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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.
Subject to change, current transmission dynamics follow this basic model
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.
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.
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
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