Research

Understanding and Predicting the Spread of Emerging Infectious Diseases

Emerging and re-emerging infectious diseases (EIDs) pose a significant and worldwide threat to the health of domesticated animals, wildlife, and humans. Recent advancements in techniques and data availability have expanded the role of modelling and forecasting in responding to these emerging threats. Using a One Health perspective, my research uses statistical and mathematical models to to generate reliable forecasts related to the (re)emergence, dynamics, and spread of EIDs and to evaluate the effectiveness of different intervention strategies;

The Role of Biases in Infectious Disease Models and Vaccine Effectiveness Studies

Biases and uncertainties can undermine our understanding of epidemiological processes and phenomena. Recently, I have been exploring how both contact patterns and testing differences can impact results of infectious disease models and measurements of vaccine effectiveness (i.e. how effective a vaccine is at preventing infection or symptomatic infection in the real world). In the figure above, we show how vaccinated contact heterogeneity (i.e. higher contact among vaccinated individuals) can cause observed vaccine effectiveness to be underestimated, and in some cases, appear negative.

Building Forecasting Capacity

How do we build better forecasts in a changing world and improve how we integrate them into decision-making? This requires good forecasts, strong collaborations and effective communication. In my research, I am interested in both the tools that improve forecasts but also those that help to increase transparency in the modelling process. I am also particularly interested in exploring how forecasting models can be optimized for decision support, I co-lead a Canadian-based network  that seeks to address the main gaps in the science-to-policy process in the Canadian context and am leading a short summer course on the topic. Please check out our website here!