AdityaLab/GradABM
[AAMAS 2023] Differentiable Agent-based Epidemiology
This project helps public health researchers and epidemiologists build and analyze detailed agent-based models for infectious disease spread. It takes in real-world epidemiological data, such as COVID-19 or Flu case numbers by region and epidemic week, to output optimized model parameters that best fit the observed disease progression. The primary users are researchers focused on understanding and predicting disease dynamics.
No commits in the last 6 months.
Use this if you need to calibrate complex agent-based epidemiological models against real-world data to improve their predictive accuracy.
Not ideal if you are looking for a simple, off-the-shelf tool for basic disease forecasting without needing to deeply customize or train an agent-based model.
Stars
24
Forks
5
Language
Python
License
MIT
Category
Last pushed
Apr 10, 2024
Commits (30d)
0
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