PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH.
PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH.
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DOI：
10.1214/16-aoas908
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Minin VN
中科院分区：
文献类型：
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作者：
Koepke AA;Longini IM Jr;Halloran ME;Wakefield J;Minin VN
尽管孟加拉国有季节性霍乱疫情，但人们对环境条件与霍乱病例之间的关系知之甚少。我们寻求建立一个基于环境预测因子的孟加拉国霍乱疫情预测模型。要做到这一点，我们估计环境变量，如水深和水温，霍乱疫情的背景下，疾病传播模型的贡献。我们实现了一种方法，同时占疾病动力学和环境变量的易感-感染-感染-易感（SIRS）模型。整个系统被视为一个连续时间隐马尔可夫模型，其中隐马尔可夫状态是在每个时间点上易感、感染或康复的人数，观察状态是报告的霍乱病例数。我们使用贝叶斯框架来拟合这个隐藏的SIRS模型，实施粒子马尔可夫链蒙特卡罗方法从环境和传输参数的后验分布中采样，给出了观测数据。我们测试这种方法使用模拟和数据从Mathbaria，孟加拉国。参数估计用于进行短期预测，捕捉流行病高峰的形成和下降。我们证明，我们的模型可以成功地预测在观察到的霍乱病例数增加之前几周内人口中感染者数量的增加，这可以使流行病的早期通知和及时分配资源。
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.