Authors: Yigong Wang*, University of Chicago, Marynia Kolak, University of Chicago, Qinyun Lin, University of Chicago, Jiaqi Yang, University of Chicago
Topics: Geographic Information Science and Systems, United States
Keywords: Epidemiology, COVID, Public Health, Health Informatics
Session Type: Virtual Paper
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 8
Presentation File: No File Uploaded
Disease surveillance facilitates better understanding of the pandemic through different geographies, ethnicities, and other socioeconomic factors. COVID-19 surveillance across the U.S. is essential to tracking and mitigating the pandemic, but complicated by a mix of official and unofficial data sources. Publicly available metrics not only cover basic information such as cases and deaths, but also provide other measures such as hospital capacity and testing information. This information is essential to understanding the pandemic and serves as key inputs for prediction models that inform policy-decisions; hence, having consistent information across different data sources is critical to ensuring data accuracy. This research validates several commonly used data sources that provide cases and death metrics (i.e., Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres) from a spatial-temporal perspective. Specifically, we highlight inconsistencies for these data sources in how geographies are presented, how data differs between sources in certain geographies, and how data differs at different points in time. In addition, we examine inconsistencies in COVID testing data reports across geographies in the US, both in their reporting standards and criterion, and also in the standards adopted to calculate key metrics such as positivity rates. Understanding these inconsistencies in COVID data reporting are especially relevant to public health professionals and policymakers to accurately understand the pandemic.