Nhsn indicator

.github/workflows/python-ci.yml

@@ -39,6 +39,8 @@ jobs:
             dir: "delphi_quidel_covidtest"
           - package: "sir_complainsalot"
             dir: "delphi_sir_complainsalot"
+          - package: "nhsn"
+            dir: "delphi_nhsn"
     defaults:
       run:
         working-directory: ${{ matrix.package }}

Jenkinsfile

@@ -10,7 +10,7 @@
    - TODO: #527 Get this list automatically from python-ci.yml at runtime.
  */
 
-def indicator_list = ['backfill_corrections', 'changehc', 'claims_hosp', 'google_symptoms', 'hhs_hosp', 'nchs_mortality', 'quidel_covidtest', 'sir_complainsalot', 'doctor_visits', 'nwss_wastewater', 'nssp']
+def indicator_list = ['backfill_corrections', 'changehc', 'claims_hosp', 'google_symptoms', 'hhs_hosp', 'nchs_mortality', 'quidel_covidtest', 'sir_complainsalot', 'doctor_visits', 'nwss_wastewater', 'nssp', 'nhsn']
 def build_package_main = [:]
 def build_package_prod = [:]
 def deploy_staging = [:]

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

@@ -0,0 +1,30 @@
+{
+  "common": {
+    "export_dir": "/common/covidcast/receiving/nhsn",
+    "backup_dir": "./raw_data_backups",
+    "log_filename": "/var/log/indicators/nhsn.log",
+    "log_exceptions": false
+  },
+  "indicator": {
+    "wip_signal": true,
+    "static_file_dir": "./static",
+    "socrata_token": "{{ nhsn_token }}"
+  },
+  "validation": {
+    "common": {
+      "data_source": "nhsn",
+      "api_credentials": "{{ validation_api_key }}",
+      "span_length": 15,
+      "min_expected_lag": {"all": "7"},
+      "max_expected_lag": {"all": "13"},
+      "dry_run": true,
+      "suppressed_errors": []
+    },
+    "static": {
+      "minimum_sample_size": 0,
+      "missing_se_allowed": true,
+      "missing_sample_size_allowed": true
+    },
+    "dynamic": {}
+  }
+}

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

@@ -45,6 +45,10 @@
     "nssp": {
       "max_age":19,
       "maintainers": []
+    },
+    "nhsn": {
+      "max_age":19,
+      "maintainers": []
     }
   }
 }

ansible/vars.yaml

@@ -59,6 +59,9 @@ nwss_wastewater_token: "{{ vault_cdc_socrata_token }}"
 # nssp
 nssp_token: "{{ vault_cdc_socrata_token }}"
 
+# nhsn
+nhsn_token: "{{ vault_cdc_socrata_token }}"
+
 # SirCAL
 sir_complainsalot_api_key: "{{ vault_sir_complainsalot_api_key }}"
 sir_complainsalot_slack_token: "{{ vault_sir_complainsalot_slack_token }}"

nhsn/DETAILS.md

@@ -0,0 +1,30 @@
+# NHSN data
+
+We import the NHSN Weekly Hospital Respiratory Data
+
+There are 2 sources we grab data from for nhsn:
+Note that they are from the same source, but with different cadence and one reporting preliminary data for the previous reporting week 
+
+Primary source: https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/ua7e-t2fy/about_data
+Secondary (preliminary source): https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data
+
+## Geographical Levels
+* `state`: reported using two-letter postal code
+* `national`: just `us` for now
+* `hhs`: reporting using Geomapper with state level 
+
+## Metrics
+*  `confirmed_admissions_covid`: total number of confirmed admission for covid
+*  `confirmed_admissions_flu`: total number of confirmed admission for flu
+*  `prelim_confirmed_admissions_covid`: total number of confirmed admission for covid from preliminary source
+*  `prelim_confirmed_admissions_flu`: total number of confirmed admission for flu  from preliminary source
+
+## Additional Notes
+HHS dataset and NHSN dataset covers the equivalent data of hospital admission for covid and flu.
+As a general trend, HHS and NHSN data matches pretty well.
+However, there are differences between some of the states, notably for GA (untill 2023), LA, NV, PR (late 2020-early 2021), TN all have HHS substantially lower, HHS is substantially lower than NHSN.
+
+Some states have this spike in NHSN or hhs where the other source doesn't have a spike and spikes don't happen at the same time_values across states
+
+More details regarding the analysis is available in the [analysis.ipynb](notebook%2Fanalysis.ipynb)
+(may require installing additional packages to work)

