https://github.com/arnauqb/gradabm
Differentiable Agent-based Epidemiology
Science Score: 10.0%
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○CITATION.cff file
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○codemeta.json file
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✓Academic publication links
Links to: arxiv.org -
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○Scientific vocabulary similarity
Low similarity (7.5%) to scientific vocabulary
Repository
Differentiable Agent-based Epidemiology
Basic Info
- Host: GitHub
- Owner: arnauqb
- License: mit
- Default Branch: main
- Size: 102 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Differentiable Agent-based Epidemiology
Pre-print paper
Implementation of the paper "Differentiable Agent-based Epidemiology."
Authors: Ayush Chopra, Alexander Rodrguez, Jayakumar Subramanian, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar *Equal contribution
Pre-print: http://arxiv.org/abs/2207.09714
Requirements
Use the package manager conda to install required Python dependencies. Note: We used Python 3.7.
bash
conda env create -f enviroment.yml
Training
The following command will train and predict for all regions from epidemic week 202036 (GPU):
bash
python -u main.py -st MA -j -d 0 1 2 3 -ew 202036 --seed 1234 -m GradABM-time-varying -di COVID
where -st is the US state (joint model for counties in the state ), -j is joint training, -d are the GPU devices to be used, -ew is the epidemic week and di is disease (either COVID or Flu).
For running this in multiple weeks, see examples in Scripts/run.sh.
For CPU, you want to use:
bash
python -u main.py -st MA -j -d cpu -ew 202036 --seed 1234 -m GradABM-time-varying -di COVID
Contact:
If you have any questions about the code, please contact Alexander Rodriguez at arodriguezc[at]gatech[dot]edu and Ayush Chopra ayushc[at]mit[dot]edu
Owner
- Name: Arnau Quera-Bofarull
- Login: arnauqb
- Kind: user
- Location: Oxford
- Website: arnau.ai
- Repositories: 42
- Profile: https://github.com/arnauqb
Astrophysicist now doing research on agent-based modeling at Oxford.