Comparing and Integrating US COVID-19 Daily Data from Multiple Sources: A County-Level Dataset with Local Characteristics
Year: 2020
Type: preprint
Abstract: Over the past several months, the outbreak of COVID-19 has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and for policy-makers to make better decisions. We collect the COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, and compare the similarities and differences among them. In addition, we examine the following problems which occur frequently: (1) the order dependencies violation, (2) the delay-reported issue on weekends and/or holidays, and (3) abnormal data point or data period. We also integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environment information, which are important for understanding the spread of the virus.
Source: arXiv (Cornell University)
Authors: G Wang, Gu Z, Li X, Y. Shi, Meelim Kim, Y Wang, Lijie Gao, L Wang
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3033693347