Building a spatial-temporal fairness model in social and social-media analysis

• Grant number: 25/08256-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2025
• End date: June 30, 2027
• Field of knowledge: Health Sciences - Collective Health - Epidemiology
• Principal Investigator: Francisco Chiaravalloti Neto
• Grantee: Erick Manuel Delgado Moya
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 23/10080-3 - Improving the arbovirus surveillance and control program's management practices: developing an integrated data platform using Big Data, analysis models, and data visualization, AP.PP

Abstract

Arboviruses, or arthropod-borne viruses, are a global public health problem, especially in tropical and subtropical regions. Common examples include dengue, yellow fever, chikungunya, and Zika virus, all transmitted by mosquitoes. These viruses are responsible for thousands of cases and deaths each year, and their geographic distribution and transmission patterns are constantly evolving. Reaction-diffusion mathematical models allow us to capture the complex interplay between viral replication, host immune responses, and vector dynamics, enabling researchers to analyze infection dynamics, predict outcomes, and develop control strategies. In this project, we aim to build a reaction-diffusion mathematical model to study the spread of arboviruses. The data collected belong to the municipality of Sao Jose do Rio Preto (SJRP) due to the complex epidemiological situation it is experiencing, and come from the Notifiable Diseases Information System (SINAN) and the Aedes Surveillance and Control System (SISAWEB). However, they present a wide variety of formats and contents, requiring the processing of these data. Using these data, we will perform parameter estimation and computational simulations of the reaction-diffusion model and propose a Bayesian space-time model. This approach is capable of analyzing the space-time behavior of viruses and also producing reliable predictions. Using both modeling techniques, we will obtain information about the transmission and spread of the virus and will be able to monitor the occurrence of arboviruses in SJRP and will allow us to evaluate the impact of current interventions and plan future control strategies. (AU)