Analysing cholera epidemics in Argentina in the 90s using case count and genetic sequence data. The code implements fitting procedure for a meta-population susceptible-infected-asymptomatic-recovered (SIAR) model. More details about the model and used data can be found in the paper cited below. The case count data used in our study is available in the /data/ folder, together with our estimation of the migration patterns between Tartagal, San Ramon de la Nueva Oran and San Salvador de Jujuy. Algorithm allows to:
- Fit the data from three argentinian cities, which were hit by cholera in the 90s.
- fit_data.py
- Run the same fitting procedure, but with an additional assumption that only undercounting is present.
- only_undercount.py
- Run synthetic experiment with different types of noise.
- synth_gamma_.py
- synth_gauss_.py
- synth_negbinom_.py
- Run the same fitting procedure, but with additional noise on the travelling parameters.
- travel_noise.py
The code was developed while working on the following article: https://arxiv.org/pdf/2210.01956.pdf
If you found the repository useful in your work, we kindly ask that you cite:
@article{wilinski2022congruity,
title={Congruity of genomic and epidemiological data in modeling of local cholera outbreaks},
author={Wilinski, Mateusz and Castro, Lauren and Keithley, Jeffrey and Manore, Carrie and Campos, Josefina and Romero-Severson, Ethan and Domman, Daryl and Lokhov, Andrey Y},
journal={arXiv preprint arXiv:2210.01956},
year={2022}
}
This code is provided under a BSD license as part of the Optimization, Inference and Learning for Advanced Networks project, C18014.