COVID-19’s U.S. Temperature Response Profile

Abstract

We estimate the U.S. temperature response profile (TRP) for COVID-19 and show it is highly sensitive to temperature variation. Replacing the erratic daily death counts U.S. states initially reported with counts based on death certificate date, we build a week-ahead statistical forecasting model that explains most of their daily variation (R2 = 0.97) and isolates COVID-19’s TRP (p < 0.001). These counts, normalized at 31 °C (U.S. mid-summer average), scale up to 160% at 5 °C in the static case where the infection pool is held constant. Positive case counts are substantially more temperature sensitive. When temperatures are declining, dynamic feedback through a growing infection pool can substantially amplify these temperature effects. Our estimated TRP can be incorporated into COVID-related planning exercises and used as an input to SEIR models employed for longer run forecasting. For the former, we show how our TRP is predictive of the realized pattern of growth rates in per capita positive cases across states five months after the end of our sample period. For the latter, we show the variation in herd immunity levels implied by temperature-driven, time-varying R0 series for the Alpha and Delta variants of COVID-19 for several representative states.

Exploring the risk factors of COVID‐19 Delta variant in the United States based on Bayesian spatio‐temporal analysis

“…Carson et al. ( 2021 ) showed that Alpha variant was substantially more temperature sensitive, with humidity insignificant in the fitted models. According to Duong ( 2021 ) and Zhao et al.…”

“…On the whole, Delta spread much faster from the central region to the southeast, while Alpha spread from the north and south to the middle. Carson et al (2021) showed that Alpha variant was substantially more temperature sensitive, with humidity insignificant in the fitted models. According to Duong (2021) and Zhao et al (2022), Alpha is 50% more transmissible than the original strain, and thus frequent community mobility and dense population will definitely accelerate the spread of the virus.…”

Relationship between meteorological factors and mortality in patients with coronavirus disease 2019: A cross-sectional study

“…To the best of our knowledge, most studies have examined the relationship between climatic factors, especially temperature, and the incidence, not mortality of COVID-19 in the United States, with time span usually less than six months [ [16] , [17] , [18] ]. Carson R.T. et al [ 19 ] concluded that COVID-19 death counts are strongly influenced by changes in maximum daily temperature. However, Karimi S.M [ 20 ] collected weather data of 3141 US counties, including minimum and maximum daily temperature, precipitation, ozone concentration, PM2.5 concentrations, and U.V.…”