EpiMoRPH: A simulation environment for generating spatially-refined intervention strategies for the control of infectious disease

EpiMoRPH：用于生成控制传染病的空间精细干预策略的模拟环境

• 批准号: 10599966
• 负责人: Joseph Mihaljevic
• 金额: $ 70.31万
• 依托单位: NORTHERN ARIZONA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10599966
• 关键词: 2019-nCoV; Address; Adopted; Algorithms; Authorization documentation; Automation; Automobile Driving; Benchmarking; COVID-19; COVID-19 pandemic; Case Study; Case/Control Studies; Cities; Collaborations; Communicable Diseases; Communication; Communities; Community Health; Complement; Consensus; County; Data; Data Set; Decision Making; Decision Support Systems; Disease Outcome; Disease model; Elements; Emerging Communicable Diseases; Ensure; Environment; Epidemic; Epidemiology; Equilibrium; Equity; Ethnic Origin; Evaluation; Face; Goals; Head; Health Planning; Informatics; Insecticides; Intervention; Life; Methodology; Methods; Modeling; Motivation; Municipalities; Performance; Positioning Attribute; Procedures; Process; Public Health; Public Health Practice; Race; Refit; Research; Research Personnel; Resources; Specific qualifier value; St. Louis Encephalitis Virus; Standardization; Structure; Study models; System; Technology; Testing; Time; Training; US State; Uncertainty; Vaccination; Validation; authority; crowdsourcing; cyber infrastructure; design; disorder control; epidemiological model; experience; infectious disease model; informatics infrastructure; informatics tool; innovation; interactive tool; intervention refinement; mathematical model; meetings; model development; next generation; pandemic disease; participant interview; pathogen; prototype; real world application; response; simulation environment; socioeconomics; spatial epidemiology; technological innovation; theories; tool; transmission process; usability

Project Summary The recent SARS-CoV-2 pandemic has highlighted that mathematical modeling of infectious disease is critical for data-informed decision making. At the same time, however, it has been made clear that the modeling community does not have appropriately advanced informatics infrastructures that facilitate a rapid consensus understanding during epidemics and that put the power of modeling in the hands of local public health stakeholders. This project proposes three integrated elements to transform the workflow of constructing, testing, and crowd-sourcing spatial epidemiological models to gain deep understanding of epidemics, to provide usable decision-making tools for local stakeholders, and to propose concrete, locally focused solutions. Our proposal is to develop a proof-of-concept, collaborative informatics framework for model construction, analysis and comparison, followed by rigorous optimization of spatial intervention strategies. In Aim 1, we design EpiMoRPH (Epidemiological Modeling Resources for Public Health), a system that will streamline and automate the construction and testing of spatial models against benchmark data. EpiMoRPH will support rapid model comparisons in a community-driven environment to build consensus and to produce a broad understanding of which modeling approaches are most appropriate in different spatial contexts. Importantly, EpiMoRPH will assist local public health stakeholders with deciding on the best, community-contributed models that are relevant for their particular situations and will then implement those best models to make locally customized forecasts. In Aim 2, we make advances in the automation of spatial and robust optimization algorithms, with the goal of allowing non-expert users to generate tailor-made intervention strategies relevant to their local municipalities. Here, we will develop a tool kit of robust optimization algorithms that account for various uncertainties and that will gradually build upon the functionality of EpiMoRPH. Importantly, a driving motivation for this tool kit is to ensure that the optimization routines allow public health stakeholders to balance the control of transmission and disease outcomes with the equitable allocation of interventions across racial, ethnic, and socio-economic sectors. In Aim 3, we will collaborate with a Public Health Advisory Council to test, formally evaluate, and refine our model-based technologies, ensuring that our innovations meet the needs of public health partners, while also appealing to the broader community of epidemiological modelers. Together our aims will build accessible and sustainable technologies that put epidemiological modeling and optimization methods in the hands of local public health decision-makers.

项目概要 最近的 SARS-CoV-2 大流行凸显了传染病的数学模型至关重要 用于基于数据的决策。但与此同时，模型也明确表明： 社区没有适当先进的信息学基础设施来促进快速达成共识 流行病期间的理解，并将建模的力量交给当地公共卫生部门 利益相关者。该项目提出了三个集成元素来改变构建、测试、 众包空间流行病学模型，深入了解流行病，提供可用的 为当地利益相关者提供决策工具，并提出具体的、以当地为重点的解决方案。我们的建议是 开发一个概念验证、协作信息学框架，用于模型构建、分析和 比较，然后严格优化空间干预策略。在目标 1 中，我们设计 EpiMoRPH （公共卫生流行病学建模资源），该系统将简化和自动化 根据基准数据构建和测试空间模型。 EpiMoRPH将支持快速模型 在社区驱动的环境中进行比较，以建立共识并产生广泛的理解 哪些建模方法最适合不同的空间环境。重要的是，EpiMoRPH 将提供帮助 当地公共卫生利益相关者决定与社区相关的最佳社区贡献模型 他们的具体情况，然后将实施这些最佳模型来进行本地定制的预测。在 目标 2，我们在空间自动化和鲁棒优化算法方面取得进展，目标是 允许非专家用户制定与其当地市政当局相关的量身定制的干预策略。 在这里，我们将开发一个强大的优化算法工具包，该算法考虑了各种不确定性，并且 将逐渐建立在 EpiMoRPH 的功能之上。重要的是，该工具包的驱动动机是 确保优化例程允许公共卫生利益相关者平衡传播控制和 跨种族、民族和社会经济公平分配干预措施的疾病结果 部门。在目标 3 中，我们将与公共卫生咨询委员会合作进行测试、正式评估和完善 我们基于模型的技术，确保我们的创新满足公共卫生合作伙伴的需求，同时 也吸引了更广泛的流行病学建模者社区。我们的目标将共同打造无障碍 和可持续技术，将流行病学建模和优化方法交给当地 公共卫生决策者。