nhsn/Makefile

@@ -0,0 +1,32 @@
+.PHONY = venv, lint, test, clean
+
+dir = $(shell find ./delphi_* -name __init__.py | grep -o 'delphi_[_[:alnum:]]*' | head -1)
+venv:
+	python3.8 -m venv env
+
+install: venv
+	. env/bin/activate; \
+	pip install wheel ; \
+	pip install -e ../_delphi_utils_python ;\
+	pip install -e .
+
+install-ci: venv
+	. env/bin/activate; \
+	pip install wheel ; \
+	pip install ../_delphi_utils_python ;\
+	pip install .
+
+lint:
+	. env/bin/activate; pylint $(dir) --rcfile=../pyproject.toml
+	. env/bin/activate; pydocstyle $(dir)
+
+format:
+	. env/bin/activate; darker $(dir)
+
+test:
+	. env/bin/activate ;\
+	(cd tests && ../env/bin/pytest --cov=$(dir) --cov-report=term-missing)
+
+clean:
+	rm -rf env
+	rm -f params.json

nhsn/delphi_nhsn/__init__.py

@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+"""Module to pull and clean indicators from the XXXXX source.
+
+This file defines the functions that are made public by the module. As the
+module is intended to be executed though the main method, these are primarily
+for testing.
+"""
+
+from __future__ import absolute_import
+
+from . import run
+
+__version__ = "0.1.0"

nhsn/delphi_nhsn/__main__.py

@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+"""Call the function run_module when executed.
+
+This file indicates that calling the module (`python -m MODULE_NAME`) will
+call the function `run_module` found within the run.py file. There should be
+no need to change this template.
+"""
+
+from delphi_utils import read_params
+
+from .run import run_module  # pragma: no cover
+
+run_module(read_params())  # pragma: no cover

nhsn/delphi_nhsn/constants.py

@@ -0,0 +1,31 @@
+"""Registry for signal names."""
+
+GEOS = ["state", "nation", "hhs"]
+
+# column name from socrata
+TOTAL_ADMISSION_COVID_API = "totalconfc19newadm"
+TOTAL_ADMISSION_FLU_API = "totalconfflunewadm"
+
+SIGNALS_MAP = {
+    "confirmed_admissions_covid_ew": TOTAL_ADMISSION_COVID_API,
+    "confirmed_admissions_flu_ew": TOTAL_ADMISSION_FLU_API,
+}
+
+TYPE_DICT = {
+    "timestamp": "datetime64[ns]",
+    "geo_id": str,
+    "confirmed_admissions_covid_ew": float,
+    "confirmed_admissions_flu_ew": float,
+}
+
+# signal mapping for secondary, preliminary source
+PRELIM_SIGNALS_MAP = {
+    "confirmed_admissions_covid_ew_prelim": TOTAL_ADMISSION_COVID_API,
+    "confirmed_admissions_flu_ew_prelim": TOTAL_ADMISSION_FLU_API,
+}
+PRELIM_TYPE_DICT = {
+    "timestamp": "datetime64[ns]",
+    "geo_id": str,
+    "confirmed_admissions_covid_ew_prelim": float,
+    "confirmed_admissions_flu_ew_prelim": float,
+}

nhsn/delphi_nhsn/pull.py

@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+"""Functions for pulling NSSP ER data."""
+import logging
+from typing import Optional
+
+import pandas as pd
+from delphi_utils import create_backup_csv
+from sodapy import Socrata
+
+from .constants import PRELIM_SIGNALS_MAP, PRELIM_TYPE_DICT, SIGNALS_MAP, TYPE_DICT
+
+
+def pull_data(socrata_token: str, dataset_id: str):
+    """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(dataset_id, limit=limit, offset=offset)
+        if not page:
+            break  # exit the loop if no more results
+        results.extend(page)
+        offset += limit
+
+    df = pd.DataFrame.from_records(results)
+    return df
+
+
+def pull_nhsn_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
+
+    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, dataset_id="ua7e-t2fy")
+
+    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]
+
+        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)
+
+    return df
+
+
+def pull_preliminary_nhsn_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
+
+    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, dataset_id="mpgq-jmmr")
+
+    keep_columns = list(PRELIM_TYPE_DICT.keys())
+
+    if not df.empty:
+        create_backup_csv(df, backup_dir, custom_run, sensor="prelim", logger=logger)
+
+        df = df.rename(columns={"weekendingdate": "timestamp", "jurisdiction": "geo_id"})
+
+        for signal, col_name in PRELIM_SIGNALS_MAP.items():
+            df[signal] = df[col_name]
+
+        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"
+    else:
+        df = pd.DataFrame(columns=keep_columns)
+
+    return df