Research
Modelling and data analysis are necessary to support decisions and advocacy efforts. Models are the formalisms that translate data into quantities that answer questions. I develop models to fit to data, to estimate key parameters, and to resolve unknown, implicit, critical assumptions. I develop theory that demonstrates the power of mechanistic mathematical models in data analysis. I am also developing resources to help experts in adjacent disciplines learn about the power of modelling.
Population dynamics
My research is in population biology, mathematical modelling, evolutionary epidemiology, species range shifts, infectious diseases and public health.
My group’s past work has added in physiology and temperature dependence to describe seasonal population dynamics and species ranges shifts in response to climate warming. We have integrated dispersal, movement, and genetic diversity into models to describe spatial dynamics and trait evolution. Our work has been applied to salmon parasites, tiny clams, rabies, drug resistance in hospitals, SARS-CoV-2, bumblebees, wolves, moths, and butterflies.
COVID-19 modelling
Population biology is a field that has contributed substantially to our knowledge of how infectious diseases spread. The connections between the fields of population biology and infectious disease dynamics are well-noted.
Our modelling work during the COVID-19 pandemic began with a technical report that was included in witness testimony when travel measures implemented under the public health emergency were challenged in provincial court. Our technical report showed that travel measures reduced clinical COVID-19 cases in Newfoundland and Labrador by 92%. This work is now peer-reviewed, published, and cited in a World Health Organization report for the Western Pacific Region. For a thorough retrospective analysis of the travel measures implemented in Newfoundland and Labrador during the pandemic see Mohammadi et al..
The travel measures remained in place after the court decision, and subsequently during the COVID-19 pandemic, Newfoundland and Labrador implemented a containment strategy. Most SARS-CoV-2 infections were reported in travelers and were contained without spread to members of the Newfoundland and Labrador community. The lack of community spread meant that classic models such as Susceptible-Infected-Recovered, and widely adopted methods for calculating dynamic reproduction numbers were not appropriate without modification to the Newfoundland and Labrador situation. We needed to extend importation modelling and use different summary statistics to determine the effectiveness of public health measures and the capacity to relax measures given increasing vaccination levels in the Newfoundland and Labrador community. This work is peer-reviewed and published in this theme issue on Modelling COVID-19 and Preparedness for Future Pandemics.
The technical annex to a World Health Organization update (July 2021) establishes regional factors as considerations to determine whether travel measures are appropriate (see bullet points at the bottom of p2). Our publication describes regional considerations that determine whether an elimination strategy is appropriate, where implementing an elimination strategy potentially requires implementing travel measures. Particularly, we emphasize that the recommended strategy, elimination or mitigation, may not be the same for all Canadian jurisdictions. This work is part of the Royal Society Open Science, Science, Society, and Policy collection. International Advisory Board members for this collection include Professor Mona Nemer, Chief Science Adviser to the Prime Minister of Canada.
We analyzed the results of Rapid Antigen Tests to understand underreporting, and inform some of our ongoing modelling.
I am a member of the Canadian small jurisdictions working group, a group that advances modelling frameworks to support public health in small jurisdictions. Here are some recommended directions for pandemic preparedness. My group models infectious disease dynamics using macpan2.
During the COVID-19 pandemic I was quoted in the British Medical Journal, the New York Times, the Globe and Mail, and local media such as NTV, the Canadian Broadcasting Corporation, and The Telegram. I appeared in a Fields Institute Panel. I was lead organizer for the AARMS-EIDM Summer School in Modelling Infectious Diseases.
Lessons from the COVID-19 pandemic
Arising from my experience doing mathematical modelling during the public health emergency, I realize that mathematical modelling matters and that modellers need to be included in decision support. During the public health emergency, modelling is what the public and decision-makers wanted, and modellers worked to produce the necessary analysis. It is necessary to advocate for the value of mathematical modelling due to the many meaningful contributions that were made during the COVID-19 pandemic. Outside of an emergency, this means training and advancing methods in infectious disease modelling as these skills are highly transferable to emergency work, and we need to have the built the capacity to respond.
Publications
For my publications, see Google Scholar.
When financially possible, our publications are open access. If you can’t access a publication, please email me. Below are links to publications that may be more difficult to access. You might also search MedRxiv and BioRxiv for author-typeset copies of the publications.