Some Multiple Regression Models for the Number of COVID-19 Cases and Deaths in the United States

John Tenenholtz, Florence George and Sneh Gulati

International Journal of Statistics and Probability, 2021, vol. 10, issue 1, 28

Abstract: The whole world has been affected by the COVID-19 pandemic. It has changed life drastically, affecting both social and business behavior and causing major economic distress throughout the world. The disease is often denominated a “novel coronavirus,” meaning that it is a new strain, that none of us carry antibodies to it and that there is much to be learned about its pathology. This obviously makes it hard to control. While several countries seem to have grasped ways to contain the virus, the United States (the “U.S.”) has seen steady growth in the number of cases and deaths. This paper uses multiple regression models to examine the differences among the several U.S. states in the numbers of cases and deaths and investigates several possible contributing factors to these totals.

Date: 2021
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