Predictive Modeling of Diseases Transmitted by Mosquitoes in the Brazilian Context: An Approach with Neural Networks and Machine Learning

• Grant number: 24/08607-6
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: January 01, 2025
• End date: December 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Leopoldo André Dutra Lusquino Filho
• Grantee: Lucas Martins Morello
• Host Institution: Instituto de Ciência e Tecnologia. Universidade Estadual Paulista (UNESP). Campus de Sorocaba. Sorocaba , SP, Brazil

Abstract

Illnesses such as malaria, leishmaniasis, yellow fever and dengue fever represent significant challenges for public health in Brazil, especially due to the tropical climate and the country's socioeconomic profile. With climate change underway, there is a tendency for these conditions to worsen. Given this scenario, it is essential to investigate how global warming and socioeconomic factors can influence the health situation in Brazil.To deal with the complexity and large volume of data coming from studies on this topic and its non-linear correlations, the use of machine learning techniques is highly recommended. These computational approaches enable the creation of predictive models and the analysis of relationships between input data, helping to understand the patterns and trends present in the data.The objective of this project is to investigate the impact of variables related to health and the environment in Brazilian municipalities. Through advanced data analysis and mathematical modeling techniques, positive spatial autocorrelations will be identified between morbidity and mortality, socioeconomic factors and greenhouse gas emissions. This analysis will make it possible to predict future morbidity and mortality scenarios based on the relationships between morbidity and mortality rates and various indicators. This information will contribute to the development of effective disease prevention and control strategies in Brazil, providing valuable insights for public health managers, researchers and others interested in the area.