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June 2016 Predictive modeling of cholera outbreaks in Bangladesh
Amanda A. Koepke, Ira M. Longini, Jr., M. Elizabeth Halloran, Jon Wakefield, Vladimir N. Minin
Ann. Appl. Stat. 10(2): 575-595 (June 2016). DOI: 10.1214/16-AOAS908

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 estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS 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 simulation and data from Mathbaria, 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.

Citation

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Amanda A. Koepke. Ira M. Longini, Jr.. M. Elizabeth Halloran. Jon Wakefield. Vladimir N. Minin. "Predictive modeling of cholera outbreaks in Bangladesh." Ann. Appl. Stat. 10 (2) 575 - 595, June 2016. https://doi.org/10.1214/16-AOAS908

Information

Received: 1 November 2014; Revised: 1 December 2015; Published: June 2016
First available in Project Euclid: 22 July 2016

zbMATH: 06625661
MathSciNet: MR3528352
Digital Object Identifier: 10.1214/16-AOAS908

Rights: Copyright © 2016 Institute of Mathematical Statistics

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Vol.10 • No. 2 • June 2016
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