[arXiv, 22 September 2022]

Maruotti et al. 2022 used a mark-recapture approach to estimate bounds on the true number of monkeypox infections in various countries. These approaches are fundamentally flawed; it is impossible to estimate undercounting based solely on a single stream of reported cases. Simulations based on a Richards curve for cumulative incidence show that, for reasonable epidemic parameters, the proposed methods estimate bounds on the ascertainment ratio of ≈0.2−0.5 roughly independently of the true ascertainment ratio. These methods should not be used.

[Oikos, 25 October 2022]

Despite considerable study of population cycles, the striking variability of cycle periods in many cyclic populations has received relatively little attention. Mathematical models of cyclic population dynamics have historically exhibited much greater regularity in cycle periods than many real populations, even when accounting for environmental stochasticity. We contend, however, that the recent focus on understanding the impact of long, transient but recurrent epochs within population oscillations points the way to a previously unrecognized means by which environmental stochasticity can create cycle period variation. Specifically, consumer–resource cycles that bring the populations near a saddle point (a combination of population sizes toward which the populations tend, before eventually transitioning to substantially different levels) may be subject to a slow passage effect that has been dubbed a ‘saddle crawlby'. In this study, we illustrate how stochasticity that generates variability in how close predator and prey populations come to saddles can result in substantial variability in the durations of crawlbys and, as a result, in the periods of population cycles. Our work suggests a new mechanistic hypothesis to explain an important factor in the irregular timing of population cycles and provides a basis for understanding when environmental stochasticity is, and is not, expected to generate cyclic dynamics with variability across periods.

[Vaccine, 16 September 2023]

Background: It is evident that COVID-19 will remain a public health concern in the coming years, largely driven by variants of concern (VOC). It is critical to continuously monitor vaccine effectiveness as new variants emerge and new vaccines and/or boosters are developed. Systematic surveillance of the scientific evidence base is necessary to inform public health action and identify key uncertainties. Evidence syntheses may also be used to populate models to fill in research gaps and help to prepare for future public health crises. This protocol outlines the rationale and methods for a living evidence synthesis of the effectiveness of COVID-19 vaccines in reducing the morbidity and mortality associated with, and transmission of, VOC of SARS-CoV-2.

Methods: Living evidence syntheses of vaccine effectiveness will be carried out over one year for (1) a range of potential outcomes in the index individual associated with VOC (pathogenesis); and (2) transmission of VOC. The literature search will be conducted up to May 2023. Observational and database-linkage primary studies will be included, as well as RCTs. Information sources include electronic databases (MEDLINE; Embase; Cochrane, L*OVE; the CNKI and Wangfang platforms), pre-print servers (medRxiv, BiorXiv), and online repositories of grey literature. Title and abstract and full-text screening will be performed by two reviewers using a liberal accelerated method. Data extraction and risk of bias assessment will be completed by one reviewer with verification of the assessment by a second reviewer. Results from included studies will be pooled via random effects meta-analysis when appropriate, or otherwise summarized narratively.

Discussion: Evidence generated from our living evidence synthesis will be used to inform policy making, modelling, and prioritization of future research on the effectiveness of COVID-19 vaccines against VOC.

[Journal of The Royal Society Interface, 25 October 2023]

Observations of male alternative reproductive tactics (ARTs) in a variety of species have stimulated the development of mathematical models that can account for the evolution and stable coexistence of multiple male phenotypes. However, little attention has been given to the population dynamic consequences of ARTs. We present a population model that takes account of the existence of two male ARTs (guarders and sneakers), assuming that tactic frequencies are environmentally determined and tactic reproductive success depends on the densities of both types. The presence of sneakers typically increases overall population density. However, if sneakers comprise a sufficiently large proportion of the population—or, equivalently, if guarders are sufficiently rare—then it is possible for the total population to crash to extinction (in this extreme regime, there is also an Allee effect, i.e. a threshold density below which the population will go extinct). We apply the model to the example of the invasive round goby (Neogobius melanostomus). We argue that ARTs can dramatically influence population dynamics and suggest that considering such phenotypic plasticity in population models is potentially important, especially for species of conservation or commercial importance.

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, 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, 679826Laura CowenAssociate Professor in the Department of Mathematics and Statistics at the University of Victoria.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.

