[medRxiv, 11 July 2022]

Even with daily case counts, the true scope of the COVID-19 pandemic in Canada is unknown due to undetected cases. We estimate the pandemic scope through a new multi-site model using publicly available disease count data including detected cases, recoveries among detected cases, and total deaths. These counts are used to estimate the case detection probability, the infection fatality rate through time, as well as the probability of recovery, and several important population parameters including the rate of spread, and importation of external cases. We also estimate the total number of active COVID-19 cases per region of Canada for each reporting interval. We applied this multi-site model Canada-wide to all provinces and territories, providing an estimate of the total COVID-19 burden for the 90 weeks from 23 Apr 2020 to 6 Jan 2022. We also applied this model to the five Health Authority regions of British Columbia, Canada, describing the pandemic in B.C. over the 31 weeks from 2 Apr 2020 to 30 Oct 2020.

[Canadian Journal of Statistics, 1 November 2021]

Asymptomatic and pauci-symptomatic presentations of COVID-19 along with restrictive testing protocols result in undetected COVID-19 cases. Estimating undetected cases is crucial to understanding the true severity of the outbreak. We introduce a new hierarchical disease dynamics model based on the N-mixtures hidden population framework. The new models make use of three sets of disease count data per region: reported cases, recoveries and deaths. Treating the first two as under-counted through binomial thinning, we model the true population state at each time point by partitioning the diseased population into the active, recovered and died categories. Both domestic spread and imported cases are considered. These models are applied to estimate the level of under-reporting of COVID-19 in the Northern Health Authority region of British Columbia, Canada, during 30 weeks of the provincial recovery plan. Parameter covariates are easily implemented and used to improve model estimates. We compare two distinct methods of model-fitting for this case study: (1) maximum likelihood estimation, and (2) Bayesian Markov chain Monte Carlo. The two methods agreed exactly in their estimates of under-reporting rate. When accounting for changes in weekly testing volumes, we found under-reporting rates varying from 60.2% to 84.2%.

[Journal of Biological Dynamics, 2 Jun 2022]

Contact tracing is an important intervention measure to control infectious diseases. We present a new approach that borrows the edge dynamics idea from network models to track contacts included in a compartmental SIR model for an epidemic spreading in a randomly mixed population. Unlike network models, our approach does not require statistical information of the contact network, data that are usually not readily available. The model resulting from this new approach allows us to study the effect of contact tracing and isolation of diagnosed patients on the control reproduction number and number of infected individuals. We estimate the effects of tracing coverage and capacity on the effectiveness of contact tracing. Our approach can be extended to more realistic models that incorporate latent and asymptomatic compartments.

Citerank: (22) 679703EIDM?The Emerging Infectious Diseases Modelling Initiative (EIDM) – by the Public Health Agency of Canada and NSERC – aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. [1]7F1CEB7, 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679769Christopher CottonChristopher Cotton is a Professor of Economics at Queen’s University where he holds the Jarislowsky-Deutsch Chair in Economic & Financial Policy.10019D3ABAB, 679776David EarnProfessor of Mathematics and Faculty of Science Research Chair in Mathematical Epidemiology at McMaster University.10019D3ABAB, 679797Huaiping ZhuProfessor of mathematics at the Department of Mathematics and Statistics at York University, a York Research Chair (YRC Tier I) in Applied Mathematics, the Director of the Laboratory of Mathematical Parallel Systems at the York University (LAMPS), the Director of the Canadian Centre for Diseases Modelling (CCDM) and the Director of the One Health Modelling Network for Emerging Infections (OMNI-RÉUNIS). 10019D3ABAB, 679806Jane HeffernanJane Heffernan is a professor of infectious disease modelling in the Mathematics & Statistics Department at York University. She is a co-director of the Canadian Centre for Disease Modelling, and she leads national and international networks in mathematical immunology and the modelling of waning and boosting immunity.10019D3ABAB, 679812Jianhong WuProfessor Jianhong Wu is a University Distinguished Research Professor and Senior Canada Research Chair in industrial and applied mathematics at York University. He is also the NSERC Industrial Research Chair in vaccine mathematics, modelling, and manufacturing. 10019D3ABAB, 679842Mark LewisProfessor Mark Lewis, Kennedy Chair in Mathematical Biology at the University of Victoria and Emeritus Professor at the University of Alberta.10019D3ABAB, 679858Patrick BrownAssociate Professor in the Centre for Global Health Research at St. Michael’s Hospital, and in the Department of Statistical Sciences at the University of Toronto.10019D3ABAB, 679859Patrick LeightonPatrick Leighton is a Professor of Epidemiology and Public Health at the Faculty of Veterinary Medicine, University of Montreal, and an active member of the Epidemiology of Zoonoses and Public Health Research Group (GREZOSP) and the Centre for Public Health Research (CReSP). 10019D3ABAB, 679869Rob DeardonAssociate Professor in the Department of Production Animal Health in the Faculty of Veterinary Medicine and the Department of Mathematics and Statistics in the Faculty of Science at the University of Calgary.10019D3ABAB, 679875Sarah OttoProfessor in Zoology. Theoretical biologist, Canada Research Chair in Theoretical and Experimental Evolution, and Killam Professor at the University of British Columbia.10019D3ABAB, 679890Troy DayTroy Day is a Professor and the Associate Head of the Department of Mathematics and Statistics at Queen’s University. He is an applied mathematician whose research focuses on dynamical systems, optimization, and game theory, applied to models of infectious disease dynamics and evolutionary biology.10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 686724Ellen RaffertyDr. Ellen Rafferty has a Master of Public Health and a PhD in epidemiology and health economics from the University of Saskatchewan. Dr. Rafferty’s research focuses on the epidemiologic and economic impact of public health policies, such as estimating the cost-effectiveness of immunization programs. She is interested in the incorporation of economics into immunization decision-making, and to that aim has worked with a variety of provincial and national organizations.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH – Publications144B5ACA0, 701071OSN – Publications144B5ACA0, 701222OMNI – Publications144B5ACA0, 704045Covid-19859FDEF6, 714608Charting a FutureCharting a Future for Emerging Infectious Disease Modelling in Canada – April 2023 [1] 2794CAE1, 715387SMMEID – Publications144B5ACA0

We propose an independent institute of emerging infectious disease modellers and policy experts, with an academic core, capable of renewing itself as needed. This institute will combine science and knowledge translation to inform decision-makers at all levels of government and ensure the highest level of preparedness (and readiness) for the next public health emergency. The Public Health Modelling Institute will provide cost-effective, science-based modelling for public policymakers in an easily visualizable, integrated framework, which can respond in an agile manner to changing needs, questions, and data. To be effective, the Institute must link to modelling groups within government, who are best able to pose questions and convey results for use by public policymakers.