From 9a7d8d6c94aa4e895bd8a14b2ab0cafaaa6b0c23 Mon Sep 17 00:00:00 2001 From: AFg6K7h4fhy2 <127630341+AFg6K7h4fhy2@users.noreply.github.com> Date: Mon, 25 Nov 2024 14:45:26 -0500 Subject: [PATCH] start README update; start github action for rendering; render readme --- .github/workflows/render-readme.yaml | 30 +++ README.md | 359 +++++++++++++++++---------- README.qmd | 337 +++++++++++++++++++++++++ 3 files changed, 590 insertions(+), 136 deletions(-) create mode 100644 .github/workflows/render-readme.yaml create mode 100644 README.qmd diff --git a/.github/workflows/render-readme.yaml b/.github/workflows/render-readme.yaml new file mode 100644 index 0000000..32124d6 --- /dev/null +++ b/.github/workflows/render-readme.yaml @@ -0,0 +1,30 @@ +name: Render Quarto README + +on: + push: + paths: + - README.qmd + +jobs: + render-readme: + runs-on: ubuntu-latest + + steps: + - name: Checkout Repository + uses: actions/checkout@v3 + + - name: Set Up Quarto + uses: quarto-dev/quarto-actions/setup@v2 + + - name: Render README.md From README.qmd + run: quarto render README.qmd + + - name: Commit And Push Changes + run: | + git config --global user.name "github-actions[bot]" + git config --global user.email "github-actions[bot]@users.noreply.github.com" + git add README.md README.qmd + git commit -m "Auto-update README.md" + git push + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/README.md b/README.md index 418402f..0e7bd0c 100644 --- a/README.md +++ b/README.md @@ -1,37 +1,52 @@ -# CFA Forecast Tools (Python) +CFA Forecast Tools (Python) +================ -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. +Summary of `forecasttools-py`: ---- +- A Python package. +- Primarily supports the Short Term Forecast’s team. +- Intended to support wider Real Time Monitoring branch operations. +- Has tools for pre- and post-processing. + - Conversion of `az.InferenceData` forecast to Hubverse format. + - Addition of time and or dates to `az.InferenceData`. + +Notes: + +- *This repository is a WORK IN PROGRESS.* # Installation Install `forecasttools` via: -``` -pip3 install git+https://github.com/CDCgov/forecasttools-py@main -``` + pip3 install git+https://github.com/CDCgov/forecasttools-py@main # Vignettes -* Format Arviz Forecast Output For FluSight Submission (In Progress) +- Format Arviz Forecast Output For FluSight Submission (In Progress) # Datasets -`forecasttools` contains several datasets. These datasets aid with experimentation or are directly necessary to some of `forecasttools` utilities. +`forecasttools` contains several datasets. These datasets aid with +experimentation or are directly necessary to some of `forecasttools` +utilities. ## Location Table -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. +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 | |---------------|------------|-----------------------------| +| str | str | str | +| ————— | ———— | —————————– | | US | US | United States | | 1 | AL | Alabama | | 2 | AK | Alaska | @@ -40,7 +55,7 @@ Shape: (58, 3) | 6 | CA | California | | 8 | CO | Colorado | | 9 | CT | Connecticut | -| … | … | … | +| … | … | … | | 56 | WY | Wyoming | | 60 | AS | American Samoa | | 66 | GU | Guam | @@ -49,16 +64,18 @@ Shape: (58, 3) | 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: +The location table is stored in `forecasttools` as a `polars` dataframe +and is accessed via: -```python +``` python import forecasttools loc_table = forecasttools.location_table ``` -Using `data.py`, the location table was created by running the following: +Using `data.py`, the location table was created by running the +following: -```python +``` python make_census_dataset( file_save_path=os.path.join(os.getcwd(), "location_table.csv"), ) @@ -66,60 +83,71 @@ make_census_dataset( ## Example FluSight Hub Submission -The example FluSight submission comes from the [following 2023-24 submission](https://raw.githubusercontent.com/cdcepi/FluSight-forecast-hub/main/model-output/cfa-flumech/2023-10-14-cfa-flumech.csv). +The example FluSight submission comes from the [following 2023-24 +submission](https://raw.githubusercontent.com/cdcepi/FluSight-forecast-hub/main/model-output/cfa-flumech/2023-10-14-cfa-flumech.csv). 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: - -```python +| 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: + +``` python import forecasttools submission = forecasttools.example_flusight_submission ``` -Using `data.py`, the example FluSight submission was created by running the following: +Using `data.py`, the example FluSight submission was created by running +the following: -```python +``` python get_and_save_flusight_submission( file_save_path=os.path.join(os.getcwd(), "example_flusight_submission.csv"), ) ``` - ## NHSN COVID And Flu Hospital Admissions -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](https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh). 