Trends of SARS-Cov-2 infection in 67 countries: Role of climate zone, temperature, humidity, and curve behavior of cumulative frequency on duplication time Trends of SARS-CoV-2 in 67 countries based on climate zone

Main Article Content

Jaime Berumen, MD MAX JULIO SCHMULSON Guadalupe Guerrero Elizabeth Barrera Jorge Larriva-Sahd Gustavo Olaiz Rebeca García-Leyva Rosa María Wong Chew Miguel Betancourt-Cravioto Hector Gallardo Germán Fajardo-Dolci Roberto Tapia-Conyer


Aims. To analyze the role of temperature, humidity, date of first case diagnosed (DFC) and behavior of the growth-curve of cumulative frequency (CF) [number of days to rise (DCS) and reach the first 100 cases (D100), and the difference between them (ΔDD)] with the doubling time (Td) of COVID-19 cases in 67 countries grouped by climate zone.

Methods. Retrospective study based on the WHO registry of cumulative incidence of COVID-19 cases. 1,706,914 subjects diagnosed between 12-29-2019 and 4-15-2020 were analyzed based on exposure to SARS-CoV-2 virus, ambient humidity, temperature, and climate areas (temperate, tropical/subtropical). DCS, D100, ΔDD, DFC, humidity, temperature, Td for the first (Td10) and second (Td20) ten days of the CF growth-curve between countries and were compared according to climate zone, and identification of factors involved in Td, as well as predictors of  CF using lineal regression models.

Results. Td10 and Td20 were ≥3 days longer in tropical/subtropical vs. temperate areas (2.8±1.2 vs. 5.7±3.4; p=1.41E-05 and 4.6±1.8 vs. 8.6±4.2; p=9.7E-05, respectively). The factors involved in Td10 (DFC and ΔDD) were different than those in Td20 (Td10 and climate areas). After D100, the fastest growth-curves during the first 10 days, were associated with Td10<2 and Td10<3 in temperate and tropical/subtropical countries, respectively. The fold change Td20/Td10 >2 was associated with earlier flattening of the growth-curve. In multivariate models, Td10, DFC and ambient temperature were negatively related with CF and explained 44.7% (r2 = 0.447) of CF variability at day 20 of the growth-curve, while Td20 and DFC were negatively related with CF and explained 63.8% (r2 = 0.638) of CF variability towards day 30 of the growth-curve.

Conclusions. Larger Td in tropical/subtropical countries is positively related to DFC and temperature. Td and environmental factors explain up to 64% of CF variability. However, pandemic containment measures may explain the remaining variability.

Keywords: COVID-19, Temperate countries, Tropical and subtropical countries, Cumulative frequency, Pandemic, Doubling time, Speed of infection spread, Containment measures

Article Details

How to Cite
BERUMEN, Jaime et al. Trends of SARS-Cov-2 infection in 67 countries: Role of climate zone, temperature, humidity, and curve behavior of cumulative frequency on duplication time. Medical Research Archives, [S.l.], v. 8, n. 9, sep. 2020. ISSN 2375-1924. Available at: <>. Date accessed: 14 june 2024. doi:
Research Articles


