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（由申请人提供）：感染学的监视数据通常是对新兴疾病的第一个可用来源，对监视数据的适当分析可以提供有关传播和控制策略的宝贵见解）可能存在暂时性的异质性和相关性；（2）可能会循环并引起相同的疾病M监视数据。人类到人类的可传播性和相关的风险因素，同时考虑到与监视数据的传播相关性的时空异质性和相关性； Coxsackie A16;（2）在特定的目标1中扩展了无症状的感染，免疫力和以前的暴露，衰老和死亡的跨界框架。流行病，以预测EV 71疫苗接种计划的有效性，并通过其他肠病毒来调查EV 71。 HFMD的验证。包括流感，腺病毒，霍乱和登革热在内的急性传染性。