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Objective. To analyze the role of temperature, humidity, date of first case diagnosed (DFC) and the 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. Design. Retrospective incident case study. Setting. WHO based register of cumulative incidence of Covid-19 cases. Participants. 1,706,914 subjects diagnosed between 12-29-2019 and 4-15-2020. Exposures. SARS-Cov-2 virus, ambient humidity, temperature and climate areas (temperate, All rights reserved. No reuse allowed without permission. : medRxiv preprint tropical/subtropical). Main outcome measures. Comparison of DCS, D100, ΔDD, DFC, humidity, temperature, Td for the first (Td10) and second (Td20) ten days of the CF growth-curve between countries 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% (r 2 = 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% (r 2 = 0.638) of CF variability towards day 30 of the growth-curve. Conclusions. The larger Td in tropical/subtropical countries is positively related to DFC and temperature. Td and environmental factors explain 64% of CF variability in the best of cases. Therefore, other factors, such as pandemic containment measures, would explain the remaining variability.

El SARS-CoV-2 es un nuevo tipo de coronavirus que posee un genoma de ARN monocatenario de sentido positivo, este virus se detectó por primera vez en diciembre de 2019 en la ciudad de Wuhan, China y causa la enfermedad que se denomina COVID-19. La tasa de mutación en los virus de ARN es extremadamente alta, el SARS-CoV-2 posee un mecanismo que corrige los errores en la replicación, por lo tanto, su tasa de mutación es menor. Sin embargo, a pesar de este mecanismo comete errores que generan un amplio espectro de mutaciones dentro de las cuales hay una población dominante, esto le confiere la capacidad de propagarse rápidamente, generando las variantes virales. Cuando surgen estas variantes se generan diferencias genéticas que en ocasiones no tienen efecto alguno, pero en otras le confieren un mayor potencial de transmisión, cargas virales más altas, mayor letalidad, además de permitirles evadir la respuesta inmunológica. En esta revisión presentamos el estado del arte de las nuevas variantes virales de SARS-CoV-2 reportadas hasta el momento en todo el mundo, así como sus características e impacto en la salud pública.

No abstract

, a new epidemic of severe acute respiratory syndrome (SARS) virus (SARS-CoV-2) broke out in China. The World Health Organization (WHO) denominated this novel viral as coronavirus disease-2019 (COVID-19) and declared it a pandemic by March 11 th-2020. 1-3 The SARS-CoV-2 spread from China to other Asian countries including Thailand, South Korea and Japan and then Australia. Subsequently, new cases appeared in Europe, the United States, Canada, and Iran, and by the end of February 2020 there were already cases in Brazil, Mexico, Greece, and Norway, among others. On March 13, there was an increase in the number of cases in western Abstract 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% (r 2 = 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% (r 2 = 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.