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

pressure (SP), and total precipitation (TP). (DOCX) S2 Table. Correlation of various factors. Notes: * represents P-value < 0.05, and � represents P-value < 0.01. 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). (DOCX) S3 Table. Coefficient of cumulative effect analysis. Notes: R-squared = 0.85, Adjusted R- squared = 0.81, and Prob(F-statistic) < 0.001. Abbreviations: growth rate of SARS-CoV-2 (GR), specific humidity (H), 2-meter temperature (T), wind speed (WS), ultraviolet (UV), sur- face pressure (SP), and total precipitation (TP). (DOCX) Acknowledgments We thank every epidemic prevention and control worker and scientific researcher for their hard work in SARS-CoV-2. Author Contributions Methodology: Zhenyu Yu. Supervision: Jinnian Wang. Writing – original draft: Zhenyu Yu. Writing – review & editing: Zhenyu Yu, Jinnian Wang, Zixuan Tan, Yiyun Luo. References 1. Wu F, Zhao S, Yu B, Chen YM, Zhang YZ. Author Correction: A new coronavirus associated with human respiratory disease in China. Nature. 2020; 580(7803). https://doi.org/10.1038/s41586-020- 2202-3 PMID: 32296181 2. Leticia TI, Ana BA, Martha C, Rossana TA, Francisco RS, Herna´ndez-Vila JE, et al. SARS-CoV-2 infec- tion fatality rate after the first epidemic wave in Mexico. International Journal of Epidemiology. 2022;( 2):2. 3. Julia S, Elise T, Zhao H, Rebecca G, Berit M P, Maria Z, et al. Interactions between SARS-CoV-2 and

influenza, and the impact of coinfection on disease severity: a test-negative design. International Jour- nal of Epidemiology. 2021;(4):4. 4. Peeri NC, Shrestha N, Rahman MS, Zaki R, Haque U. The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? International Journal of Epidemiology. 2020; 49(3). https://doi.org/10.1093/ije/dyaa033 PMID: 32086938 5. Sun P, Ren J, Li K, Qie S, Liu Z, Xi J. Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: A single arm meta-analysis. Journal of Medical Virology. 2020; 92. 6. Tung NT, Cheng PC, Chi KH, Hsiao TC, Chuang HC. Particulate matter and SARS-CoV-2: A possible model of COVID-19 transmission. Science of The Total Environment. 2020;141532. https://doi.org/10. 1016/j.scitotenv.2020.141532 PMID: 32858292 7. Lim W, Cowling B. Mechanistic Correlates of Protection for SARS-CoV-2 Vaccines. Epidemiology. 2022; https://doi.org/10.1097/EDE.0000000000001419 PMID: 34799478 8. Bruckner T, Das A, Singh P, Boden-Albala B. SARS-CoV-2: An empirical investigation of Rose’s popu- lation-based logic. Epidemiology. 2021; https://doi.org/10.1097/EDE.0000000000001405 PMID: 34347688 9. Riley S, Ainslie K, Eales O, Walters CE, Wang H, Atchison C, et al. Resurgence of SARS-CoV-2: Detec- tion by community viral surveillance. Science. 2021;(6545). https://doi.org/10.1126/science.abf0874 PMID: 33893241 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 19 / 21

