Spatio-Temporal Modeling for Surveillance Data of Multiple Pathogens

 描述(申请人提供)：传染病监测数据通常是研究新出现疾病流行病学的第一个可用来源，对监测数据的适当分析可以为传播性和控制策略提供有价值的见解。然而，适当的分析应该解决一些挑战。(1)可能有多种传播途径，例如，从环境宿主到人和从人到人，每条途径都有自己的时空异质性和相关性；(2)可能有多种类型的病原体共同循环并导致相同的疾病，但只对一小部分病例进行抽样以确定责任病原体；(3)包括免疫、交叉免疫、未观察到的无症状感染在内的复杂系统，加上人口的出生和老龄化过程，可能形成了监测数据的人口、空间和时间结构。受中国手足口病监测数据的启发，我们建议通过以下具体目标来应对上述挑战：(1)建立和测试共循环病原体监测数据的贝叶斯建模框架，以评估病原体在特定环境中人与人之间的传播性和相关危险因素，同时考虑传播性的时空异质性和相关性；将该模型应用于中国手足口病监测数据，估计两种主要肠道病毒EV71和柯萨奇A16的风险因素的传播性和影响；以及(2)在特定目标1中扩展建模框架，包括无症状感染、免疫和交叉免疫，以及人口的出生、衰老和死亡；使用这个扩展的模型来解释手足口病流行的长期演变，预测EV71疫苗接种计划的有效性，并研究其他肠道病毒取代EV71的可能性。我们对一个简化模型的初步模拟研究表明，在适度数量的地理单元和少量的实验室验证的情况下，同时估计传输速率、空间效应和时间效应是可行的。一旦模型框架在模拟研究中得到验证，我们将使其适应手足口病疫情多年的监测数据，以检测与HMFD相关的病原体的未知特征，特别是无症状感染和交叉免疫，并向公共卫生政策制定者提出关于疫苗接种计划的建议。建议的分析框架可在适当定制的情况下推广到包括流感、腺病毒、霍乱和登革热在内的各种急性传染病。

 DESCRIPTION (provided by applicant): Surveillance data of infectious diseases often constitute the first available source for studying the epidemiology of emerging diseases, and proper analysis of surveillance data can provide valuable insights on transmissibility and control strategies. However, proper analysis should address a few challenges. (1) there may be multiple transmission routes, e.g., from environmental reservoir to human and from human to human, and each route has its own spatio-temporal heterogeneity and correlation; (2) there may be multiple types of pathogens co- circulating and causing the same disease, but only a small proportion of cases are sampled to identify the responsible pathogen; and (3) a complex system including immunity, cross-immunity, unobserved asymptomatic infections, together with the birth and aging process of the population may have shaped the demographic, spatial and temporal structure of the surveillance data. Motivated by the Chinese surveillance data of the hand, foot and mouth disease (HFMD) that is caused by a family of enteroviruses (EV), we propose to address above challenges via the following Specific Aims: (1) To build and test a Bayesian modeling framework for surveillance data of co-circulating pathogens to assess pathogen-specific environment-to-human and human-to-human transmissibility and associated risk factors, while accounting for spatio-temporal heterogeneity and correlation in transmissibility; with this model applied to the surveillance data of HFMD in China, to estimate the transmissibility and effects of risk factors for two major enteroviruses, EV 71 and Coxsackie A16; and (2) To extend the modeling framework in Specific Aim 1 with asymptomatic infection, immunity and cross-immunity from previous exposure, and birth, aging and death of the population; to use this extended model to explain the long-term evolvement of the HFMD epidemics, to predict the effectiveness of EV 71 vaccination programs, and to investigate possible replacement of EV 71 by other enteroviruses. Our preliminary simulation study on a simplified model showed feasibility of simultaneous estimation of transmission rates, spatial effects and temporal effects with a moderate number of geographic units and a small amount of laboratory validation. Once the modeling frame work is validated in simulation studies, we will adapt it to multiple years of surveillance data of the HFMD epidemics to detect unknown features about the HMFD-related pathogens, in particular asymptomatic infection and cross-immunity, and to make recommendations about vaccination programs to public health policy-makers. The proposed analytic framework may be generalized, with appropriate customization, to a broad range of acute infectious diseases including influenza, adenovirus, cholera and dengue.