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

1项目概要 在没有疫苗或有效治疗的情况下，迫切需要进行广泛的 3 COVID-19检测以控制疾病传播。然而，完整的群体测试是 4.由于检测用品有限，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被证实感染的人。因此， 6.在高优先地区进行检测，这些地区的检测可能会发现阳性病例。识别感染者 随着测试变得更加广泛，7个人将提供以下方面的关键信息： 8总体疾病流行率，为今后的疾病控制工作提供信息。 9.我们可以通过综合和使用 10种交通模式，因为交通模式可以揭示可能的传播模式， 11洛杉矶县。我们建议使用USC存档数据管理系统 12（ADMS），它收集和综合交通数据，以创建一个流行病模型， 13最新的始发地-目的地交通信息。我们将使用该模型来识别26个 拉丁美洲和加勒比地区的14个卫生区面临不明病例的最高风险，并最佳地选择检测地点 15个在这些地区。这使得我们的建议纳入交通变化 16模式作为社会距离的建议演变。具体来说，我们将与洛杉矶合作， 17县卫生局： 18 1.使用道路传感器数据分析洛杉矶县的交通模式， 19.社会距离准则对人口流动的影响。 20 2.使用目标1的结果开发COVID-19的动态传播网络模型 21和医学文献中的疾病参数，以确定高优先地区， 22测试 233.开发一个位置模型，以便在这些地区最佳地放置免下车考试点。 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。我们不仅会提供急需的 第26章在社会距离的背景下利用经验数据对人口流动动态的洞察 27条建议，我们将更普遍地阐明传染病建模。通过创建 图28是一个具有现实的、随时间变化的旅行模式的分区网络模型， 29大都市圈，建议的工作将进一步了解结构性的影响， 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.