The following repository, forecasttools-py is a Python package for common pre- and post-processing operations done by CFA Predict for short term forecasting, nowcasting, and scenario modeling.
NOTE: This repository is a WORK IN PROGRESS.
Install forecasttools via:
pip3 install git+https://github.com/CDCgov/forecasttools-py@main
- Format Arviz Forecast Output For FluSight Submission (In Progress)
forecasttools contains several datasets. These datasets aid with experimentation or are directly necessary to some of forecasttools utilities.
The location table contains abbreviations, codes, and extended names for the US jurisdictions for which the FluSight and COVID forecasting hubs require users to generate forecasts.
Shape: (58, 3)
| location_code | short_name | long_name |
|---|---|---|
| str | str | str |
| --------------- | ------------ | ----------------------------- |
| US | US | United States |
| 1 | AL | Alabama |
| 2 | AK | Alaska |
| 4 | AZ | Arizona |
| 5 | AR | Arkansas |
| 6 | CA | California |
| 8 | CO | Colorado |
| 9 | CT | Connecticut |
| … | … | … |
| 56 | WY | Wyoming |
| 60 | AS | American Samoa |
| 66 | GU | Guam |
| 69 | MP | Northern Mariana Islands |
| 72 | PR | Puerto Rico |
| 74 | UM | U.S. Minor Outlying Islands |
| 78 | VI | U.S. Virgin Islands |
The location table is stored in forecasttools as a polars dataframe and is accessed via:
import forecasttools
loc_table = forecasttools.location_tableUsing data.py, the location table was created by running the following:
make_census_dataset(
file_save_path=os.path.join(os.getcwd(), "location_table.csv"),
)The example FluSight submission comes from the following 2023-24 submission.
Shape: (4_876, 8)
| reference_date | target | horizon | target_end_date | location | output_type | output_type_id | value |
|---|---|---|---|---|---|---|---|
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.01 | 7.670286 |
| hosp | |||||||
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.025 | 9.968043 |
| hosp | |||||||
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.05 | 12.022354 |
| hosp | |||||||
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.1 | 14.497646 |
| hosp | |||||||
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.15 | 16.119813 |
| hosp | |||||||
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.2 | 17.670122 |
| hosp | |||||||
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.25 | 19.125462 |
| hosp | |||||||
| 2023-10-14 | wk inc flu | -1 | 2023-10-07 | 01 | quantile | 0.3 | 20.443282 |
| hosp | |||||||
| … | … | … | … | … | … | … | … |
| 2023-10-14 | wk inc flu | 2 | 2023-10-28 | US | quantile | 0.75 | 1995.98533 |
| hosp | 6 | ||||||
| 2023-10-14 | wk inc flu | 2 | 2023-10-28 | US | quantile | 0.99 | 4761.75738 |
| hosp | 5 |
The example FluSight submission is stored in forecasttools as a polars dataframe and is accessed via:
import forecasttools
submission = forecasttools.example_flusight_submissionUsing data.py, the example FluSight submission was created by running the following:
get_and_save_flusight_submission(
file_save_path=os.path.join(os.getcwd(), "example_flusight_submission.csv"),
)Hospital admissions data for fitting from NHSN for COVID and Flu is included in forecasttools as well, for user experimentation. This data is current as of 2024-04-27 and comes from the website HealthData.gov COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries. For influenza, the previous_day_admission_influenza_confirmed column is retained and for COVID the previous_day_admission_adult_covid_confirmed column is retained. As can be seen in the example below, some early dates for each jurisdiction do not have data.
Shape: (81_713, 3)
| state | date | hosp |
|---|---|---|
| str | str | str |
| ------- | ------------ | ------ |
| AK | 2020-03-23 | null |
| AK | 2020-03-24 | null |
| AK | 2020-03-25 | null |
| AK | 2020-03-26 | null |
| AK | 2020-03-27 | null |
| AK | 2020-03-28 | null |
| AK | 2020-03-29 | null |
| AK | 2020-03-30 | null |
| … | … | … |
| WY | 2024-04-21 | 0 |
| WY | 2024-04-22 | 2 |
| WY | 2024-04-23 | 1 |
| WY | 2024-04-24 | 1 |
| WY | 2024-04-25 | 0 |
| WY | 2024-04-26 | 0 |
| WY | 2024-04-27 | 0 |
The fitting data is stored in forecasttools as a polars dataframe and is accessed via:
import forecasttools
# access COVID data
covid_nhsn_data = forecasttools.nhsn_hosp_COVID
# access flu data
flu_nhsn_data = forecasttools.nhsn_hosp_fluThe data was created by placing a csv file called NHSN_RAW_20240926.csv (the full NHSN dataset) into ./forecasttools and running, in data.py, the following:
# generate COVID dataset
make_nshn_fitting_dataset(
dataset="COVID",
nhsn_dataset_path="NHSN_RAW_20240926.csv",
file_save_path=os.path.join(os.getcwd(),"nhsn_hosp_COVID.csv")
)
# generate flu dataset
make_nshn_fitting_dataset(
dataset="flu",
nhsn_dataset_path="NHSN_RAW_20240926.csv",
file_save_path=os.path.join(os.getcwd(),"nhsn_hosp_flu.csv")
)Two example forecasts stored in Arviz InferenceData objects are included for vignettes and user experimentation. Both are 28 day influenza hospital admissions forecasts for Texas made using a spline regression model fitted to NHSN data between 2022-08-08 and 2022-12-08. The only difference between the forecasts is that example_flu_forecast_w_dates.nc has dates as its coordinates. The idata objects which includes the observed data and posterior predictive samples is given below:
Inference data with groups:
> posterior
> posterior_predictive
> log_likelihood
> sample_stats
> prior
> prior_predictive
> observed_data
The forecast idatas are accessed via:
import forecasttools
# idata with dates as coordinates
idata_w_dates = forecasttools.nhsn_flu_forecast_w_dates
# idata without dates as coordinates
idata_wo_dates = forecasttools.nhsn_flu_forecast_wo_datesThe forecast was generated following the creation of nhsn_hosp_flu.csv (see previous section) by running data.py with the following added:
make_forecast(
nhsn_data=forecasttools.nhsn_hosp_flu,
start_date="2022-08-08",
end_date="2022-12-08",
juris_subset=["TX"],
forecast_days=28,
save_path="../forecasttools/example_flu_forecast_w_dates.nc",
save_idata=True,
use_log=False,
)The forecast looks like:
{ width=75% }
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