Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America.

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

Attribution and projections of temperature extreme trends in South America based on CMIP5 models.

Temperature extreme indices were analyzed for five continental regions of southern South America defined according to their climatic characteristics. Gridded observations, reanalysis, and global-coupled climate models from CMIP5 were used with the approach of temperature extreme trend attribution analysis on fixed-threshold and percentile-based temperature extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The largest positive trends are exhibited in the tropical nights index, and a clear anthropogenic signal is evident in the subtropical region. In the subtropical central Andes, there is a decrease in the frost days index and increases in the tropical nights and summer days indices, and an anthropogenic signal is evident. In the Patagonian region, all trends from the historical runs were significant, while the ones from the natural experiment were nonsignificant, showing the marked effect of anthropogenic forcing in this region in the extreme temperature events. Projected changes in extreme indices for the 21st century are consistent with a warming climate, and larger changes are expected in the warm nights index.

Association between weather conditions and the number of patients at the emergency room in an Argentine hospital.

The aim of this paper is to study the relationships between hospital emergencies and weather conditions by analysing summer and winter cases of patients requiring attention at the emergency room of a hospital in the city of Buenos Aires, Argentina. Hospital data have been sorted into seven different diagnostic groups as follows: (1) respiratory, cardiovascular and chest-pain complaints; (2) digestive, genitourinary and abdominal complaints; (3) neurological and psychopathological disorders; (4) infections; (5) contusion and crushing, bone and muscle complaints; (6) skin and allergies and (7) miscellaneous complaints. In general, there is an increase of 16.7% in winter while, for group 2 and group 6, there are more patients in summer, 54% and 75% respectively. In summer, the total number of patients for group 6 shows a significant positive correlation with temperature and dew-point temperature, and a negative correlation with the sea-level pressure for the same day. In winter, the same relationship exists, however its correlation is not as strong. The lags observed between these three variables: maximum dew-point temperature, maximum temperature, minimum air pressure and the peaks in admissions are 1, 2 and 4 days respectively. In winter, increases in temperature and dew point and decreases in pressure are followed by a peak in admissions for group 2. In winter, there are significantly more cases in group 5 on warm, dry days and on warm, wet days in the summer.