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Activity Number: 513
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312179 View Presentation
Title: Predictive Modeling of Cholera Outbreaks in Bangladesh
Author(s): Amanda Koepke*+ and Ira Longini and M. Elizabeth Halloran and Jon Wakefield and Vladimir Minin
Companies: University of Washington and University of Florida and Fred Hutchinson Cancer Research Center and University of Washington and University of Washington
Keywords: Bayesian ; Sequential Monte Carlo ; Time series ; Hidden Markov Model ; Infectious diseases
Abstract:

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 environmental predictors. To do this, we implement a method which simultaneously accounts for disease dynamics and environmental variables in a compartmental model of disease transmission. The entire system is treated as a continuous-time hidden Markov model. We use a Bayesian framework to fit this hidden compartmental model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulated data and data from Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources.


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