The impact of air pollution on COVID-19 pandemic varied within different cities in South America using different models.

There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results were not consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) was calculated to assess the spread of COVID-19. Two frequently used models, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. According to our research results, even for the same region, varied models gave inconsistent results. For example, in Sao Paulo, both models show SO2 and O3 are significant independent variables, however, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. Moreover, in the case of multiple regions, currently used models should be selected according to local conditions. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which will help states, health practitioners, and policy makers in combating the COVID-19 pandemic in South America.

Meteorological impact on the COVID-19 pandemic: A study across eight severely affected regions in South America.

The role of meteorological factors in the transmission of the COVID-19 still needs to be determined. In this study, the daily new cases of the eight severely affected regions in four countries of South America and their corresponding meteorological data (average temperature, maximum temperature, minimum temperature, average wind speed, visibility, absolute humidity) were collected. Daily number of confirmed and incubative cases, as well as time-dependent reproductive number (Rt) was calculated to indicate the transmission of the diseases in the population. Spearman's correlation coefficients were assessed to show the correlation between meteorological factors and daily confirmed cases, daily incubative cases, as well as Rt. In particular, the results showed that there was a highly significant correlation between daily incubative cases and absolute humidity throughout the selected regions. Multiple linear regression model further confirmed the negative correlation between absolute humidity and incubative cases. The absolute humidity is predicted to show a decreasing trend in the coming months from the meteorological data of recent three years. Our results suggest the necessity of continuous controlling policy in these areas and some other complementary strategies to mitigate the contagious rate of the COVID-19.