Sequential Bayesian Inference for Detection and Response to Seasonal Epidemics
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How to Cite

Ludkovski, M., & Lin, J. (2013). Sequential Bayesian Inference for Detection and Response to Seasonal Epidemics. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4570

Abstract

We study sequential Bayesian inference in continuous-time stochastic compartmental models with latent factors. A motivating application of our methods is to modeling of seasonal infectious disease outbreaks, notably influenza. Assuming continuous observation of all the epidemiological transitions, our focus is on joint inference of the unknown transition rates and the dynamic latent states, modeled as a hidden Markov factor. Using insights from nonlinear filtering of jump Markov processes we develop a novel sequential Monte Carlo algorithm for this purpose. Our approach applies the ideas of particle learning to minimize particle degeneracy and exploit the analytical jump Markov structure. We demonstrate inference in such models with several numerical illustrations and also discuss predictive analysis of epidemic countermeasures using sequential Bayes estimates.
https://doi.org/10.5210/ojphi.v5i1.4570
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