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

引起COVID-19的病毒SARS-CoV-2已经成为一种全球流行病，在我国和地球仪的社区迅速蔓延。准确预测疾病传播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将在医院，以及每家医院的资源需求（例如消毒器）。到目前为止，大多数关于SARS-CoV-2的建模研究都集中在预测病毒如何在人口稠密的城市（如纽约市）传播，但该病毒也在人口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，这些模型可以代表被大面积低人口密度地区隔开的中型人口中心。该项目将开发和完善这些更先进的数学模型，以了解人口密度和相互联系如何影响流行病爆发的轨迹。重要的是，该项目将通过地方和区域应急规划人员可访问的安全门户网站，近实时部署模型预测，以协助地方公共卫生干预措施。模型输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。大多数用于预测SARS-CoV-2传播的流行病学模型所做的假设可能只在人口密集的城市中心才是现实的，例如依赖频率的传播动力学，缺乏随机过程，缺乏密度依赖效应。在这个项目中，将开发更适合稀疏集合种群的模型，考虑多个随机性来源，人口在景观中的移动，密度依赖的传输，以及传输率的空间异质性。该项目将使用先进的统计计算算法，使模型与来自大型农村地区的数据相匹配。这一拟合程序将确定描述病例计数纵向模式的最合适的传播函数，并使该项目能够近实时地发布SARS-CoV-2传播的数据预测。这些模型预测将与公共卫生专家和医疗保健从业人员合作开发，以满足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粒度的住院率预测，以便已经紧张的农村医院系统可以适应和规划不同的干预方案。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.