Utilization of machine learning for dengue case screening.

Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.

Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis.

Although leptospirosis is endemic in most Brazilian regions, South Brazil shows the highest morbidity and mortality rates in the country. The present study aimed to analyze the spatial and temporal dynamics of leptospirosis cases in South Brazil to identify the temporal trends and high-risk areas for transmission and to propose a model to predict the disease incidence. An ecological study of leptospirosis cases in the 497 municipalities of the state of Rio Grande do Sul, Brazil, was conducted from 2007 to 2019. The spatial distribution of disease incidence in southern Rio Grande do Sul municipalities was evaluated, and a high incidence of the disease was identified using the hotspot density technique. The trend of leptospirosis over the study period was evaluated by time series analyses using a generalized additive model and a seasonal autoregressive integrated moving average model to predict its future incidence. The highest incidence was recorded in the Centro Oriental Rio Grandense and metropolitan of Porto Alegre mesoregions, which were also identified as clusters with a high incidence and high risk of contagion. The analysis of the incidence temporal series identified peaks in the years 2011, 2014, and 2019. The SARIMA model predicted a decline in incidence in the first half of 2020, followed by an increase in the second half. Thus, the developed model proved to be adequate for predicting leptospirosis incidence and can be used as a tool for epidemiological analyses and healthcare services.Temporal and spatial clustering of leptospirosis cases highlights the demand for intersectorial surveillance and community control policies, with a focus on reducing the disparity among municipalities in Brazil.