Dengue surveillance and control actions: a space-time analysis in a medium-sized municipality in the state of São Paulo.

• Grant number: 24/13664-9
• Support Opportunities: Scholarships abroad - Research
• Start date: January 21, 2025
• End date: April 20, 2025
• Field of knowledge: Health Sciences - Collective Health - Public Health
• Principal Investigator: Gerson Laurindo Barbosa
• Grantee: Gerson Laurindo Barbosa
• Host Investigator: Monica Pirani
• Host Institution: Instituto Pasteur (IP). Coordenadoria de Controle de Doenças (CCD). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
• Institution abroad: Imperial College London, England

Abstract

Dengue fever remains a major infectious disease in tropical and subtropical regions, with an alarming increase in cases in the Americas from 2020 to 2024. Brazil, particularly in 2024, is facing its largest epidemic, with more than 4 million cases recorded by June. The dengue vector, Aedes aegypti, proliferates in high population densities and favorable climate conditions. In addition to dengue, other arboviruses such as Zika and Chikungunya are also transmitted by this mosquito, further worsening the public health scenario and highlighting the need for more effective vector control strategies.This study aims to analyze the transmission of dengue fever within the municipality of São José do Rio Preto and its surrounding municipalities between 2012 and 2022, focusing on vector control actions, investments in health, and human mobility. The research seeks to understand how the epidemic develops and spreads regionally, using explanatory and predictive models to improve decision-making and strategic investments in disease control. The goal is to identify the relationship between the actions of the control program, financing, and human mobility in the occurrence and spread of dengue.The study aims to develop technological tools, such as thematic maps and management reports, to support managers in visualizing and interpreting data, facilitating the implementation of more effective control measures. The challenges include evaluating the effectiveness of vector control program actions, resource allocation, and the influence of mobility on the spread of the disease. The models will be built using a Bayesian approach, employing latent Gaussian models with INLA (Integrated Nested Laplace Approximations) to produce robust results that can guide public policies and vector control strategies.