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

项目摘要 最近的SARS-CoV-2大流行凸显了传染病的数学建模至关重要 进行基于数据的决策。然而，与此同时，已经清楚地表明， 社区没有适当先进的信息学基础设施，以促进快速共识 在流行病期间的理解，并将建模的权力交给当地公共卫生部门， 持份者该项目提出了三个综合要素，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， 为当地利益攸关方提供决策工具，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，我们设计了EpiMoRPH （公共卫生流行病学建模资源），一个将简化和自动化 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， 哪种建模方法最适合不同的空间背景。重要的是，EpiMoRPH将协助 当地公共卫生利益攸关方决定最佳的，社区贡献的模式， 他们的具体情况，然后将实施这些最好的模型，使当地定制的预测。在 目标2，我们在空间和鲁棒优化算法的自动化方面取得进展，目标是 允许非专家用户制定与当地市政当局相关的定制干预战略。 在这里，我们将开发一个强大的优化算法的工具包，这些算法考虑了各种不确定性， 将逐步建立在EpiMoRPH的功能基础上。重要的是，该工具包的驱动动机是 确保优化程序允许公共卫生利益相关者平衡传播控制， 在不同种族、民族和社会经济条件下公平分配干预措施， 板块在目标3中，我们将与公共卫生咨询理事会合作，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， 也吸引了更广泛的流行病学建模者。我们的目标将共同建立无障碍 和可持续技术，将流行病学建模和优化方法交给当地人， 公共卫生决策者。

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.