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. +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](https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh). +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 | |-------|------------|------| +| str | str | str | +| ——- | ———— | —— | | AK | 2020-03-23 | null | | AK | 2020-03-24 | null | | AK | 2020-03-25 | null | @@ -128,7 +156,7 @@ Shape: (81_713, 3) | 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 | @@ -137,10 +165,10 @@ Shape: (81_713, 3) | 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: -The fitting data is stored in `forecasttools` as a `polars` dataframe and is accessed via: - -```python +``` python import forecasttools @@ -151,9 +179,11 @@ covid_nhsn_data = forecasttools.nhsn_hosp_COVID flu_nhsn_data = forecasttools.nhsn_hosp_flu ``` -The 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: +The 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: -```python +``` python # generate COVID dataset make_nshn_fitting_dataset( dataset="COVID", @@ -171,22 +201,27 @@ make_nshn_fitting_dataset( ## Influenza Hospitalizations Forecast(s) -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 -``` +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 `idata`s are accessed via: -```python +``` python import forecasttools @@ -197,9 +232,10 @@ idata_w_dates = forecasttools.nhsn_flu_forecast_w_dates idata_wo_dates = forecasttools.nhsn_flu_forecast_wo_dates ``` -The forecast was generated following the creation of `nhsn_hosp_flu.csv` (see previous section) by running `data.py` with the following added: +The forecast was generated following the creation of `nhsn_hosp_flu.csv` +(see previous section) by running `data.py` with the following added: -```python +``` python make_forecast( nhsn_data=forecasttools.nhsn_hosp_flu, start_date="2022-08-08", @@ -214,73 +250,124 @@ make_forecast( The forecast looks like: -![Example NHSN-based Influenza forecast](./assets/example_forecast_w_dates.png){ width=75% } +
+ + +
---- +------------------------------------------------------------------------ # CDC Open Source Considerations -**General disclaimer** This repository was created for use by CDC programs to collaborate on public health related projects in support of the [CDC mission](https://www.cdc.gov/about/organization/mission.htm). GitHub is not hosted by the CDC, but is a third party website used by CDC and its partners to share information and collaborate on software. CDC use of GitHub does not imply an endorsement of any one particular service, product, or enterprise. - -## Rules, Policy, And Collaboration - -* [Open Practices](./rules-and-policies/open_practices.md) -* [Rules of Behavior](./rules-and-policies/rules_of_behavior.md) -* [Thanks and Acknowledgements](./rules-and-policies/thanks.md) -* [Disclaimer](DISCLAIMER.md) -* [Contribution Notice](CONTRIBUTING.md) -* [Code of Conduct](./rules-and-policies/code-of-conduct.md) - - -## Public Domain Standard Notice -This repository constitutes a work of the United States Government and is not -subject to domestic copyright protection under 17 USC § 105. This repository is in -the public domain within the United States, and copyright and related rights in -the work worldwide are waived through the [CC0 1.0 Universal public domain dedication](https://creativecommons.org/publicdomain/zero/1.0/). -All contributions to this repository will be released under the CC0 dedication. By -submitting a pull request you are agreeing to comply with this waiver of -copyright interest. - -## License Standard Notice -The repository utilizes code licensed under the terms of the Apache Software -License and therefore is licensed under ASL v2 or later. - -This source code in this repository is free: you can redistribute it and/or modify it under -the terms of the Apache Software License version 2, or (at your option) any -later version. - -This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY -WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A -PARTICULAR PURPOSE. See the Apache Software License for more details. - -You should have received a copy of the Apache Software License along with this -program. If not, see http://www.apache.org/licenses/LICENSE-2.0.html - -The source code forked from other open source projects will inherit its license. - -## Privacy Standard Notice +**General disclaimer** This repository was created for use by CDC +programs to collaborate on public health related projects in support of +the [CDC mission](https://www.cdc.gov/about/organization/mission.htm). +GitHub is not hosted by the CDC, but is a third party website used by +CDC and its partners to share information and collaborate on software. +CDC use of GitHub does not imply an endorsement of any one particular +service, product, or enterprise. + +
