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

EAGER：协作研究：快速生成地理空间网络输入，用于美国 COVID-19 的空间显式流行病学建模

• 批准号: 2032230
• 负责人: Christopher Small
• 金额: $ 4.86万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032230&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Rapid; Production

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.

疾病传播的动态计算机模型用于了解和预测传染病如何在宿主人群中传播。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，准确确定人口的空间分布是困难的，因为大多数数据来源（例如人口普查）只表明人们大约居住的地方，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，只在人口稠密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（例如美国一个县）的总人数，但不显示人们居住和工作的地点。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计数和流动性数据融合在一起，为流行病学家制作更详细的人口地图，以更准确地模拟COVID-19等传染病的传播。拟议的合作将把地理空间动力学和遥感的专门知识与疾病生态学和流行病学结合起来，产生跨越边界的科学，有可能推动这两个领域的发展。此外，拟议的项目将支持两名早期职业科学家以及本科生参与研究。当飞机和车辆旅行显著减少时，人口流动和空间连通性的准确性和细节对于建模流行病传播具有更大的重要性。从地理空间数据（遥感夜光所得的住区和基础设施密度以及人口普查所得的人口密度）共同分析得出的空间网络可以提供比用于汇总和分析卫生数据的行政单位（如县）更准确的空间域。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地图，用于流行病学模型。由地球科学家、疾病生态学家、流行病学家组成的研究小组将制定标准化的方案，其中包括分析程序和工具，以便制作适合于对美国新冠病毒感染情况进行定量时空分析的地图，包括对纽约和洛杉矶大都市地区的详细分析。利用基于智能体的模型估计人口中心之间的网络流动参数，建立由城市内人口和流动约束以及城市间人口流动构成的完整地理空间网络。人口和网络流量估计数将直接输入到空间明确的COVID-19传播模型中，并将抽象为可以简化未来流行病学模型的边界条件。该RAPID奖由环境生物学部传染病生态学和进化项目提供，资金来自《冠状病毒援助、救济和经济安全法案》。该奖项反映了美国国家科学基金会的法定使命，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。