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CFA Scenarios Model

Overview | Model Structure | Quick Start | Data Sources | Project Admins | Fine Text and Disclaimers

Overview

Important

This repository is under active development and will be substantially refactored in the near future. Please look around, but we advise against working with this code until it has stabilized.

This repository is for the design and implementation of a Scenarios forecasting model, built by the Scenarios team within CFA-Predict.

Currently, we aim to use this code to forecast different disease tranmission scenarios with a compartmental mechanistic ODE model. This model is under development with transmission of SARS-CoV-2 as our primary focus. We plan to apply this model to the transmission of other respiratory viruses such as influenza and RSV. We aim to provide enough flexibility for the code users to explore a variety of scenarios, but also making certain design decisions that allow for fast computation and fitting as well as code readability.

What this model is:

a compartmental mechanistic ODE model that accounts for age structure, immunity history, vaccination, immunity waning and multiple variants.

What this model is not:

A fully dynamic suite of compartment models where any compartment may be easily added or removed. All models have assumptions, the basic compartment structure is assumed in many places, making it non-trivial to change.

Model Structure

Subject to change, current transmission dynamics follow this basic model

Quick Start

As a first pass at running model ODEs and inference of parameters, take a look at example_end_to_end_run.py to see the process of running our model. You will probably need to poetry install to get started as well, and then run it with poetry run examples/example_end_to_end_run.py.

Within example_end_to_end_run.py there is an optional --infer flag added from the terminal. This will kick off an example inference on synthetic data generated by the model itself and will produce different output.

To try out your own basic scenarios, check out the config/ folder, where you can modify parameters within the four base json files as you see fit and see their impacts on the model back at example_end_to_end_run.py.

If you are interested in understanding how the model is initialized, rather than looking through the model matricies yourself, the Scenarios team has created a Shiny application allowing for easy data visualization of the model's initial state! Simply run shiny_visualizers/visualizer_app.py and navigate to http://localhost:8000/ and play with the data yourself.

For a breakdown of the Ordinary Differential Equations at the heart of this model take a look at ode_model.md.

Technical Details

For a full in-depth description of the model please see the Github Pages of this repo, where a living document of the model is stored.

Project Admins

Thomas Hladish, Lead Data Scientist, [email protected], CDC/IOD/ORR/CFA

Ariel Shurygin, Data Scientist, [email protected], CDC/IOD/ORR/CFA

Kok Ben Toh, Data Scientist, [email protected], CDC/IOD/ORR/CFA

Michael Batista, Data Scientist, [email protected], CDC/IOD/ORR/CFA

General Disclaimer

This repository was created for use by CDC programs to collaborate on public health related projects in support of the CDC mission. 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.

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. 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

This repository 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.

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This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the Disclaimer and Code of Conduct. For more information about CDC's privacy policy, please visit http://www.cdc.gov/other/privacy.html.

Contributing Standard Notice

Anyone is encouraged to contribute to the repository by forking and submitting a pull request. (If you are new to GitHub, you might start with a basic tutorial.) 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 or later.

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Records Management Standard Notice

This repository is not a source of government records but is a copy to increase collaboration and collaborative potential. All government records will be published through the CDC web site.

Additional Standard Notices

Please refer to CDC's Template Repository for more information about contributing to this repository, public domain notices and disclaimers, and code of conduct.