Together with collaborators from the clinic or the lab, we build, study and apply mathematical models to empirical infection data to quantify key infection or intervention parameters, to test hypotheses and to provide new data analysis and predictive tools. We have worked on experimental mice data of: malaria parasite infections (Ramos et al. 2019), trypanosome dynamics across blood and fat compartments (Trindade et al. 2022), and Streptococcus pneumoniae infections in the lung (Costa et al. 2024), using mechanistic modeling approaches.

We have worked on human clinical data of sepsis patients, analyzing physiological biomarkers for prediction of sepsis onset and critical events, under a machine learning approach (Ramos et al. 2021).