+ +Rules, Policy, And Collaboration + + +- [Open Practices](./rules-and-policies/open_practices.md) +- [Rules of Behavior](./rules-and-policies/rules_of_behavior.md) +- [Thanks and Acknowledgements](./rules-and-policies/thanks.md) +- [Disclaimer](DISCLAIMER.md) +- [Contribution Notice](CONTRIBUTING.md) +- [Code of Conduct](./rules-and-policies/code-of-conduct.md) + +
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+ +Contributing Standard Notice + + +Anyone is encouraged to contribute to the repository by +[forking](https://help.github.com/articles/fork-a-repo) and submitting a +pull request. (If you are new to GitHub, you might start with a [basic +tutorial](https://help.github.com/articles/set-up-git).) By contributing +to this project, you grant a world-wide, royalty-free, perpetual, +irrevocable, non-exclusive, transferable license to all users under the +terms of the [Apache Software License +v2](http://www.apache.org/licenses/LICENSE-2.0.html) or later. + +All comments, messages, pull requests, and other submissions received +through CDC including this GitHub page may be subject to applicable +federal law, including but not limited to the Federal Records Act, and +may be archived. Learn more at . +
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+ +Additional Standard Notices + +Please refer to [CDC’s Template +Repository](https://github.com/CDCgov/template) for more information +about [contributing to this +repository](https://github.com/CDCgov/template/blob/main/CONTRIBUTING.md), +[public domain notices and +disclaimers](https://github.com/CDCgov/template/blob/main/DISCLAIMER.md), +and [code of +conduct](https://github.com/CDCgov/template/blob/main/code-of-conduct.md). +
diff --git a/README.qmd b/README.qmd new file mode 100644 index 0000000..7cbc865 --- /dev/null +++ b/README.qmd @@ -0,0 +1,337 @@ +--- +title: CFA Forecast Tools (Python) +format: gfm # github flavored markdown +jupyter: python3 +--- + + + +Summary of `forecasttools-py`: + +* A Python package. +* Primarily supports the Short Term Forecast's team. +* Intended to support wider Real Time Monitoring branch operations. +* Has tools for pre- and post-processing. + * Conversion of `az.InferenceData` forecast to Hubverse format. + * Addition of time and or dates to `az.InferenceData`. + +Notes: + +* _This repository is a WORK IN PROGRESS._ + + +# Installation + +Install `forecasttools` via: + +``` +pip3 install git+https://github.com/CDCgov/forecasttools-py@main +``` + +# Vignettes + +* Format Arviz Forecast Output For FluSight Submission (In Progress) + +# Datasets + +`forecasttools` contains several datasets. These datasets aid with experimentation or are directly necessary to some of `forecasttools` utilities. + +## Location Table + +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: + +```python +import forecasttools +loc_table = forecasttools.location_table +``` + +Using `data.py`, the location table was created by running the following: + +```python +make_census_dataset( + file_save_path=os.path.join(os.getcwd(), "location_table.csv"), +) +``` + +## Example FluSight Hub Submission + +The example FluSight submission comes from the [following 2023-24 submission](https://raw.githubusercontent.com/cdcepi/FluSight-forecast-hub/main/model-output/cfa-flumech/2023-10-14-cfa-flumech.csv). + +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: + +```python +import forecasttools +submission = forecasttools.example_flusight_submission +``` + +Using `data.py`, the example FluSight submission was created by running the following: + +```python +get_and_save_flusight_submission( + file_save_path=os.path.join(os.getcwd(), "example_flusight_submission.csv"), +) +``` + + +## NHSN COVID And Flu Hospital Admissions + +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](https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh). 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: + +```python +import forecasttools + + +# access COVID data +covid_nhsn_data = forecasttools.nhsn_hosp_COVID + +# access flu data +flu_nhsn_data = forecasttools.nhsn_hosp_flu +``` + +The 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: + +```python +# 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") +) +``` + +## Influenza Hospitalizations Forecast(s) + +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 `idata`s are accessed via: + +```python +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_dates +``` + +The forecast was generated following the creation of `nhsn_hosp_flu.csv` (see previous section) by running `data.py` with the following added: + +```python +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: + +![Example NHSN-based Influenza forecast](./assets/example_forecast_w_dates.png){ width=75% } + +--- + +# CDC Open Source Considerations + +**General disclaimer** This repository was created for use by CDC programs to collaborate on public health related projects in support of the [CDC mission](https://www.cdc.gov/about/organization/mission.htm). 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