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

0.25˚ and an hourly temporal resolution. A total of six weather factors were selected (as shown in Table 1), namely 1,000 hPa hourly downward UV radiation at the surface (J/m2�h), 1,000 hPa H (%), T (˚C), TP (mm), 10-m WS (m�s-1), and SP (kPa). The T, TP, SP, and WS data were obtained from ERA5-Land hourly data (from 1950 to the present), and the UV radiation and H data were obtained from ERA5 hourly data on pressure levels (from 1959 to the pres- ent). Among them, the UV radiation referred to solar radiation in the wavelength band between 100 and 400 nm, which is typically classified into three categories, i.e., UV-A (320– 400 nm), UV-B (280–320 nm), and UV-C (100–280 nm). UV-B was used in this study. The WS in the data was calculated from the 10-m u-component and 10-m v-component of the wind, and the combined method for calculating u and v components is shown in Eq (1). WS ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðuÞ2 þ ðvÞ2 q ð1Þ Methods Data preprocessing Data preprocessing is essential for investigating the correlation between the SARS-CoV-2 epi- demic and weather factors, which primary determines whether credible results can be obtained. (1) Data format conversion. The ECMWF data format was NetCDF (127 GB in total). Python’s netCDF4 package was used to convert it into hourly GeoTIFF format (16,968 views in total), convert the geographic coordinates into projection coordinates, establish a 10 km Fig 2. Percentage of provinces with zero growth rate in SARS-CoV-2. Notes: Statistics conducted by provincial administrative regions.

https://doi.org/10.1371/journal.pone.0285179.g002 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 4 / 21