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Activity Number: 526
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #305963
Title: Predicting Cholera Cases from Environmental Variables Using Sequential Monte Carlo Methods
Author(s): Amanda Allen*+ and Vladimir N Minin and Ira Longini and Jon Wakefield and Elizabeth Halloran
Companies: University of Washington and University of Washington and Emerging Pathogens Institute and University of Washington and University of Washington
Address: Dept. of Statistics, Box 354322, Seattle, WA, 98195, United States
Keywords: cholera ; Sequential Imputation ; Markov chain Monte Carlo

Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on lagged environmental predictors. To do this, we must estimate the environmental parameters in the context of a transmission model. We develop a method to simultaneously estimate the transmission parameters and the environmental parameters in an SIRS model. The entire system is treated as a partially observed Markov process model, where the unobserved Markov states are the number of people who are susceptible, infected, and recovered at each time point, and the observed states are the number of cholera cases reported. We use Bayesian particle Markov chain Monte Carlo (MCMC) methods to sequentially estimate the missing state vectors. In this way, we can estimate the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulated data and data from a particular pond in Bakerganj.

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