Weather, Social Distancing, and the Spread of COVID-19

Abstract

This paper develops an econometric panel data model that can be used both to identify the dynamic effects of disease transmission factors and to forecast disease spread.The empirical model is derived from the canonical SIR epidemiological model of infectious disease spread. The model is estimated using near real-time, county-level data on mobility, weather, and COVID-19 cases. Both mobility and weather are found to have significant effects on COVID-19 effects up to 70 days ahead. Predicted values from the estimated model, augmented to incorporate recent vaccinations, provide out-of-sample forecasts of COVID-19 infections at the county and national levels. Prior forecasts are shown to have been fairly accurate, especially in terms of the geographical/crosssectional distribution of COVID-19 infections and in terms of the national aggregate forecast. The latest forecasts, using data through February 19, 2021, predict steep declines in infections in most parts of the country over the next several weeks. Nationally, infections are predicted to fall by 59% over the subsequent 30 days. Decomposing the drivers of the latest forecast, the model indicates that accumulated natural immunity (i.e., cumulative infections to date, a.k.a. "seroprevalence") is the primary factor exerting a strong downward pull on new infections.

Winter Is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19

“…Similarly, Leung et al [ 162 ] suggest support for the hypothesis that lower temperature and humidity enhance COVID-19 transmission. Similar responses are seen by Lin et al [ 165 ] and Wilson [ 174 ] with regards to temperature, but they also suggest an interaction between temperature and relative humidity [ 165 ] and temperature and mobility [ 174 ] in terms of modulating infection rates. In contrast, Gupta and Gharehgozli [ 147 ] show that higher temperatures enhance the spread of the disease; they also show that viral transmission is enhanced under higher concentrations of PM2.5.…”

“…These techniques rely on Generalised Additive Models (GAMs) for the flexible estimation of smooth responses and parametric terms. The recognition that disease dynamics may differ between locations for a multitude of reasons requires that ‘location’ be specified as random effect (notable examples involving COVID-19 include Carlton et al [ 153 ] and Wilson [ 174 ]). Such approaches can be accommodated by longitudinal models (called panel regressions by economists) (sensu Gardiner et al [ 199 ]), which regress the dependant variable (plus covariates and constraints) as a function of time.…”

“…The studies in Supplementary Tables S1 and S2 employed the following statistical methods to evaluate relationships between environmental variables and the transmission rate of SARS-CoV-2: various linear, logistic, or exponential parametric models [ 39 , 41 , 50 , 130 , 134 , 142 , 146 , 148 , 151 , 158 , 164 , 165 , 166 , 167 ], sometimes with the inclusion of non-Gaussian error structures as permitted by Generalised Linear Models (GLMs) [ 141 , 157 , 162 , 164 , 168 , 169 , 170 , 171 ]; Generalised Additive Models (GAMs) [ 41 , 134 , 167 , 172 , 173 ]; distributed lag panel regression models [ 153 ]; machine learning such as support vector machines and decision trees [ 147 ]; local panel projection estimator within a country-level dynamic framework [ 174 ]; Loess smoothers/curves [ 142 ]; Bayesian methods [ 157 ]; and Pearson’s, Spearman’s, and Kendall’s correlations [ 40 , 138 , 139 , 140 , 154 , 175 , 176 , 177 ].…”

“…A single published study does not provide robust support for the presence or absence of a climatic influence on SARS-CoV-2 transmission rates. The preprint studies [ 11 , 147 , 152 , 153 , 155 , 156 , 162 , 174 , 190 ] offer mixed statistical support (none, weak, or strong relationships) for the influence of environmental drivers. Carlton et al [ 153 ] show that that UV radiation affects COVID-19 growth rates, but not temperature or humidity.…”

Weather and Policy Influences on Mobility during the U.S. COVID-19 Pandemic

“…30,32 Figure 3 shows a weakly positive correlation between temperature and park visitation but a lack of correlation between temperature and encounter rate. In other words, while people in most states change their behavior to visit parks more when temperatures are higher, in line with previous work, 13,33 their potential encounter rates do not significantly increase (with a few exceptions). (Similar trends are observed on the county level in Extended Data Figure 5.…”