The outbreak of coronavirus disease 2019 (COVID-19) continues to spread worldwide and has led to recession, rising unemployment, and the collapse of the health-care system . The aim of this study was to explore the exposure-response relationship between daily confirmed COVID-19 cases and environmental factors . We used a time-series generalized additive model (GAM) to investigate the short-term association between COVID-19 and environmental factors by using daily meteorological elements, air pollutant concentration, and daily confirmed COVID-19 cases from January 21 , 2020, to February 29 , 2020, in Shanghai, China . We observed significant negative associations between daily confirmed COVID-19 cases and mean temperature (Tave), temperature humidity index (THI), and index of wind effect (K), whereas air quality index (AQI), PM2.5, PM10 NO2, and SO2 were significantly associated with the increase in daily confirmed COVID-19 cases . A 1 °C increase in Tave, one-unit increase in THI, and 10-unit increase in K (lag 0-7 days) were associated with 4.7 , 1.8, and 1.6% decrease in daily confirmed cases, respectively . Daily Tave, THI, K, PM10, and SO2 had significant lag and persistence (lag 0-7 days), whereas the lag and persistence of AQI, PM2.5, and NO2 were significant at both lag 0-7 and 0-14 days . A 10-µg/m3 increase in PM10 and 1-µg/m3 increase in SO2 was associated with 13.9 and 5.7% increase in daily confirmed cases at lag 0-7 days, respectively, whereas a 10-unit increase in AQI and a 10-µg/m3 increase in PM2.5 and NO2 were associated with 7.9 , 7.8, and 10.1% increase in daily confirmed cases at lag 0-14 days, respectively . Our findings have important implications for public health in the city of Shanghai.