DEP - Apresentações orais em encontros internacionais
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Percorrer DEP - Apresentações orais em encontros internacionais por Domínios Científicos e Tecnológicos (FOS) "Ciências Naturais::Matemáticas"
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- High-Dose Quadrivalent vs Standard Dose Influenza Vaccine Effectiveness using EHR in PortugalPublication . Brito, André; Soares, Patrícia; Gómez, Verónica; Rodrigues, Ana Paula; Leite, Andreia; Machado, AusendaThis study presents an updated protocol to assess the effectiveness of the high-dose quadrivalent influenza vaccine (HD-QIV) in preventing hospitalisation due to influenza-like illness among older adults in Portugal. Using routinely collected electronic health records, we will conduct a retrospective cohort study covering the 2022/23 to 2025/26 influenza seasons, including residents of long-term care facilities and community-dwelling individuals eligible for HD or standard-dose vaccination. Vaccine exposure will be defined 14 days post-administration, and outcomes will include first hospitalisation related to influenza or associated complications. Relative and absolute vaccine effectiveness will be estimated using Cox proportional hazards models. Key challenges include potential misclassification of residence and outcomes, uneven vaccine rollout, and small sample sizes in some groups; we will address these through sensitivity analyses, spatio-temporal assessments, and propensity-score matching where appropriate. The study will provide timely evidence on HD-QIV performance in real-world conditions, supporting national vaccination strategies for high-risk populations. This was a presentation made at the annual VEBIS Consortium LOT 4.
- Modelling Mobility Data during COVID-19 with Neural NetworksPublication . Brito, André; Machado, Ausenda; Rodrigues, Ana Paula; Patrício, Paula; Bispo, ReginaThe COVID-19 pandemic underscored the vital role of mathematical modelling in epidemiology, aiding in forecasting disease trends and informing public health strategies. In Portugal, modelling efforts demonstrated the effectiveness of physical distancing in curbing transmission and provided key insights into the impacts of deconfinement and vaccination on disease burden. The period also saw increased use of non-traditional data, particularly human mobility data, which served as a proxy for contact patterns and helped refine models of disease spread. In this work, we aim to evaluate the predictive performance of neural network models in capturing mobility patterns across Portuguese districts during the COVID-19 pandemic, using data from Google Mobility Reports.