PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH

• 成果类型: Article
• 署名作者: Koepke, Amanda A.; Longini, Ira M., Jr.; Halloran, M. Elizabeth; Wakefield, Jon; Minin, Vladimir N.
• 署名单位: Fred Hutchinson Cancer Center; State University System of Florida; University of Florida; State University System of Florida; University of Florida; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS908
• 发表日期: 2016
• 页码: 575-595
• 关键词: multiple transmission pathways; vibrio-cholerae; disease dynamics; stochastic simulation; epidemic models; inference; computation; environment; measles; TRENDS

摘要

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 chainMonte 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.