Data: 29 de setembro de 2025

Horário: 14h30

Local: Online

Transmissão: https://meet.google.com/dtx-csjj-rhd

Resumo

Dengue fever, transmitted primarily by infected female Aedes aegypti mosquitoes, underwent a sharp global surge in recent years with record-breaking incidence and more regions becoming endemic. Predictive computational modeling of dengue disease incidence and Ae. aegypti abundance can help local authorities and policymakers develop situational awareness and manage interventions. In this talk, I will discuss ongoing and future efforts in computational modeling of dengue and its main vector, Ae. aegypti, with insights from both mechanistic and statistical approaches. In particular, I will present a new method for short-term forecasts of Ae. aegypti relative abundance in urban areas. This method is based on the renewal equation, coupled with a compartmental model and a Bayesian framework to infer past trends and project them 1-4 weeks into the future. When applied to data from four jurisdictions of the United States, it achieved 83-92% coverage of the 90% interquantile range, outperforming a naïve baseline method in most scenarios. I will also briefly show insights from other projects developed along with the CEPH Lab team at Indiana University and our collaborators. Finally, I will conclude by outlining future perspectives on predictive mathematical models of dengue and Ae. aegypti, inviting discussion on how modeling can help address the increasing public health challenges of dengue fever.