Spatio-Temporal Modeling for Surveillance Data of Multiple Pathogens

多种病原体监测数据的时空建模

• 批准号: 8950460
• 负责人: Yang Yang
• 金额: $ 24.81万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-16 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8950460
• 关键词: Accounting; Acute; Address; Adenoviruses; Age; Age Distribution; Aging; Aging-Related Process; Animals; Bayesian Modeling; Birth; Camels; Cessation of life; Child; China; Chinese People; Cholera; Chronic Disease; Clinical; Communicable Diseases; Complex; Data; Decision Making; Dengue; Diagnosis; Disease; Effectiveness; Effectiveness of Interventions; Enterovirus; Enterovirus 71; Environment; Epidemic; Exhibits; Family; Future; Hand, Foot and Mouth Disease; Health Policy; Heterogeneity; Human; Immunity; Incidence; Infection; Influenza; Influenza A Virus, H1N1 Subtype; Influenza A Virus, H3N2 Subtype; Influenza A virus; Influenza B Virus; Internet; Joints; Laboratories; Light; Middle East Respiratory Syndrome Coronavirus; Modeling; Movement; Outcome; Pathogenicity; Pattern; Performance; Persons; Policy Maker; Population; Public Health; Recommendation; Recording of previous events; Risk Factors; Route; Sampling; Schools; Severities; Severity of illness; Shapes; Source; Spatial Distribution; Specimen; Statistical Models; Stratification; Structure; Symptoms; System; Technology; Test Result; Testing; Time; Vaccination; Vaccines; Validation; Work; age group; base; cross immunity; disease transmission; epidemiology study; exhaustion; improved; insight; intervention program; pathogen; preference; programs; public health relevance; simulation; surveillance data; transmission process; trend

 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.

 描述（由适用提供）：传染病的监视数据通常构成研究新兴疾病流行病学的第一个可用来源，并且对监视数据的正确分析可以提供有关传播和控制策略的宝贵见解。但是，适当的分析应解决一些挑战。 （1）从人类到人类到人类，可能有多个传输途径，例如，从环境储存库到人类，并且每条路线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相同的疾病，但只有一小部分病例被采样以识别负责的病原体； （3）一个复杂的系统，包括免疫学，跨免疫力，未观察到的不对称感染以及人口的出生和衰老过程，可能塑造了监视数据的人口统计学，空间和临时结构。由中国脚和口腔疾病（HFMD）的中国监视数据激发，该数据是由肠病毒家族（EV）引起的，我们建议通过以下特定目的来应对上述挑战：（1）构建和测试贝叶斯建模框架，以构建贝叶斯建模框架，以构建对病原体和培养病原体和人类和人类和人类相关的病原体的监视数据，并传播的时空异质性和相关性；该模型应用于中国HFMD的监视数据，以估算两个主要肠病毒的危险因素的传播和影响，即EV 71和Coxsackie A16； （2）将建模框架扩展到特定的目标1中，以不对称的感染，以前的暴露，免疫组织化学和交叉免疫性以及人口的出生，衰老和死亡；使用此扩展模型来解释HFMD流行病的长期演变，以预测EV 71疫苗接种计划的有效性，并研究其他肠病毒对EV 71的替代。我们对简化模型的初步仿真研究表明，对传输速率，空间效应和临时效应的简单估计具有现代数量的地理单位以及少量实验室验证的可行性。一旦在模拟研究中验证了建模框架的工作，我们将其调整为HFMD发作的多年监视数据，以检测有关HMFD相关病原体的未知特征，特别是非对称性感染和交叉免疫，并就公共卫生保险公司制定者的疫苗接种计划提出建议。拟议的分析框架可以通过适当的自定义概括为广泛的急性传染病，包括影响力，腺病毒，霍乱和龙舌兰。