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.

项目摘要 最近的SARS-COV-2大流行表明，传染病的数学建模至关重要 用于数据信息决策。但是，同时，已经明确表明了建模 社区没有适当的高级信息学基础架构来促进快速共识 在流行期间的理解，这使建模的力量掌握在当地公共卫生的手中 利益相关者。该项目提出了三个集成元素，以改变构建，测试， 和众包空间流行病学模型，以深入了解流行病，以提供可用的 针对本地利益相关者的决策工具，并提出具体的，以本地为重点的解决方案。我们的建议是 为了开发概念验证，用于模型构建，分析和的协作信息学框架 比较，然后对空间干预策略进行严格的优化。在AIM 1中，我们设计了表达式 （公共卫生的流行病学建模资源），该系统将简化和自动化 针对基准数据的空间模型的构建和测试。表达式将支持快速模型 在社区驱动的环境中进行比较，以建立共识并对 在不同的空间环境中，哪种建模方法最合适。重要的是，epimorph将有助于 当地的公共卫生利益相关者决定了与社区最佳的模型相关的最佳模型 他们的特殊情况，然后将实施这些最佳模型以进行本地定制的预测。在 AIM 2，我们在空间和健壮优化算法的自动化方面取得了进步，目的是 允许非专家用户生成与当地市政当局相关的量身定制的干预策略。 在这里，我们将开发一个具有强大优化算法的工具套件，以解释各种不确定性，并且 将逐渐建立在epimorph的功能上。重要的是，该工具套件的驾驶动机是 确保优化程序允许公共卫生利益相关者平衡传播的控制和 疾病的结果是在种族，种族和社会经济上公平分配干预措施 部门。在AIM 3中，我们将与公共卫生咨询委员会合作测试，正式评估和完善 我们的基于模型的技术，确保我们的创新满足公共卫生合作伙伴的需求，而 还吸引了更广泛的流行病学建模社区。我们的目标共同建立可访问的 以及将流行病学建模和优化方法掌握在本地的可持续技术 公共卫生决策者。