10. Zoran MA, Savastru RS, Dan MS, Tautan MN. Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy. Science of The Total Environment. 2020; 740:140005. https://doi.org/10.1016/j.scitotenv.2020.140005 PMID: 32559534 11. Nkengasong J. China’s response to a novel coronavirus stands in stark contrast to the 2002 SARS out- break response. Nature Medicine. 2020; 12. Domingo C, Fraissinet J, Ansah PO, Kelly C, Mejı´a JE. Long-term immunity against yellow fever in chil- dren vaccinated during infancy: a longitudinal cohort study. The Lancet Infectious Diseases. 2019; 19 (12):1363–70. https://doi.org/10.1016/S1473-3099(19)30323-8 PMID: 31543249 13. Yao MX, Wu SZ, Wang GL, Wang XJ, Fan WJ, Zhang WG, et al. Imported dengue serotype 1 outbreak in a non-endemic region, China, 2017: A molecular and seroepidemiological study—ScienceDirect. Journal of Infection. 2020; 81(2):304–10. 14. Rachael Piltch-Loeb David Abramson. Information-accessing behavior during zika virus outbreak, united states, 2016. Emerging Infectious Diseases. 2020; 26(9). 15. Liu M, Liu X, Huang Y, Ma Z, Bi J. Epidemic transition of environmental health risk during China’s urban- ization. Science Bulletin. 2017; 62(2):92–8. https://doi.org/10.1016/j.scib.2016.12.004 PMID: 36659489 16. Liu Y, Wu X, Liu R, Chen Y, Fu J, Ou H. Modifications of ultraviolet irradiation and chlorination on micro- plastics: Effect of sterilization pattern. Science of the Total Environment. 2022;(812-):812. https://doi.

org/10.1016/j.scitotenv.2021.152541 PMID: 34954159 17. Wang M, Jiang A, Gong L, Luo L, Li H. Temperature significant change COVID-19 Transmission in 429 cities. Cold Spring Harbor Laboratory Press. 2020; 18. Sajadi MM, Habibzadeh P, Vintzileos A, Shokouhi S, Amoroso A. Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19). JAMA Network Open. 2020; 3(6):e2011834. https://doi.org/10.1001/jamanetworkopen.2020.11834 PMID: 32525550 19. Correa-Araneda FJ, Yaez A, Nu´ez D, Boyero L, Esse C. Environmental determinants of COVID-19 transmission across a wide climatic gradient in Chile. Scientific Reports. 2021; 11(9849). https://doi.org/ 10.1038/s41598-021-89213-4 PMID: 33972582 20. Ganslmeier M, Furceri D, Ostry JD. The impact of weather on COVID-19 pandemic. Scientific Reports. 2021;(1). https://doi.org/10.1038/s41598-021-01189-3 PMID: 34764317 21. Shaman J P, Pietzonka E, Brorson W, Bankes M E, et al. Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK. PLoS ONE. 2021; 16(11): e0258968. 22. Briz-Redo´n L, Serrano-Aroca N. The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques. Progress in Physical Geography. 2020; 44 (18):030913332094630. 23. Baker RE, Yang W, Vecchi GA, Metcalf CJE, Grenfell BT. Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic. Science. 2020; 369(6501):eabc2535. https://doi.org/10.1126/ science.abc2535 PMID: 32423996

24. Park A, YC B, DS C, Ekk D. Natural ventilation strategy and related issues to prevent coronavirus dis- ease 2019 (COVID-19) airborne transmission in a school building. Science of The Total Environment. 2021; 25. Carleton T, Cornetet J, Huybers P, Meng KC, Proctor J. Global evidence for ultraviolet radiation decreasing COVID-19 growth rates. Proceedings of the National Academy of Sciences. 2021; 118(1): e2012370118. https://doi.org/10.1073/pnas.2012370118 PMID: 33323525 26. Cacciapaglia G, Sannino F. Interplay of social distancing and border restrictions for pandemics (COVID-19) via the epidemic Renormalisation Group framework. 2020; 27. Yang T, Liu Y, Deng W, Zhao W, Deng J. SARS-Cov-2 trajectory predictions and scenario simulations from a global perspective: a modelling study. Scientific Reports. https://doi.org/10.1038/s41598-020- 75332-x PMID: 33110114 28. Brauner JM, Mindermann S, Sharma M, Johnston D, Salvatier J, Gaveniak T, et al. Inferring the effec- tiveness of government interventions against COVID-19. American Association for the Advancement of Science. 2021;(6531). https://doi.org/10.1126/science.abd9338 PMID: 33323424 29. Xi J, Liu X, Wang J, Yao L, Zhou C. A Systematic Review of COVID-19 Geographical Research: Machine Learning and Bibliometric Approach. Annals of the American Association of Geographers. 2022;1–18. 30. Wong D W, Li S Y. Spreading of COVID-19: Density matters. PLoS ONE. 2020; 15 (12):e0242398. https://doi.org/10.1371/journal.pone.0242398 PMID: 33362283 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 20 / 21

