Using Road Traffic Data to Identify COVID-19 Priority Testing Locations in Southern California

使用道路交通数据确定南加州的 COVID-19 优先测试地点

• 批准号: 10472496
• 负责人: Sze-Chuan Suen
• 金额: $ 16.54万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10472496
• 关键词: Algorithms; Area; COVID-19; COVID-19 testing; California; Caring; Country; County; Data; Data Management Resources; Destinations; Diagnosis; Disease; Disease model; Elderly; Engineering; Epidemic; Future; Guidelines; Health; Individual; Infection; Letters; Light; Literature; Location; Los Angeles; Maintenance; Measures; Medical; Methodology; Modeling; Nursing Homes; Patients; Pattern; Policies; Population; Prevalence; Public Health; Quarantine; Recommendation; Research Personnel; Site; Social Distance; Speed; Structural Models; Symptoms; System; Testing; Time; Transportation; Travel; Vaccines; Widespread Disease; Work; archive data; archived data; disease transmission; disorder control; effective therapy; flu; health training; high risk; infectious disease model; insight; metropolitan; network models; novel; novel strategies; policy recommendation; sensor; social; social contact; transmission process; urban area

Project Summary Without a vaccine or effective treatment, there is an urgent need for performing widespread COVID-19 testing to control disease spread. However, complete population testing is prohibitively challenging as testing supplies are limited and require trained health staff which could be better used in caring for those confirmed to be infected. It is therefore critical to focus testing in high-priority areas, where tests are likely to capture positive cases. Identifying infected individuals quickly as tests become more widely available will provide crucial information on overall disease prevalence to inform future disease control efforts. We can help identify areas of potentially high disease prevalence by synthesizing and using traffic patterns, as transportation patterns may shed light on possible transmission patterns in Los Angeles County (LAC). We propose using the USC Archived Data Management System (ADMS), which collects and synthesizes traffic data, to create an epidemic model informed by up-to-date origin-destination traffic information. We will use the model to identify which of the 26 health districts in LAC are at highest risk for unidentified cases and optimally locate testing sites within these regions. This allows our recommendations to incorporate change in transportation patterns as social distancing recommendations evolve. Specifically, we will partner with the LA County Department of Public Health to: 1. Use road sensor data to analyze traffic patterns in Los Angeles County to understand the impact of social distancing guidelines on population flow. 2. Develop a dynamic transmission network model of COVID-19 using results from Aim 1 and disease parameters from the medical literature to identify high priority districts for testing. 3. Develop a location model to optimally place drive-through testing sites in these districts. The proposed work will use methodology from infectious disease transmission models, traffic data, and facility location models together in a novel way. Not only will we provide much needed insight using empirical data into population flow dynamics in the context of social distancing recommendations, we will shed light on infectious disease modeling more generally. By creating a compartmental network model with realistic, time-varying travel patterns in a large metropolitan area, the proposed work will further our understanding of the impacts of structural modeling assumptions on disease prediction.

项目摘要 在没有疫苗或有效治疗的情况下，迫切需要进行广泛的 新冠肺炎检测以控制疾病传播。然而，完整的人口测试是 极具挑战性，因为检测用品有限，而且需要训练有素的卫生工作人员， 可以更好地用于照顾那些被确认感染的人。因此，将重点放在 在高优先领域进行检测，在那里检测可能捕捉到阳性病例。识别受感染的 随着测试变得更加普遍，个人将迅速提供关于 总体疾病流行情况，为今后的疾病控制工作提供信息。 我们可以通过综合和使用 交通模式，因为交通模式可以揭示可能的传输模式 洛杉矶县(LAC)。我们建议使用南加州大学存档数据管理系统 (ADMS)，收集和合成交通数据，以创建由 最新的始发地-目的地交通信息。我们将使用该模型来确定26个 拉丁美洲和加勒比地区的卫生区是不明病例风险最高的地区，检测地点也处于最佳位置 在这些区域内。这使得我们的建议纳入了交通运输的变化 随着社交疏远建议的发展，模式也在不断演变。具体地说，我们将与LA合作 县公共卫生局： 1.使用道路传感器数据分析洛杉矶县的交通模式，以了解 社会疏远准则对人口流动的影响。 2.利用AIM 1的结果建立了新冠肺炎的动态传输网络模型 和医学文献中的疾病参数，以确定高优先级区域 测试。 3.开发一个选址模型，以便在这些地区对得来速测试地点进行最佳布置。 拟议的工作将使用传染病传播模型、交通 数据和设施位置模型以一种新颖的方式结合在一起。我们不仅将提供急需的 使用经验数据洞察社会疏远背景下的人口流动动态 建议，我们将更广泛地阐明传染病建模。通过创建 一种车厢网络模型，具有现实的、时变的大范围出行模式 大都会地区，拟议的工作将进一步加深我们对结构影响的理解 对疾病预测的假设进行建模。