Impact of climate change on SARS-CoV-2 epidemic in China

0.35), insignificantly and positively correlated with TP (RTP = 0.14), and significantly and negatively correlated with the other four factors. GR had the highest correlation with UV and T (RUV = -0.53, RT = -0.51). This indicates that UV, H, T, and WS imposed inhibitory effects on GR, whereas SP and TP facilitated the GR. 3. In terms of the effects of the weather factors, the most significant effect on GR was indicated by H (ratio = 23.06%), followed by T (ratio = 20.92%) and UV (ratio = 18.65%). For every 1% increase in H, every 1˚C increase in T, and every 1 kJ�m-2�h-1 increase in UV, the GR decreased by 16.47±20.28%, 1.58±1.11%, and 0.30±0.21%, respectively. The UV effect in China was higher than the global level (0.92% higher per day). However, theoretically, cli- mate or weather changes within normal ranges do not prevent the spread of SARS-CoV-2. The factors that directly affected the SARS-CoV-2 epidemic were high population aggrega- tion and high-level urbanization, whereas weather imposed only an indirect effect. Supporting information S1 Fig. SARS-CoV-2 growth rate change trend in each province of China in 2020 (Unit: %). The green line is the growth rate of SARS-CoV-2 in each province, and the yellow line is April 30, 2020, which is used to select the time period. (TIF) S2 Fig. Data comparison before and after processing. Notes: Original versus weighted data. Take Beijing and Guangdong for example. Abbreviations: growth rate of SARS-CoV-2 (GR), specific humidity (H), 2-meter temperature (T), wind speed (WS), ultraviolet (UV), surface

pressure (SP), and total precipitation (TP). (TIF) S3 Fig. Date distribution of the highest growth rate (GR) of SARS-CoV-2 in each province (Unit: %). (TIF) S4 Fig. Time series characteristics of various factors in each province. Notes: growth rate of SARS-CoV-2 (GR), specific humidity (H), 2-meter temperature (T), wind speed (WS), ultravi- olet (UV), surface pressure (SP), and total precipitation (TP). (TIF) S5 Fig. Correlation of various factors. Notes: growth rate of SARS-CoV-2 (GR), specific humidity (H), 2-meter temperature (T), wind speed (WS), ultraviolet (UV), surface pressure (SP), and total precipitation (TP). (TIF) S6 Fig. Correlation between SARS-CoV-2 growth rate and each weather factor (time series). Notes: growth rate of SARS-CoV-2 (GR), specific humidity (H), 2-meter temperature (T), wind speed (WS), ultraviolet (UV), surface pressure (SP), and total precipitation (TP). (ZIP) S7 Fig. Distribution of climate zones in China and Hu Line (Heihe-Tengchong Line). (TIF) S1 Table. Statistical information of each factor. Notes: growth rate of SARS-CoV-2 (GR), specific humidity (H), 2-meter temperature (T), wind speed (WS), ultraviolet (UV), surface PLOS ONE Impact of climate change on SARS-CoV-2 epidemic in China PLOS ONE | https://doi.org/10.1371/journal.pone.0285179 July 27, 2023 18 / 21