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  1. ; ; ; (, SIAM Conference on Applied and Computational Discrete Algorithms)
    Coronavirus Disease 2019 (Covid-19) is an ongoing outbreak and the latest threat to global health. It is imperative to understand the implications of social interaction on Covid-19 indicators in order to help formulate policies and guidelines by governments and local authorities. We present a case-study of curating state-level Covid-19 indicators such as Active Cases, Deaths, Hospitalization Rate, etc. for the United States. We also curate open source domestic US air travel data and present its impact on Covid-19 indicators. We perform a time-series analysis of the dataset using Independent Temporal Motif (ITeM) to find weekly trends in the data. We publish the dataset and the results for further exploration by the research community. 
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    Accepted Manuscript Full Text Available