EAGER: Collaborative Research: Rapid Production of Geospatial Network Inputs for Spatially Explicit Epidemiological Modeling of COVID-19 in the USA

疾病传播的动态计算机模型用于了解和预测传染病如何通过宿主人群传播。人口分布、流动性和旅行走廊的地图是许多这些模型的关键组成部分。然而，准确地确定人的空间分布是困难的，因为大多数数据源（例如，人口普查）只能大致显示人们居住的地方，而不是他们工作和去的地方。特别是，人口普查数据的汇总方式也只在人口密集的城市地区提供精细的空间细节。在郊区和农村地区，人口普查地图只提供居住在每个人口普查单位的总人数（例如，美国的一个县），但不显示人们生活和工作的地方。这项研究将把城市、城镇和旅游走廊发出的夜间灯光的详细卫星图像与人口普查和流动数据融合在一起，为流行病学家提供更详细的人口地图，以更准确地模拟COVID-19等传染病的传播。拟议的合作将汇集来自地球空间动力学和遥感与疾病生态学和流行病学的专业知识，以产生具有推动这两个领域的潜力的跨越边界的科学。此外，拟议的项目将支持两个早期的职业科学家以及本科生参与研究。当航空和汽车旅行显着减少，人口流动和空间连通性的准确性和细节假设更重要的建模流行病传播。通过对地理空间数据进行共同分析而得出的空间网络（根据遥感夜灯得出的定居点和基础设施密度以及根据人口普查得出的人口密度）可以提供比行政单位更准确的空间域（例如，县）用于汇总和分析健康数据。此外，这些空间网络的结构和连通性可以用来量化影响疾病传播的网络结构的基本参数。这项研究将开发一套逐步完善的网络图，用于流行病学模型。由地球科学家、疾病生态学家和流行病学家组成的研究小组将开发一个标准化的协议，其中包括用于制作这些地图的分析程序和工具，以便适合美国SARS-CoV-2感染的定量时空分析，包括对纽约和洛杉矶都市区的详细分析。人口中心之间的网络流参数将使用基于代理的建模，建立一个完整的地理空间网络，包括城市内的人口和流动性约束，以及城市之间的人口流量。人口和网络流量估计将直接输入空间明确的COVID-19传播模型，并将抽象为边界条件，以简化未来的流行病学模型。该奖项由环境生物学部门的传染病生态学和进化计划颁发，使用冠状病毒援助，救济和经济安全（CARES）法案的资金。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。

Dynamical computer models of disease transmission are used to understand and predict how infectious diseases spread through host populations. Maps of population distribution, mobility, and travel corridors are critical components of many of these models. However, accurately determining the spatial distribution of people is difficult because most sources of data (e.g., census) indicate only approximately where people reside, rather than where they work and go. Census data, in particular, are also aggregated in a way that provides fine spatial detail only in densely populated urban areas. In suburban and rural areas, census maps provide only the total number of people living in each census unit (e.g., a U.S. county), but do not show where people live and work. This research will fuse detailed satellite images of night light emitted from cities, towns and travel corridors with census counts and mobility data to produce more detailed population maps for epidemiologists to use to more accurately simulate the transmission of communicable diseases like COVID-19. The proposed collaboration will bring together expertise from geospatial dynamics and remote sensing with disease ecology and epidemiology to produce boundary spanning science with potential to advance both fields. Further, the proposed project will support two early career scientists as well as undergraduate student involvement in research.When air and vehicle travel are significantly reduced, the accuracy and detail of population movement and spatial connectedness assumes greater importance for modeling epidemic spread. Spatial networks derived from co-analysis of geospatial data (settlement and infrastructure density from remotely sensed night light and population density from census enumerations) can provide more accurate spatial domains than the administrative units (e.g., counties) used to aggregate and analyze health data. In addition, the structure and connectivity of these spatial networks can be used to quantify fundamental parameters of network structure that influence disease spread. This research will develop a progressively refined suite of network maps for use with epidemiological models. The research team, composed of geoscientists, disease ecologists and epidemiologists will develop a standardized protocol with analytic procedures and tools for production of these maps structured so as to be suited for quantitative spatiotemporal analysis of SARS-CoV-2 infections in the U.S., including detailed analyses of the New York and Los Angeles metro areas. Network flow parameters among population centers will be estimated using agent-based modeling, establishing a complete geospatial network consisting of population and mobility constraints within cities, and population fluxes among cities. Population and network flow estimates will be input directly into spatially explicit COVID-19 transmission models, and will be abstracted into boundary conditions that can streamline future epidemiological models. This RAPID award is made by the Ecology and Evolution of Infectious Disease Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.