Discipline III · INSECAB Analytical Framework
When an outbreak appears, you are the one who produces the model. Mathematical models and computational tools to analyse transmission dynamics, combine surveillance data with simulation and support public health decisions with quantitative evidence.
Classical epidemiological surveillance rests on case recording and notification. Computational epidemiology complements that approach with models that make it possible to explore scenarios, estimate trends and quantify the uncertainty attached to predictions.
The training brings into the classroom the team's experience and methodology from INSECAB's active European projects, worked through on analogous cases and public data.
Active projects in this discipline
Cross-border Epidemiological Surveillance
Risk modelling for cross-border biothreats. In collaboration with international reference centres.
Biomap-EU
Mapping of harmonised European biodata for translational research in molecular epidemiology.
Methods and applications
Mathematical modelling of the transmission dynamics of infectious diseases. Estimation of R₀, R(t) and herd immunity thresholds. Adaptation to conditions with latent periods, asymptomatic carriers and reinfections.
Systems based on sentinel signals and active surveillance data. Integration of heterogeneous sources for the early detection of signals consistent with outbreaks.
Geospatial clustering analysis, hotspot detection and modelling of the spatial spread of disease. Production of risk maps for public health decision-making.
Design and analysis of quasi-experimental studies to evaluate the impact of health policies, vaccination campaigns or control measures in real populations.
Building and analysing contact networks to characterise transmission patterns, identify highly connected nodes and explore possible intervention points.
Other disciplines in the INSECAB framework