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Over the past few months, the outbreak of Coronavirus disease (COVID-19) hasbeen expanding over the world. A reliable and accurate dataset of the cases isvital for scientists to conduct related research and for policy-makers to makebetter decisions. We collect the United States COVID-19 daily reported datafrom four open sources: the New York Times, the COVID-19 Data Repository byJohns Hopkins University, the COVID Tracking Project at the Atlantic, and theUSAFacts, then compare the similarities and differences among them. To obtainreliable data for further analysis, we first examine the cyclical pattern andthe following anomalies, which frequently occur in the reported cases: (1) theorder dependencies violation, (2) the point or period anomalies, and (3) theissue of reporting delay. To address these detected issues, we propose thecorresponding repairing methods and procedures if corrections are necessary. Inaddition, we integrate the COVID-19 reported cases with the county-levelauxiliary information of the local features from official sources, such ashealth infrastructure, demographic, socioeconomic, and environmentalinformation, which are also essential for understanding the spread of thevirus.

Comparing and Integrating US COVID-19 Data from Multiple Sources with Anomaly Detection and Repairing