Abstract
The number of confirmed cases of COVID-19 peaked in the United States in the month of April, 2020 in the most populated cities. A study was done to assess if temperature, dewpoint, and population density in fifty cities had a significant effect on the number of confirmed cases of COVID-19 in the month of April. Temperature and Dewpoint data for the month of April were accessed from the National Oceanic and Atmospheric Administration (NOAA) and confirmed cases of COVID-19 was accessed from the CDC (Centers for Disease Control and Prevention). Pearson Correlation Coefficient and Multiple Regression statistical analyses were performed. COVID-19 cases in the fifty states largest cities were correlated with average temperature and dewpoint in April, as well as the city’s population density. For the Multiple Regression analysis the total number of COVID-19 cases in fifty cities served as the dependent variable and average temperature, average dewpoint, and population density comprised the predictor variables. Correlation coefficient statistics utilizing data for fifty largest cities in each state for COVID-19 cases and average temperature in April 2020 yielded an r of -.097 and P>.05; between COVID-19 and average dewpoint for April 2020 yielded an r of -.001 and P>.05, and COVID-19 and population density pairing yielded an r of .792 and P<.01 for respective cities. Multiple regression analysis yielded statistical significance for the population density variable only. This study demonstrated that cities population density had a significant effect on the number of COVID-19 cases in April 2020, and while both temperature and dewpoint resulted in a negative correlation in association with COVID-19 cases, results were not statistically significant. The seasonal uncertainty and high transmission rate of COVID-19 in the United States suggest that similar analyses should be replicated in the impending summer and winter months of 2020 to further delineate COVID-19 seasonal characteristics.
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