Population health and economic decision-making problems are characterized by sparse, biased, and heterogeneous data, coupled with strong structural constraints imposed by demography, disease natural history, and health-system processes.
We focus on mechanistic, data-informed dynamical models that enable inference, extrapolation, and uncertainty quantification at the population level. Rather than relying on purely statistical trend fitting, these models explicitly encode disease dynamics, intervention effects, and health-system structure.
We have developed and applied compartmental, agent-based, and state-space models for infectious disease epidemiology, with a focus on chronic viral infections and emerging outbreaks. These models integrate surveillance data, cohort studies, and administrative health records to infer latent disease states, prevalence, and transmission dynamics.
A recurring theme of this work is identifiability under partial observability—quantifying what aspects of disease burden and transmission can be inferred reliably, and where uncertainty is irreducible due to data limitations.
Our tools:
Bayesian back-calculation for prevalence estimation
State-space and latent-variable models
Integration of multiple imperfect data sources
Scenario analysis for public-health planning
In pharmacoeconomics, we have developed model-based frameworks to evaluate the cost-effectiveness and budget impact of therapeutic interventions under uncertainty. These models link epidemiological dynamics with treatment uptake, health outcomes, and economic endpoints to support evidence-based decision-making.
This work emphasizes propagation of structural and parameter uncertainty from epidemiological models into economic outcomes, enabling transparent assessment of confidence in cost-effectiveness conclusions.
Our tools:
Cost-effectiveness and budget-impact modeling
Model-based evaluation of intervention strategies
Sensitivity and uncertainty analysis
Support for reimbursement and policy decisions
A Model-Based Framework for Chronic Hepatitis C Prevalence Estimation, PLoS ONE, 2019
Estimating Chronic Hepatitis C Prevalence in Canada Using Population-Based Cohort Studies, Journal of Viral Hepatitis, 2020
Estimation of COVID-19 Period Prevalence and the Undiagnosed Population in Canadian Provinces, JMIR Public Health and Surveillance, 2021
Cost-Effectiveness Analysis of Sofosbuvir and Velpatasvir in Chronic Hepatitis C, Journal of Viral Hepatitis, 2021