Research

My research is in population biology, mathematical modelling, evolutionary epidemiology, species range shifts, infectious diseases and public health.

I am interested in the biological questions. I like to learn new mathematical frameworks, so there are fewer technical barriers to answering the biological questions I am interested in.

COVID-19 modelling

  • We analyzed Rapid Antigen Test results and found that in January 2022, 1 in 4.3 households positive for COVID-19 were captured in Newfoundland and Labrador provincial case counts. This is notable as this under-reporting ratio is lower than for other Canadian provinces, and there are few data of this type for school-aged children in Canada.

  • We developed a framework that extends importation modelling by considering characteristics of the local population, such as vaccination status and non-pharmaceutical interventions. This framework can be used to inform re-opening policies in regions implementing an elimination strategy, where few such frameworks currently exist.

  • We found that travel to Newfoundland and Labrador decreased by 82\(\%\) during the pandemic, and that reporting travel-related cases is essential to accurately model importations.

  • We provide key criterion to inform the decision of whether a mitigation or elimination strategy is recommended to manage a pandemic.

I have been 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.

What next?

In 1997, cross-fertilization of ideas between population biology and epidemiology was described. Given the skills of population biologists and the need for modelling in public health, as highlighted by the COVID-19 pandemic, we need to work to bring these fields closer together.

When working in human health, mathematical modellers need to do better considering health equity; or alternatively, do better in explaining the data or computational limitations that are barriers to doing so.

Mathematical modellers need to give better guidance regarding which modelling approaches are best for what questions. At present, it is very easy to learn coding without any training in modelling to learn the different types of models. When you only have a hammer, everything looks like a nail. Inevitably, this approach will produce bad results for some questions.

We need to develop mathematical modelling frameworks that can recommend elimination strategies. This requires carefully choosing and interpreting model results. We also need to realize that an elimination strategy is a coordinated, synergistic strategy involving travel measures, non-pharmaceutic interventions conditional on community cases, and a plan for a strong vaccination campaign when vaccines become available. Currently, we could be making recommendations treating all the measures available to public health decision-makers as independent.

We should work for more synergy between data science and science, and machine learning, statistics, and mechanistic modelling approaches.

If you agree or disagree with any of this, let me know what you think!

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.