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.
Supplementary information: The online version contains supplementary material available at 10.1007/s10640-021-00603-8.
Keywords: Data temporal alignment; Epidemiology; Forecasting; Temperature sensitivity.
© The Author(s) 2021.