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

The local Moran’s I is a decomposition into individual region cells, and for a particular spa- tial cell i, its local Moran’s I is defined as shown in Eq (7). IiðdÞ ¼ Zi X n j¼1 WijZj ð7Þ where Zi and Zj are the degrees of deviation of the observations from the mean, Wij is a spatial weight matrix in row-normalized form, and Ii is the product of Zi and the weighted average of the observation cells around spatial cell i. Results Spatial and temporal distribution characteristics SARS-CoV-2 spatial distribution characteristics. In the spatial distribution characteriza- tion of the GR, Xinjiang and Qinghai were in the low-low (LL) cluster; Yunnan and Sichuan were in the high-low (HL) cluster; and Shandong, Anhui, Jiangsu, Jiangxi, and Zhejiang were in the high-high (HH) cluster. All of the regions were significant statistically (P-value < 0.05), and no low-high cluster regions were discovered. Xinjiang and Qinghai were surrounded by provinces with higher GRs. Yunnan and Sichuan were surrounded by provinces with lower GRs, whereas the provinces in the HH aggregation region had higher GRs than the surround- ing provinces, as shown in Fig 4. Based on the confirmed spatial distribution, Hubei showed HL clustering; Anhui, Hunan, and Jiangxi showed low-high (LH) clustering; and Gansu, Inner Mongolia, Qinghai, Sichuan, and Xinjiang showed LL clustering, all of which were significant significantly (P-value < 0.05), Fig 4. Spatial distribution and cluster position of SARS-CoV-2. Notes: (a) & (d) show spatial distribution of GR and cluster position; (b) & (e) show confirmed cases;

and (c) & (f) show the ratio. GR is the growth rate of SARS-CoV-2 in China, “confirmed” is the number of confirmed cases, and the ratio is the percentage of confirmed cases in the population. https://doi.org/10.1371/journal.pone.0285179.g004 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 8 / 21