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Correlation between weather and the transmission of SARS-CoV-2 may suggestits seasonality. Cointegration analysis can avoid spurious correlation amongtime series data. We examined the cointegration of virus transmission withdaily temperature, dewpoint, and confounding factors of mobility measurementsduring the first year of the pandemic in the United States. We examined thecointegration of the effective reproductive rate, Rt, of the virus with thedewpoint at two meters, the temperature at two meters, Apple driving mobility,and Google workplace mobility measurements. We found that dewpoint and Appledriving mobility are the best factors to cointegrate with Rt, althoughtemperature and Google workplace mobility also cointegrate with Rt atsubstantial levels. We found that the optimal lag is two days for cointegrationbetween Rt and weather variables, and three days for Rt and mobility. Weobserved clusters of states that share similar cointegration results of Rt,weather, and mobility, suggesting regional patterns. Our results support thecorrelation of weather with the spread of SARS-CoV-2 and its potentialseasonality.

Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States