Seasonal flu places a heavy burden on human populations and healthcare systems, thus, require permanent surveillance. Current surveillance methods are robust yet slow. With the collaboration of national and international public health institutions, we are developing models that can timely predict flu levels by using a combination of offline and online data (such as search trends and social media shares).
Team
- Sara Mesquita
- Cláudio Haupt-Vieira (past member)
- Miguel Won (past member)
Publications
- Won M, Louro C, Pita MM, Gonçalves-Sá J, “Early and Real-Time Detection of Seasonal Influenza Onset”, (2017), PLoS Comput Bio 13 (2)
- Haupt-Vieira C, “Online behavioral patterns in a health crisis setting”, MSc thesis
- Cortes J, “Can We Improve the Prediction of the Influenza Onset and Save Money in the Process?”, MSc thesis
Funding
Fundação para a Ciência e Tecnologia:
- PTDC IVC ESCT 5337 2012
-
DSAIPA/AI/0087/2018 (Projeto Piloto em Ciência dos Dados e Inteligência Artificial na Administração Pública)
