Spatiotemporal modelling
Understanding how infectious diseases spread over both space and time is essential for designing effective interventions. At SIMID, we specialize in spatiotemporal modelling to quantify regional and temporal variability in transmission dynamics and health outcomes. Our work supports more granular, data-driven public health decision-making.
Our models integrate various data types—including surveillance records, self-reported symptoms, and environmental exposures—into flexible spatial and spatiotemporal frameworks. This includes both classical and Bayesian approaches, allowing us to address uncertainty and to adapt to data limitations common in real-world settings.
We develop and apply methods to detect regional disparities in disease burden, identify spatial patterns in excess mortality (Natalia et al., 2024), and explore the influence of sample imbalance on disease surveillance (Rozo Posada et al., 2024).
Our recent work has shown how fine-scale spatial heterogeneity can affect biomarker performance in liver disease diagnostics (Claes et al., 2024) and asthma incidence modelling in urban settings (Vandeninden et al., 2023).
Bayesian hierarchical models have been used to jointly analyze self-reported COVID-19 symptoms and confirmed incidence data, improving both inference and predictive accuracy (Vranckx et al., 2023). We have also investigated how resolution affects model performance and prediction strength, and studied the lagged association between environmental stressors and respiratory illness risk (Rutten et al., 2025).
Beyond infectious disease, our modelling supports environmental health assessments, such as estimating the impact of traffic policies on asthma or mapping mortality clusters linked to pollution (Vandeninden et al., 2024).
Spatiotemporal modelling at SIMID enables high-resolution, data-integrated insights to guide targeted and equitable health interventions.