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

1个项目摘要 2在没有疫苗或有效治疗的情况下，迫切需要进行广泛的 3新冠肺炎检测，控制疾病传播。然而，完整的人口测试是 4令人望而却步的挑战，因为检测用品有限，需要训练有素的卫生工作人员 5可以更好地用于照顾那些确认感染的人。因此，将重点放在 6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 随着测试变得更加广泛，7个人将迅速提供有关 8总体疾病流行情况，为今后的疾病控制工作提供信息。 9我们可以帮助识别潜在的疾病高发地区通过综合和使用 10种交通模式，因为交通模式可能会揭示 11洛杉矶县(LAC)。我们建议使用南加州大学存档数据管理系统 12(ADMS)，收集和合成交通数据，以创建由 13最新的起点-目的地交通信息。我们将使用该模型来确定26个 拉丁美洲和加勒比的14个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 15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演变，出现了16种模式。具体地说，我们将与LA合作 17县公共卫生厅： 18.使用道路传感器数据分析洛杉矶县的交通模式，以了解 19社会疏远准则对人口流动的影响。 20.利用目标1的结果建立新冠肺炎的动态传输网络模型 21和医学文献中的疾病参数，以确定高度优先的地区 22测试。 23 3.开发一个选址模型，以便在这些地区最佳地布置得来速测试地点。 24拟议的工作将使用传染病传播模型、交通 25个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 26使用经验数据洞察社会距离背景下的人口流动动态 27条建议，我们将更全面地阐明传染病模型。通过创建 28车厢网络模型，具有现实的、随时间变化的出行模式 29都会区，拟议的工作将进一步加深我们对结构影响的理解 关于疾病预测的30个建模假设。

1 Project Summary 2 Without a vaccine or effective treatment, there is an urgent need for performing widespread 3 COVID-19 testing to control disease spread. However, complete population testing is 4 prohibitively challenging as testing supplies are limited and require trained health staff which 5 could be better used in caring for those confirmed to be infected. It is therefore critical to focus 6 testing in high-priority areas, where tests are likely to capture positive cases. Identifying infected 7 individuals quickly as tests become more widely available will provide crucial information on 8 overall disease prevalence to inform future disease control efforts. 9 We can help identify areas of potentially high disease prevalence by synthesizing and using 10 traffic patterns, as transportation patterns may shed light on possible transmission patterns in 11 Los Angeles County (LAC). We propose using the USC Archived Data Management System 12 (ADMS), which collects and synthesizes traffic data, to create an epidemic model informed by 13 up-to-date origin-destination traffic information. We will use the model to identify which of the 26 14 health districts in LAC are at highest risk for unidentified cases and optimally locate testing sites 15 within these regions. This allows our recommendations to incorporate change in transportation 16 patterns as social distancing recommendations evolve. Specifically, we will partner with the LA 17 County Department of Public Health to: 18 1. Use road sensor data to analyze traffic patterns in Los Angeles County to understand 19 the impact of social distancing guidelines on population flow. 20 2. Develop a dynamic transmission network model of COVID-19 using results from Aim 1 21 and disease parameters from the medical literature to identify high priority districts for 22 testing. 23 3. Develop a location model to optimally place drive-through testing sites in these districts. 24 The proposed work will use methodology from infectious disease transmission models, traffic 25 data, and facility location models together in a novel way. Not only will we provide much needed 26 insight using empirical data into population flow dynamics in the context of social distancing 27 recommendations, we will shed light on infectious disease modeling more generally. By creating 28 a compartmental network model with realistic, time-varying travel patterns in a large 29 metropolitan area, the proposed work will further our understanding of the impacts of structural 30 modeling assumptions on disease prediction.