The Planetary Child Health Observatory: an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

行星儿童健康观察站：一项跨学科研究计划和基于网络的仪表板，用于绘制中低收入国家肠道传染病及其危险因素和干预措施

• 批准号: 10591991
• 负责人: Josh M Colston
• 金额: $ 13.04万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-20 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591991
• 关键词: Adult; Age; Area; Bacteria; Big Data; Biological Process; Biometry; COVID-19; Campylobacter; Cessation of life; Child; Child Health; Childhood; Cholera; Climate; Communicable Diseases; Communities; Country; Cryptosporidium; Data; Data Set; Databases; Development; Development Plans; Diagnosis; Diagnostic; Differential Diagnosis; Disease; Disease Outcome; Enteral; Epidemiologist; Etiology; Exclusion; Funding; Geographic Distribution; Geography; Goals; HIV; Health; Health Information System; Household; Interdisciplinary Study; International; Intervention; Lower Respiratory Tract Infection; Malaria; Maps; Marketing; Mentors; Mentorship; Methodology; Methods; Modeling; Modernization; Molecular; Monitor; National Institute of Allergy and Infectious Disease; Norovirus; Online Systems; Outcome; Perception; Personal Satisfaction; Planet Earth; Population; Population Intervention; Populations at Risk; Prevalence; Process; Public Health; Publications; Reporting; Research; Research Activity; Research Personnel; Resolution; Resources; Risk; Risk Factors; Sampling Studies; Secure; Shigella; Source; Standardization; Surveys; Target Populations; Testing; Time; Training; United States National Aeronautics and Space Administration; Universities; Update; Vaccines; Variant; Viral; age group; burden of illness; career development; central database; climate change; comparative; dashboard; diagnostic technologies; disability-adjusted life years; economic outcome; enteric pathogen; evidence base; improved; inter-institutional; large datasets; low and middle-income countries; molecular diagnostics; novel; novel vaccines; pathogen; peer; peer learning; priority pathogen; public repository; remote sensing; skills; sociodemographic factors; sociodemographics; spatiotemporal; stool sample; success; surveillance study; tool; transmission process; trend; vaccine candidate; web page; web portal; years of life lost

PROJECT SUMMARY/ABSTRACT Mapping spatiotemporal variation in the burden of infectious diseases is critical for targeting interventions like vaccines and prioritizing at-risk populations, particularly in Low- and Middle-Income Countries (LMICs), where such diseases are most prevalent. While many priority pathogens have been mapped over entire endemic regions, enteric infectious diseases (EIDs) have not, due to a perception that the necessary spatially referenced data on their prevalence and environmental determinants are not available. However, improvements in differential diagnosis of EIDs, geostatistical methods, and accessibility and accuracy of environmental data mean that it is now possible to carry out such a mapping. The long-term goal of this proposal is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The overall objective is to apply a big data approach to the modeling of EIDs in combination with advanced geostatistical analyses and global earth observation (EO)- derived datasets, resulting in generalizable estimates of the geographical distribution of these outcomes and of their associations with environmental drivers disseminated via an interactive web-based dashboard. The central hypothesis is that the prevalence of many enteropathogens varies spatiotemporally as a function of climatic, environmental, and socio-demographic factors in a way that can be modelled using global EO datasets and similar products. The rationale underlying the proposed research is that it will enable the identification of target populations for interventions. Specifically, building on existing partnerships between epidemiologists, climatologists, and hydrologists as well as investigators in numerous LMICs, Dr. Colston will: 1) compile and maintain a large database of georeferenced results from studies that diagnosed EIDs in children in LMICs; 2) Apply geostatistical models to EID outcome data (aim 1) and spatiotemporally matched, high resolution environmental covariates to a). draw inferences about underlying biological processes and b). generate prediction maps to identify geographical foci of transmission risk. 3) Establish a Planetary Child Health Observatory (PCHO), an interinstitutional initiative consisting of a). an interactive web-based dashboard and b). an international consortium of investigators. In addition to these research activities, Dr. Colston proposes a career development plan that includes mentorship, experiential and peer-to-peer learning, coursework, publications, and presentations with the objectives of: 1) gaining skills and formal training in geostatistical inference, biostatistics, and large datasets; 2) expanding expertise in applications of environmental and remote sensing-derived datasets in health research; 3) transitioning to research independence by securing follow-on R01 funding. His proposal will be supervised by an outstanding, interdisciplinary mentoring team with complementary methodological and substantive skills. The project will have a positive impact on public health by providing data inputs urgently needed for targeting EID interventions to priority populations in LMICs.

项目总结/摘要 绘制传染病负担的时空变化图对于有针对性的干预措施至关重要， 疫苗和优先考虑高危人群，特别是在中低收入国家， 这类疾病最为普遍。虽然许多优先病原体已在整个地方病 地区，肠道传染病（EID）没有，由于必要的空间参考的看法， 目前尚无有关其流行率和环境决定因素的数据。然而， EID的鉴别诊断、地质统计学方法以及环境数据平均值的可获得性和准确性 现在可以进行这样的映射。这项提案的长期目标是提供研究 和利益相关者社区，为肠道病原体特异性 儿童健康干预措施，如新型疫苗。总体目标是将大数据方法应用于 结合先进的地质统计学分析和全球地球观测（EO）建立EID模型- 衍生数据集，从而对这些成果的地理分布和 他们与环境驱动因素之间的联系通过一个互动式网络仪表板传播。中央 假设是许多肠道病原体的流行作为气候的函数在时空上变化， 环境和社会人口因素，可以使用全球EO数据集进行建模， 同类产品拟议研究的基本原理是，它将能够确定目标 人口进行干预。具体而言，在流行病学家之间现有伙伴关系的基础上， 气候学家，水文学家以及许多LMIC的调查人员，Colston博士将：1）汇编和 维护一个大型数据库，其中包含诊断中低收入国家儿童EID的研究的地理参考结果; 2） 将地质统计模型应用于EID结果数据（目标1），并进行时空匹配，高分辨率 环境协变量为a）。推断潜在的生物学过程和B）。生成 预测地图，以确定传播风险的地理集中点。3)建立行星儿童健康 观察站，这是一项机构间举措，包括：基于网络的交互式仪表板，以及B）. 一个国际调查联盟除了这些研究活动外，Colston博士还提出了一项 职业发展计划，包括指导，经验和同行学习，课程， 出版物和演示文稿，目的是：1）获得地统计方面的技能和正式培训 推理，生物统计学和大型数据集; 2）扩大环境和远程应用的专业知识 健康研究中的感知衍生数据集; 3）通过确保后续研究过渡到研究独立性 R 01融资他的建议将由一个杰出的跨学科指导团队监督， 补充方法和实质性技能。该项目将对公共卫生产生积极影响， 提供迫切需要的数据投入，以便针对中低收入国家的优先人口采取紧急免疫干预措施。