RAPID: Real-time modeling of the source-sink dynamics of SARS-CoV-2 in rural regions for equitable public health

导致新冠肺炎的SARS-CoV-2病毒已经成为一种全球流行病，通过我国和全球各地的社区迅速传播。对疾病传播的准确预测是有效规划疫情应对的关键工具，使我们能够预测有多少人将被感染，在任何给定时间有多少患者在医院，以及每家医院的资源需求(如呼吸机)。到目前为止，大多数对SARS-CoV-2的建模研究都集中在预测病毒将如何在纽约市等人口稠密的城市传播，但病毒也在更稀少的农村地区迅速传播。为了准确地模拟更多农村地区的疫情动态，需要更现实的数学模型来表示被大片低人口密度地区隔开的中型人口中心。该项目将开发和改进这些更高级的数学模型，以了解人口密度和相互关联性如何影响疫情爆发的轨迹。重要的是，该项目将通过当地和区域应对规划者可访问的安全门户网站，近乎实时地部署模型预测，以帮助地方公共卫生干预。模型产出还将与广大农村地区特定地方和区域医院的可用资源挂钩，以便这些医院能够更好地管理SARS-CoV-2疫情的影响。建模系统和网络应用程序将作为快速应对未来疫情的框架。大多数已开发的用于预测SARS-CoV-2传播的流行病学模型都做出了可能只在人口稠密的城市中心才现实的假设，如频率依赖的传播动力学、缺乏随机过程和缺乏密度依赖的效应。在这个项目中，将开发更适合于稀疏集合种群的模型，考虑到随机性的多个来源、跨景观的人口流动、依赖密度的传播以及传播速率的空间异质性。该项目将使用先进的统计计算算法，将模型与来自大片农村地区的数据进行匹配。这一拟合例程将确定描述病例计数纵向模式的最合适的传输函数，并将允许该项目近乎实时地发布基于数据的SARS-CoV-2传播预测。这些模型预测将与公共卫生专家和医疗从业者合作制定，以应对服务不足的农村社区的需求。安全的网络应用程序将提供精细空间颗粒的住院率预测，以便已经紧张的农村医院系统可以在不同的干预情景下适应和规划。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

SARS-CoV-2, the virus that causes COVID-19, has become a global epidemic, spreading rapidly through communities across our country and around the globe. Accurate forecasting of disease spread is a critical tool for effectively planning outbreak responses, allowing us to predict how many people will be infected, how many patients will be in hospitals at any given time, and the resource needs (e.g. ventilators) at each hospital. Most modeling studies of SARS-CoV-2 have so far focused on predicting how the virus will spread in densely populated cities like New York City, but the virus is also rapidly spreading in more sparsely populated, rural regions. To accurately model outbreak dynamics in more rural areas, more realistic mathematical models are needed that can represent mid-sized population centers separated by large areas of low population density. This project will develop and refine these more advanced mathematical models to understand how population density and interconnectedness influence the trajectory of epidemic outbreaks. Importantly, the project will deploy model projections in near-real-time to aid in local public health interventions, via a secure web portal accessible to local and regional response planners. Model output will also be linked to the resources available at specific local and regional hospitals in a broad rural area, so that these hospitals can better manage the impacts of the SARS-CoV-2 epidemic. The modeling system and web application will serve as a framework for rapidly responding to future outbreaks. Most epidemiological models that have been developed to forecast the spread of SARS-CoV-2 make assumptions that are likely only realistic in dense urban centers, such as frequency-dependent transmission dynamics, a lack of stochastic processes, and a lack of density-dependent effects. In this project, models will be developed that are more appropriate for sparse metapopulations, accounting for multiple sources of stochasticity, population movement across a landscape, density-dependent transmission, and spatial heterogeneity in transmission rates. The project will use advanced statistical computing algorithms to fit the models to data from a large rural region. This fitting routine will identify the most appropriate transmission functions that describe longitudinal patterns of case-counts and will allow the project to release data-informed predictions of SARS-CoV-2 spread in near-real-time. These model forecasts will be developed in collaboration with public health experts and healthcare practitioners to respond to the needs of under-served, rural communities. The secure web application will provide predictions of hospitalization rates at fine spatial grains so that already strained, rural hospital systems can adapt and plan under different intervention scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.