31. Buja A, Paganini M, Cocchio S, Scioni M, Rebba V, Baldo V. Demographic and Socio-Economic Fac- tors, and Healthcare Resource Indicators Associated with the Rapid Spread of COVID-19 in Northern Italy: An Ecological Study. Cold Spring Harbor Laboratory Press. 2020;(12). 32. Bright EA, Rose AN, Urban ML. LandScan 2015 High-Resolution Global Population Data Set. 2016; 33. Stelzenmu¨ ller V, Coll M, Cormier R, Mazaris AD, Dimitriadis C. Operationalizing risk-based cumulative effect assessments in the marine environment. Science of The Total Environment. 2020; 724:138118. https://doi.org/10.1016/j.scitotenv.2020.138118 PMID: 32247136 34. Galan DI, Roess AA, Pereira S, Schneider MC. Epidemiology of human leptospirosis in urban and rural areas of Brazil, 2000–2015. PLoS ONE. 2021; 16(3):e0247763. https://doi.org/10.1371/journal.pone. 0247763 PMID: 33661947 35. Wilt GE, Adams EE, Thomas E, Ekperi L, Leblanc TT, Dunn I, et al. A Space Time Analysis Evaluating the Impact of Hurricane Sandy on HIV Testing Rates. International Journal of Disaster Risk Reduction. 2018;S2212420918304266. 36. Wang WN, Cheng TH, Gu XF, Hao C, Zhang XC. Assessing Spatial and Temporal Patterns of Observed Ground-level Ozone in China. Scientific Reports. 2017; 7(1). https://doi.org/10.1038/s41598- 017-03929-w PMID: 28623330 37. Burns JN, Acuna-Soto R, Stahle DW. Drought and Epidemic Typhus, Central Mexico, 1655–1918. Emerging Infectious Diseases. 2014; 20(3):442–7. https://doi.org/10.3201/eid2003.131366 PMID: 24564928

38. Hao Z, Zhang X, Singh VP, Hao F. Joint modeling of precipitation and temperature under influences of El Nio Southern Oscillation for compound event evaluation and prediction. Atmospheric Research. 2020; 245:105090. 39. Song Z, Xia J, She D, Zhang L, Zhao L. The development of a Nonstationary Standardized Precipitation Index using climate covariates: A case study in the middle and lower reaches of Yangtze River Basin, China. Journal of Hydrology. 2020; 588(3):125115. 40. Akter R, Hu W, Gatton M, Bambrick H, Tong S. Different responses of dengue to weather variability across climate zones in Queensland, Australia. Environmental Research. 2020; 184:109222-. https:// doi.org/10.1016/j.envres.2020.109222 PMID: 32114157 41. Feng L, Zhao Q, Zhou C. Epidemic in networked population with recurrent mobility pattern. Chaos Soli- tons & Fractals. 2020; 139:110016. https://doi.org/10.1016/j.chaos.2020.110016 PMID: 32834588 42. Auranen K, Shubin M, Karhunen M, Sivel J, Nurhonen M. Social Distancing and SARS-CoV-2 Trans- mission Potential Early in the Epidemic in Finland. Epidemiology (Cambridge, Mass). 2021; Publish Ahead of Print(4). 43. Pandey BD, Costello A. The dengue epidemic and climate change in Nepal. The Lancet. 2019; (10215):394. https://doi.org/10.1016/S0140-6736(19)32689-3 PMID: 31839187 44. Mcgough SF, Clemente L, Kutz JN, Santillana M. A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles. Journal of