Surveillance & case data
Robust surveillance data are essential to track disease trends, evaluate interventions, and validate predictive models. At SIMID, we use real-time and historical data—including case counts, hospitalizations, mortality, and treatment records—to monitor outbreaks and inform public health response.
Our models are routinely calibrated and validated against surveillance data to ensure accurate forecasting and scenario evaluation. This data-driven approach supports timely decision-making during crises and long-term planning for endemic conditions.
We contribute to international efforts to track infectious disease dynamics, including the analysis of social contact and behavior data from 21 European countries over two years of the COVID-19 pandemic. This work helps contextualize transmission patterns in relation to behavioral change and policy measures (Wong et al., 2023).
We also analyze health system indicators to track broader trends. For example, our research on antibiotic use in Europe revealed a post-pandemic rebound in community antibiotic consumption and distinct seasonal patterns (Vermeulen et al., 2024)—an important signal for antimicrobial resistance monitoring.
We support surveillance-informed vaccine evaluation. In the Democratic Republic of the Congo, SIMID researchers contributed to the assessment of a heterologous Ebola vaccine regimen among frontline workers, using clinical safety and immunogenicity data to inform deployment strategies (Lariviere et al., 2024).
By grounding our models in high-quality surveillance data, SIMID ensures that its outputs are relevant, timely, and responsive to public health needs across diverse settings.