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: 2032276
• 负责人: Andrew MacDonald
• 金额: $ 10万
• 依托单位: University of California-Santa Barbara
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032276&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.

动态的疾病传播计算机模型用于理解和预测传染病如何通过宿主种群传播。人口分布，流动性和旅行走廊的地图是其中许多模型的关键组成部分。但是，准确地确定人的空间分布很困难，因为大多数数据来源（例如人口普查）仅表示人们居住的地方，而不是他们的工作和去处。尤其是人口普查数据，也以一种仅在人口稠密的城市地区提供精细的空间细节的方式进行了汇总。在郊区和农村地区，人口普查地图仅提供居住在每个人口普查部门（例如美国县）的人数，但不显示人们在哪里生活和工作。这项研究将融合从城市，城镇和旅行走廊发出的夜灯的详细卫星图像，并使用人口普查计数和流动性数据融合，以生成更详细的人口图，以供流行病学家使用，以更准确地模拟Covid-19等传染病的传播。拟议的合作将使地理空间动力学和远程感知与疾病生态学和流行病学融合在一起，以产生跨越科学的边界，并有可能推进这两个领域。此外，拟议的项目将支持两名早期职业科学家以及学生参与研究的本科生。当空气和车辆旅行大大降低时，人口运动的准确性和细节和空间联系的准确性和细节对建模流行病的差异更为重要。来自地理空间数据的共分析（从远程感知的夜灯和人口普查列举的夜间光密度和基础设施密度）得出的空间网络可以提供比用于聚集和分析健康数据的行政部门（例如县）更准确的空间域（例如县）。此外，这些空间网络的结构和连通性可用于量化影响疾病扩散的网络结构的基本参数。这项研究将开发一套逐步精制的网络图，以与流行病学模型一起使用。由地球科学家，疾病生态学家和流行病学家组成的研究团队将开发一种标准化协议，其分析程序和工具用于生产这些地图，以适合美国的SARS-COV-2感染，包括详细的SARS-COV-2感染纽约和洛杉矶都会区的分析。人口中心之间的网络流参数将使用基于代理的建模估算，建立一个完整的地理空间网络，该网络包括城市内部的人口和流动性约束，以及城市之间的人口通量。人口和网络流量估计将直接输入到空间显式的COVID-19传输模型中，并将将其抽象为可以简化未来流行病学模型的边界条件。这项快速奖励是由环境生物学系的传染病计划的生态和进化，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的法定任务，并被认为是值得的。通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。

项目成果

Human footprint is associated with shifts in the assemblages of major vector-borne diseases.

• DOI: 10.1038/s41893-023-01080-1
• 发表时间: 2023-06
• 期刊: NATURE SUSTAINABILITY
• 影响因子: 27.6
• 作者: Skinner, Eloise B.;Glidden, Caroline K.;MacDonald, Andrew J.;Mordecai, Erin A.
• 通讯作者: Mordecai, Erin A.