Frequency of antibiotic prescribing in adults hospitalized for SARS-CoV-2 in a high complexity institution in Santiago de Cali, Colombia.

Objective: To determine the frequency of antibiotic use and to know which clinical and socio-demographic variables were related to the probability of suffering infections associated with COVID-19. Method(s): Adults hospitalized for COVID-19 who received one or more antibiotics during hospitalization were evaluated. We performed a descriptive analysis of variables in the general population' bivariate analysis in two groups (documented vs. suspected infection) and multivariate logistic regression of factors associated with mortality. Result(s): It was determined that 60.4% of adults hospitalized for COVID-19 received antibiotics. Coinfection was documented in 6.2% and superinfection in 23.3%. Gram-negative germs were reported in 75.8% of cultures, fungi in 17.8% and gram-positive in 14.2%. Variables such as age, comorbidities, ICU, anemia, steroids, mechanical ventilation, hemofiltration were statistically significantly related to documented infection. High-flow cannula was associated as a protective factor. Overall mortality was 43.9%, 57.8% in the first group and 38.1% in the second (p=0.002). Conclusion(s): There is a considerable frequency of antibiotic use in subjects hospitalized for COVID-19, particularly related to relevant findings of bacterial superinfection, in those with comorbidities, such as diabetes mellitus, immunosuppression, anemia and fragility, in whom the behavior of the disease is more severe and lethal.Copyright © 2023 Asociacion Colombiana de Infectologia. All rights reserved.

Forecasting the COVID-19 Space-time Dynamics in Brazil with Convolutional Graph Neural Networks and Transport Modals

One of the major challenges imposed by the SARS-CoV-2 pandemic is the lack of pattern in which the virus spreads, making it difficult to create effective policies to prevent and tackle the pandemic. Several approaches have been proposed to understand the virus behavior and anticipate its infection and death curves at country ans state levels, thus supporting containment measures. Those initiatives generalize well for general extents and decisions, but they do not predict so well the trajectory of the virus through specific regions, such as municipalities, considering their distinct interconnection profiles. Specially in countries with continental dimensions, like Brazil, too general decisions imply that containment measures are applied either too soon or too late. This study presents a novel scalable alternative to forecast the numbers of case and death by SARS-CoV-2, according to the influence that certain regions exert on others. By exploiting a single-model architecture of graph convolutional networks with recurrent networks, our approach maps the main access routes to municipalities in Brazil using the modals of transport, and processes this information via neural network algorithms to forecast at the municipal level ans for the whole country. We compared the performance in forecasting the pandemic daily numbers with three baseline models using Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (sMAPE) and Normalized Root Mean Square Error (NRMSE) metrics, with the forecasting horizon varying from 1 to 25 days. Results show that the proposed model overcomes the baselines when considering the MAE and NRMSE (p ˂0.01), being specially suitable for forecasts from 14 to 24 days ahead. Author

Teaching practices for enhancing self-regulated learning during the COVID-19 pandemic: Measuring scales and predictive model

The main objectives of this research study were to examine the factorial structure of teaching-variable scales to enhance self-regulated learning in students and to estimate a predictive model for teaching practices during the COVID-19 pandemic. The methodology applied contained a psychometric and a predictive design for each phase of the study. A total of 765 professors from six Chilean universities were surveyed. The results showed four scales (beliefs, self-efficacy, knowledge, and teaching practices) with adequate psychometric properties. The variables “self-regulated learning knowledge” and “self-efficacy for promoting self-regulated learning” had indirect effects on the variable “teaching practices for promoting self-regulated learning”, which was a partial mediation type. The estimated mediation model accurately predicted 33.7% of the teaching practices used for boosting self-regulated learning. In conclusion, the present study developed valid and reliable scales along with a predictive model with adequate adjustment that served to improve the understanding of self-regulated learning enhanced by university professors. © 2022