Method & model development
SIMID develops and applies innovative statistical methods and mathematical models to analyze infectious disease dynamics. Our work spans a broad methodological spectrum, combining theoretical development with practical relevance for public health.
We work extensively with compartmental models such as SIR and SEIR, including age-structured and stochastic extensions. These frameworks are tailored to incorporate behavioral, serological, and demographic heterogeneities, enabling us to evaluate intervention strategies and forecast epidemic trajectories under varying assumptions.
In addition to traditional modeling techniques, we incorporate stochastic elements to account for randomness in disease transmission. This is particularly important in small populations or the early stages of outbreaks, where chance events can strongly influence observed dynamics.
We develop efficient data augmentation techniques for inference in partially observed epidemics, as demonstrated in our recent work on Bayesian smoothing approaches for real-time epidemic surveillance (Epidemics, 2024).
Our modeling strategies are widely applicable. Examples include optimizing age-targeted COVID-19 interventions (Statistical Modelling, 2024), modeling waning immunity in vaccine evaluations (Vaccine, 2023), addressing unobserved heterogeneity with frailty models (Frailty Models in Survival Analysis, Wiley, 2023), or simulating malaria transmission in high-burden areas (Malaria Journal, 2025).
As infectious disease threats evolve, so does the need for sound, quantitative approaches to inform response. At SIMID, we view mathematics as a vital instrument for tackling pressing societal challenges, particularly in public health. Through rigorous modeling, we aim to support evidence-based, impactful decisions.