Disease Modeling with Scipy and PyMC
summary
Uses SciPy ODE solvers and PyMC MCMC to fit interpretable compartmental models to Israeli Ministry of Health data, estimating the force of infection (beta) (modal ≈ 1.42) and an age-structured contact matrix. Fits recover parameter distributions, then simulates alternative vaccine schedules; changing ages yields only ~1% reduction so the policy does not change. Built a reproducible pipeline and a public site epidemics plus an open source helper dove panda both used in the work.
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chapters (14)
- 0:00Introduction
- 1:57Speaker background
- 3:01Dynamic models
- 5:50SIR framework
- 8:22Differential equations with SciPy
- 10:11MCMC and Bayesian modeling
- 14:50Fitting models with PyMC
- 17:24Model fitting with health data
- 19:31Beta in disease modeling
- 20:16Simulating policy outcomes
- 22:22Pertussis modeling with age groups
- 25:56Evaluating vaccination policies
- 27:01Limitations and considerations
- 27:44Closing
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