1. Guan WJ, Ni ZY, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. The New England journal of medicine. Feb 28 2020;doi:10.1056/NEJMoa2002032
2. Zhu N, Zhang D, Wang W, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. The New England journal of medicine. Feb 20 2020;382(8):727-733. doi:10.1056/NEJMoa2001017
3. World Health Organization WHO. Coronavirus disease 2019 (COVID19) Situation report 76.
4. Gutierrez P. Coronavirus world map which countries have the most cases and deaths. Coronavirus updates. April 5-2020, 11.25 UTC. 2020.
5. Worldometer. COVID-19 Coronavirus Pandemic. Accessed April 11th-2020,
6. Kudo E, Song E, Yockey LJ, et al. Low ambient humidity impairs barrier function and innate resistance against influenza infection. Proceedings of the National Academy of Sciences of the United States of America. May 28 2019;116(22):10905-10910. doi:10.1073/pnas.1902840116
7. Sun Z, Thilakavathy K, Kumar SS, He G, Liu SV. Potential Factors Influencing Repeated SARS Outbreaks in China. International journal of environmental research and public health. Mar 3 2020;17(5)doi:10.3390/ijerph17051633
8. de Angel Sola DE, Wang L, Vazquez M, Mendez Lazaro PA. Weathering the pandemic: How the Caribbean Basin can use viral and environmental patterns to predict, prepare and respond to COVID-19. Journal of medical virology. Apr 10 2020;doi:10.1002/jmv.25864
9. World Temperatures — Weather Around The World. 2020.
10. Oliveiros B, Caramelo L, Ferreira NC, Caramelo F. Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases. medRxiv. 2020; preprint doi:
11. Day M. Covid-19: identifying and isolating asymptomatic people helped eliminate virus in Italian village. Bmj. Mar 23 2020;368:m1165. doi:10.1136/bmj.m1165
12. World Health Organization WHO. Q&A: Similarities and differences – COVID-19 and influenza. Accessed April 5th, 2020,
13. Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19) (2020).
14. Serra M. Coronavirus, Castiglione d’Adda è un caso di studio: “Il 70% dei donatori di sangue è positivo”. La Stampa https://wwwlastampait/topnews/primo-piano/2020/04/02/news/coronavirus-castiglione-d-adda-e-un-caso-di-studio-il-70-dei-donatori-di-sangue-e-positivo-138666481. April 2-2020.
15. OECD. Elderly population (indicator). Accessed 4-22-2020, 2020.
16. Haq K, McElhaney JE. Ageing and respiratory infections: the airway of ageing. Immunology letters. Nov 2014;162(1 Pt B):323-8. doi:10.1016/j.imlet.2014.06.009
17. Gruver AL, Hudson LL, Sempowski GD. Immunosenescence of ageing. The Journal of pathology. Jan 2007;211(2):144-56. doi:10.1002/path.2104
18. Meyer KC. Lung infections and aging. Ageing research reviews. Jan 2004;3(1):55-67. doi:10.1016/j.arr.2003.07.002
19. Solana R, Tarazona R, Gayoso I, Lesur O, Dupuis G, Fulop T. Innate immunosenescence: effect of aging on cells and receptors of the innate immune system in humans. Seminars in immunology. Oct 2012;24(5):331-41. doi:10.1016/j.smim.2012.04.008
20. Reporte diario COVID-19. Secretaría de Salud. Gobierno de México. Abril 5 de 2020.
21. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. Mar 28 2020;395(10229):1054-1062. doi:10.1016/S0140-6736(20)30566-3
22. Livingston E, Bucher K. Coronavirus Disease 2019 (COVID-19) in Italy. Jama. Mar 17 2020;doi:10.1001/jama.2020.4344
23. Martens WJ. Climate change, thermal stress and mortality changes. Social science & medicine. Feb 1998;46(3):331-44. doi:10.1016/s0277-9536(97)00162-7
24. Sajadi MM, Habibzadeh P, Vintzileos A, Shokouhi S, Miralles-Wilhelm F, Amoroso A. Temperature, humidity, and latitude analysis to predict potential spread and seasonality for COVID-19 (March 5, 2020). SSRN. 2020;
25. Eccles R. An explanation for the seasonality of acute upper respiratory tract viral infections. Acta Otolaryngol. 2002;122(2):183-91.
26. Alderson MRHT. Season and mortality. Health Trends. 1985;17:87-96.
27. Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. Jama. Mar 13 2020;doi:10.1001/jama.2020.4031
28. World Health Organization WHO. Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). 2020:
29. Li C, Romagnani P, von Brunn A, Anders HJ. SARS-CoV-2 and Europe: timing of containment measures for outbreak control. Infection. Apr 9 2020;doi:10.1007/s15010-020-01420-9
30. Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England journal of medicine. Mar 26 2020;382(13):1199-1207. doi:10.1056/NEJMoa2001316