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

TL;DR

This study uses cointegration analysis to explore the relationship between SARS-CoV-2 transmission, weather conditions, and mobility in the US during the first pandemic year, highlighting regional patterns and potential seasonality.

Contribution

It introduces cointegration analysis to distinguish genuine relationships from spurious correlations among transmission, weather, and mobility data.

Findings

Dewpoint and mobility are strongly cointegrated with transmission rate.

Optimal lag for weather variables is two days; for mobility, three days.

Regional clusters show similar cointegration patterns.

Abstract

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.