[PNAS, 26 January 2024]

If a new pathogen causes a large epidemic, then it might “burn out” before causing a second epidemic. The burnout probability can be estimated from large numbers of computationally intensive simulations, but an easily computable formula for the burnout probability has never been found. Using a conceptually simple approach, we derive such a formula for the standard SIR epidemic model with vital dynamics (host births and deaths). With this formula, we show that the burnout probability is always smaller for diseases with longer infectious periods, but is bimodal with respect to transmissibility (the basic reproduction number). Our analysis shows that the persistence of typical human infectious diseases cannot be explained by births of new susceptibles, clarifying an important epidemiological puzzle…

Citerank: (15) 679712CANMOD – PeopleCANMOD is a national network, with members located across the country and associated with a broader Emerging Infectious Disease Modelling (EIDM) initiative. We are a community of modellers, statisticians, epidemiologists, public health decision-makers, and those implementing and delivering interventions.10019D3ABAB, 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 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, 679775David BuckeridgeDavid is a Professor in the School of Population and Global Health at McGill University, where he directs the Surveillance Lab, an interdisciplinary group that develops, implements, and evaluates novel computational methods for population health surveillance. He is also the Chief Digital Health Officer at the McGill University Health Center where he directs strategy on digital transformation and analytics and he is an Associate Member with the Montreal Institute for Learning Algorithms (Mila).10019D3ABAB, 679844Mathieu Maheu-GirouxCanada Research Chair (Tier 2) in Population Health Modeling and Associate Professor, McGill University.10019D3ABAB, 679855Nathaniel OsgoodNathaniel D. Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan.10019D3ABAB, 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.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, 685387Michael Y LiProfessor of Mathematics in the Department of Mathematical and Statistical Sciences at the University of Alberta, and Director of the Information Research Lab (IRL).10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH – Publications144B5ACA0, 704045Covid-19859FDEF6, 728553Pandemic modeling questions?140D5CB99

[Canadian Journal of Public Health, 25 July 2024]

Setting: Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies.

Intervention: Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments.

Outcomes: We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces.

Implication: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

[Journal of The Royal Society Interface, 11 December 2024]

As SARS-CoV-2 has transitioned from a novel pandemic-causing pathogen into an established seasonal respiratory virus, focus has shifted to post-acute sequelae of COVID-19 (PASC, colloquially ‘long COVID’). We use compartmental mathematical models simulating emergence of new variants to help identify key sources of uncertainty in PASC trajectories. Some parameters (such as the duration and equilibrium prevalence of infection, as well as the fraction of infections that develop PASC) matter more than others (such as the duration of immunity and secondary vaccine efficacy against PASC). Even if newer variants carry the same risk of PASC as older types, the dynamics of selection can give rise to greater PASC prevalence. However, identifying plausible PASC prevalence trajectories requires accurate knowledge of the transmission potential of COVID-19 variants in the endemic phase. Precise estimates for secondary vaccine efficacy and duration of immunity will not greatly improve forecasts for PASC prevalence. Researchers involved with Living Evidence Synthesis, or other similar initiatives focused on PASC, are well advised to ascertain primary efficacy against infection, duration of infection and prevalence of active infection in order to facilitate predictions.

[medRxiv, 20 December 2024]

Background: Canadian notifiable disease surveillance provides data on the incidence of communicable diseases, dating back to the late 19th century. The Public Health Agency of Canada offers summaries of these data (from 1924–2022) through an online portal. These summaries provide historical context for Canadian health researchers, but lack information on intra-annual and inter-provincial patterns. Sub-annual and sub-national data can be found in published documents, or requested from archives of government agencies, but are only available in typewritten or handwritten hard copies. We digitized and collated these data sources to create a resource for epidemiology and public health.

Methods: We manually entered data from scans of hard copies into spreadsheets resembling the originals, facilitating accurate transcription through easier cross-checking. We developed open-source pipelines to harmonize these spreadsheets into CSV files that blend data across sources.

Results: We assembled and processed 1,631,380 incidence values from 1903–2021. Focusing on sub-annual and sub-national data and removing redundancy yielded 934,009 weekly, monthly, or quarterly incidence values broken down by province/territory, containing 139 diseases. We give two examples of sub-annual and sub-national patterns: strong annual cycles of poliomyelitis that peaked simultaneously across provinces, and spatially heterogeneous resurgence of whooping cough in the 1990s.

Interpretation: Canada’s history of infectious disease surveillance has produced a detailed record of sub-annual and sub-national disease incidence patterns that remains largely unexplored. This important record is now available as the Canadian Disease Incidence Dataset (CANDID), hosted on a publicly accessible website along with the pipelines used to create it and scans of the original sources.

[PLoS ONE, 3 April 2025]

Living evidence synthesis (LES) involves repeatedly updating a systematic review or meta-analysis at regular intervals to incorporate new evidence into the summary results. It requires a considerable amount of human time investment in the article search, collection, and data extraction phases. Tools exist to automate the retrieval of relevant journal articles, but pulling data out of those articles is currently still a manual process. In this article, we present a proof-of-concept Python program that leverages artificial intelligence (AI) tools (specifically, ChatGPT) to parse a batch of journal articles and extract relevant results, greatly reducing the human time investment in this action without compromising on accuracy. Our program is tested on a set of journal articles that estimate the mean incubation period for COVID-19, an epidemiological parameter of importance for mathematical modelling. We also discuss important limitations related to the total amount of information and rate at which that information can be sent to the AI engine. This work contributes to the ongoing discussion about the use of AI and the role such tools can have in scientific research.