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County COVID-19 Compartmental Models

Tools for forecasting COVID-19 Propagation at the US County Level. We implement a generalized comparmental model based on the SEIR formalism.

Dynamical Model Transitions

Examples

Flatten the Curve

The top figure represents unsupressed COVID19 flow through the population, while the second figure demonstrates the impact of distancing policies.

Installation

Install miniconda python 3.7 from here https://docs.conda.io/en/latest/miniconda.html

Execute conda env create -f environment.yaml

Activate the environment here.. conda activate pyseir

Installing pyseir

Change to into the county_covid_seir_models directory pip install -e .

Running Models

pyseir run-all --state=California

This will take a few minutes to download today's data, run inference and model ensembles, and generate the output. Then check the output/ folder for results.

Changelog

###4/3

  1. Add hospital admissions per day
  2. Add deaths per day
  3. Compute surge window start/end
  4. Plots case data and death data to county specific reports (still not yet fitting to death data: coming soon)
  5. Add empirical suppression policiesProduce 0%, 25%, 50%, 65% mitigation projections
  6. Reduce hospitalizations infection from 20% -> 10%