+
)
169+
raise RuntimeError
170+
elif geo == "nation":
171+
df = df[(df["geo_type"] == "nation")]
172+
df["geo_id"] = "us"
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elif geo == "hhs":
174+
df = df[(df["geo_type"] == "hhs")]
175+
df["geo_id"] = df["geo_value"]
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# add se, sample_size, and na codes
177+
missing_cols = set(CSV_COLS) - set(df.columns)
178+
df = add_needed_columns(df, col_names=list(missing_cols))
179+
df_csv = df[CSV_COLS + ["timestamp"]]
180+
# actual export
181+
dates = create_export_csv(
182+
df_csv,
183+
geo_res=geo,
184+
export_dir=export_dir,
185+
sensor=signal,
186+
weekly_dates=True,
187+
)
188+
if len(dates) > 0:
189+
run_stats.append((max(dates), len(dates)))
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## log this indicator run
143192
logging(start_time, run_stats, logger)

‎nssp/tests/test_data/secondary_page.txt

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[
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{
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"week_end": "2022-10-01T00:00:00.000",
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"pathogen": "COVID-19", "geography": "National",
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"percent_visits": "1.8",
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"status": "Reporting",
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"trend_on_date": "Decreasing",
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"recent_trend": "Decreasing"
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},
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{
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"week_end": "2022-10-01T00:00:00.000",
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"pathogen": "Influenza",
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"geography": "National",
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"percent_visits": "0.5",
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"status": "Reporting",
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"trend_on_date": "Increasing",
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"recent_trend": "Increasing"
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},
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{
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"week_end": "2022-10-01T00:00:00.000",
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"pathogen": "RSV",
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"geography": "National",
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"percent_visits": "0.5",
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"status": "Reporting",
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"trend_on_date": "Increasing",
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"recent_trend": "Increasing"
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},
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{
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"week_end": "2022-10-01T00:00:00.000",
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"pathogen": "Combined",
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"geography": "National",
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"percent_visits": "2.8",
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"status": "Reporting",
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"trend_on_date": "Decreasing",
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"recent_trend": "Decreasing"
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},
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{
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"week_end": "2022-10-15T00:00:00.000",
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"pathogen": "COVID-19",
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"geography": "National",
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"percent_visits": "1.6",
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"status": "Reporting",
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"trend_on_date": "Decreasing",
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"recent_trend": "Decreasing"
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},
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{
47+
"week_end": "2022-10-15T00:00:00.000",
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"pathogen": "Influenza",
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"geography": "National",
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"percent_visits": "0.9",
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"status": "Reporting",
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"trend_on_date": "Increasing",
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"recent_trend": "Increasing"
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},
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{
56+
"week_end": "2022-10-15T00:00:00.000",
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"pathogen": "RSV",
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"geography": "National",
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"percent_visits": "0.7",
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"status": "Reporting",
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"trend_on_date": "Increasing",
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"recent_trend": "Increasing"
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},
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{
65+
"week_end": "2022-10-15T00:00:00.000",
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"pathogen": "Combined",
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"geography": "National",
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"percent_visits": "3.2",
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"status": "Reporting",
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"trend_on_date": "Increasing",
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"recent_trend": "Decreasing"
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}
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]

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