Research

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.

Drivers of Ecological Network Structure

 I am interested in understanding the environmental and biological mechanisms that shape the structure of ecological networks. I am also equally intrigued by the sampling and construction decisions that influence our observations of these networks. My work aims to model and control each of these components when studying ecological networks.

Forecasting Ecological and Epidemiological Dynamics

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!

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