Spatio-Temporal Modeling for Surveillance Data of Multiple Pathogens

 描述（由申请人提供）：传染病的监测数据通常是研究新发疾病流行病学的第一个可用来源，对监测数据进行适当分析可以提供有关传染性和控制策略的宝贵见解。然而，适当的分析应解决一些挑战。(1)可能存在多个传输路由，例如，从环境水库到人类和从人类到人类，每一条路径都有其自身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引起同一种疾病，但只有一小部分病例被采样以确定责任病原体;（3）一个复杂的系统，包括免疫、交叉免疫、未观察到的无症状感染，以及人口的出生和老龄化过程，可能塑造了人口统计学，监测数据的时空结构。基于中国对由肠道病毒（EV）家族引起的手足口病（HFMD）的监测数据，我们建议通过以下具体目标应对上述挑战：（1）建立和测试共循环病原体监测数据的贝叶斯建模框架，以评估病原体特定环境对人类和人类对人类的传播能力和相关风险因素，将该模型应用于中国手足口病监测数据，对EV 71和科萨基A16两种主要肠道病毒的传播性及其危险因素的影响进行了估计;（2）将特定目标1中的建模框架扩展到无症状感染、既往暴露的免疫和交叉免疫以及人群的出生、衰老和死亡;利用此扩展模型来解释手足口病流行的长期演变，预测EV 71疫苗接种计划的有效性，并研究EV 71被其他肠道病毒替代的可能性。我们的初步模拟研究的简化模型表明，同时估计的传输率，空间效应和时间效应的可行性与适度数量的地理单元和少量的实验室验证。一旦模型框架在模拟研究中得到验证，我们将使其适应多年的手足口病流行的监测数据，以检测HMFD相关病原体的未知特征，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疫苗接种计划的建议。拟议的分析框架可以推广到广泛的急性传染病，包括流感，腺病毒，霍乱和登革热，适当的定制。

 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.