Sequential Bayesian Inference for Detection and Response to Seasonal Epidemics

Authors

  • Michael Ludkovski Statistics and Applied Probability, UC Santa Barbara
  • Junjing Lin Statistics and Applied Probability, UC Santa Barbara

DOI:

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.

Author Biography

Michael Ludkovski, Statistics and Applied Probability, UC Santa Barbara

Mike Ludkovski is an Associate Professor in the Department of Statistics and Applied Probabilty at UC Santa Barbara. He received a PhD in Operations Research and Financial Engineering from Princeton in 2005 and was previously a term assistant professor at University of Michigan. His research interests are in stochastic computational control and applied probability with applications ranging from stochastic games in resource management to sequential change detection and control in biosurveillance models.

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Published

2013-03-24

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

Issue

Section

Oral Presentations: Analytical Methods - Bayesian