Spatialized PM2.5 during COVID-19 pandemic in Brazil's most populous southern city: implications for post-pandemic era.

Brazil has experienced one of the highest COVID-19 fatality rates globally. While numerous studies have explored the potential connection between air pollution, specifically fine particulate matter (PM2.5), and the exacerbation of SARS-CoV-2 infection, the majority of this research has been conducted in foreign regions-Europe, the United States, and China-correlating generalized pollution levels with health-related scopes. In this study, our objective is to investigate the localized connection between exposure to air pollution exposure and its health implications within a specific Brazilian municipality, focusing on COVID-19 susceptibility. Our investigation involves assessing pollution levels through spatial interpolation of in situ PM2.5 measurements. A network of affordable sensors collected data across 9 regions in Curitiba, as well as its metropolitan counterpart, Araucaria. Our findings distinctly reveal a significant positive correlation (with r-values reaching up to 0.36, p-value < 0.01) between regions characterized by higher levels of pollution, particularly during the winter months (with r-values peaking at 0.40, p-value < 0.05), with both COVID-19 mortality and incidence rates. This correlation gains added significance due to the intricate interplay between urban atmospheric pollution and regional human development indices. Notably, heightened pollution aligns with industrial hubs and intensified vehicular activity. The spatial analysis performed in this study assumes a pivotal role by identifying priority regions that require targeted action post-COVID. By comprehending the localized dynamics between air pollution and its health repercussions, tailored strategies can be implemented to alleviate these effects and ensure the well-being of the public.

Prediction of the occurrence of leprosy reactions based on Bayesian networks.

Introduction: Leprosy reactions (LR) are severe episodes of intense activation of the host inflammatory response of uncertain etiology, today the leading cause of permanent nerve damage in leprosy patients. Several genetic and non-genetic risk factors for LR have been described; however, there are limited attempts to combine this information to estimate the risk of a leprosy patient developing LR. Here we present an artificial intelligence (AI)-based system that can assess LR risk using clinical, demographic, and genetic data. Methods: The study includes four datasets from different regions of Brazil, totalizing 1,450 leprosy patients followed prospectively for at least 2 years to assess the occurrence of LR. Data mining using WEKA software was performed following a two-step protocol to select the variables included in the AI system, based on Bayesian Networks, and developed using the NETICA software. Results: Analysis of the complete database resulted in a system able to estimate LR risk with 82.7% accuracy, 79.3% sensitivity, and 86.2% specificity. When using only databases for which host genetic information associated with LR was included, the performance increased to 87.7% accuracy, 85.7% sensitivity, and 89.4% specificity. Conclusion: We produced an easy-to-use, online, free-access system that identifies leprosy patients at risk of developing LR. Risk assessment of LR for individual patients may detect candidates for close monitoring, with a potentially positive impact on the prevention of permanent disabilities, the quality of life of the patients, and upon leprosy control programs.

Prediction of the occurrence of leprosy reactions based on Bayesian networks

Introduction: Leprosy reactions (LR) are severe episodes of intense activation of the host inflammatory response of uncertain etiology, today the leading cause of permanent nerve damage in leprosy patients. Several genetic and non-genetic risk factors for LR have been described; however, there are limited attempts to combine this information to estimate the risk of a leprosy patient developing LR. Here we present an artificial intelligence (AI)-based system that can assess LR risk using clinical, demographic, and genetic data. Methods: The study includes four datasets from different regions of Brazil, totalizing 1,450 leprosy patients followed prospectively for at least 2 years to assess the occurrence of LR. Data mining using WEKA software was performed following a two-step protocol to select the variables included in the AI system, based on Bayesian Networks, and developed using the NETICA software. Results: Analysis of the complete database resulted in a system able to estimate LR risk with 82.7% accuracy, 79.3% sensitivity, and 86.2% specificity. When using only databases for which host genetic information associated with LR was included, the performance increased to 87.7% accuracy, 85.7% sensitivity, and 89.4% specificity. Conclusion: We produced an easy-to-use, online, free-access system that identifies leprosy patients at risk of developing LR. Risk assessment of LR for individual patients may detect candidates for close monitoring, with a potentially positive impact on the prevention of permanent disabilities, the quality of life of the patients, and upon leprosy control programs.