  Correlation between weather and the transmission of SARS-CoV-2 may suggest
its seasonality. Cointegration analysis can avoid spurious correlation among
time series data. We examined the cointegration of virus transmission with
daily temperature, dewpoint, and confounding factors of mobility measurements
during the first year of the pandemic in the United States. We examined the
cointegration of the effective reproductive rate, Rt, of the virus with the
dewpoint at two meters, the temperature at two meters, Apple driving mobility,
and Google workplace mobility measurements. We found that dewpoint and Apple
driving mobility are the best factors to cointegrate with Rt, although
temperature and Google workplace mobility also cointegrate with Rt at
substantial levels. We found that the optimal lag is two days for cointegration
between Rt and weather variables, and three days for Rt and mobility. We
observed clusters of states that share similar cointegration results of Rt,
weather, and mobility, suggesting regional patterns. Our results support the
correlation of weather with the spread of SARS-CoV-2 and its potential
seasonality.
