CANadian Network for MODelling infectious Disease / Réseau CANadien de MODélisation des maladies infectieuses



  • The overarching aim of the CANMOD network is to enhance Canada’s capacity for data-driven infectious disease modelling that directly supports short, medium and long-term public health decisions in infectious disease.
  • Our network will be grounded in collaborative teams comprised of modellers, statisticians, epidemiologists, public health decision-makers, and those implementing and delivering interventions.
  • These teams will coordinate a broader community of modellers and statisticians to contribute to and benefit from the work.
  • The questions tackled by our network will be grounded in public health needs and generated in partnership between research investigators and knowledge users, composed of public health leaders, health administrators and policy-makers.
  • This collaborative research will drive data collection, curation and access such that the necessary data are available when needed.
  • The strong collaborative partnerships with public health policy makers will cement connections between epidemiology, modelling, statistics, public health and policy.
  • While the network will address questions of immediate relevance in the shorter term, we will undertake this with longer-term challenges in mind, building a trajectory enhancing modelling expertise and capacity in infectious disease in Canada.


We are on this path already as researchers move from the daily challenges of decision-making in COVID-19 to longer-term modelling, policy questions and challenges. Moving into addressing longer-term challenges also lays the foundation for sustainability in terms of collaboration and training opportunities.

CANMOD will build and coordinate national capacity through sharing research problems, models and estimates, data fields and flows, and expertise nationally. It will build capacity to meet the modelling, data analysis and statistical estimation needs of public health and decision makers, at time scales from urgent short-term requests to longer-term modelling research.

It will also offer extensive “hands-on” training opportunities to postdoctoral researchers, graduate and undergraduate students from disciplines that are rarely closely linked, who will receive mentorship at the intersection of applied infectious disease modelling. CANMOD has a commitment to increasing equity, diversity, and inclusivity in the next generation of infectious disease modellers. Trainees will be well-placed for careers in academia, industry and the public sector in quantitative roles.


(1) Answer research questions and develop methods as collaborative teams of modellers, statisticians, epidemiologists and public health researchers.

  • Using this framework, we will:

1a) Rapidly answer stakeholder questions related to pandemic response in short-term by building data analysis tools that can be quickly modified for immediate response

1b) Conduct investigator-led medium to longer-term research to advance methods, modify and adapt existing models, and conduct modelling studies for pandemic preparedness and evaluation of interventions to date.

  • Aim 1 will be accomplished by working with, as appropriate, multiple layers of pandemic-relevant data across public health surveillance, laboratory, clinical, and health care systems in partnership with the public health agencies and stakeholders.
  • Examples of such data-sharing systems between stakeholders and researchers include for example, the BCCDC modelling team, the Ontario COVID-19 Modelling Consensus Table, the Ontario Health Data Platform, Quebec’s INESSS-McGill and INSPQ-ULaval-McGill collaborations, and the Health Data Research Network. CANMOD will also facilitate opportunities for modelling research embedded as part of clinical and population-intervention trials and cohort studies.

(2) Train a cadre of applied HQP/modellers who can respond rapidly to public health questions within these two years.

  • Complement this with training beyond the two years through internships, postdoctoral fellowships, and sustained relationships and data flows between public health decision makers and the broader academic modelling and statistical communities.
  • The training component will offer infectious disease modelling and estimation training, designed for trainees with a quantitative background. We will also offer training in epidemiology to mathematical modellers (e.g., study design, quantitative bias analyses, causal inference, systematic review and meta-analyses, pathogen diversification and genomics, health/policy system issues, communication, and public health bioethics).
  • CANMOD will provide training for non-modellers in the use and potential role of models and will also offer to host and provide training modules bridging to other funded networks.

(3) Provide lasting resources in the infectious disease modelling and estimation community.

  • We will facilitate iterative curation of models, algorithms, software and infrastructures for past and current public infectious disease data, including genomic surveillance.
  • We will build on existing structures with which our team has experience, and work in collaboration with Ministries of Health and, where possible, education, labour, transport, and social services.
  • The network will host historical and already-publicly-available data in data gateway systems, thereby providing a repository of publicly-available, anonymized data for developing, testing and comparing models and estimation methods, and for use in the nationally-accessible training programs in Aim 2.

It is our experience that when modellers and statisticians undertake research driven by public health and epidemiological questions, it leads to new science and advances methods. If these questions were easy to answer, Ministries of Health and public health researchers would answer them with existing tools - these researchers are quantitatively savvy and, under normal circumstances, have capacity.

However, even with foundational and established approaches to infectious disease modelling, there remains a need to specify, adapt, and design novel approaches and tools to address new and nuanced scientific questions; to integrate noisy, incomplete, and often multiple layers of data; and to capture fundamental properties defining local context.

The COVID-19 pandemic in Canada was emblematic of the urgent and immediate need for modelling of local context to inform public health decisions that are often implemented at the local jurisdiction, and across diverse epidemiologic and health system contexts. Effective public health requires that local decision-makers have the support of engaged modellers who know their data, their constraints, their contexts and who are passionate about collaborating on their research problems.

Our co-applicants and collaborators have been engaged in this kind of work since the beginning of the COVID-19 pandemic. Some of the scientific questions that have emerged through undertaking research directly embedded in public health decision-making are listed below under Scientific Directions.

The Canadian Statistical Sciences Institute (CANSSI) and the four mathematical institutes (Fields, PIMS, AARMS and CRM) are working with CANMOD to provide resources supporting our plans for training, events, data and model platforms and other opportunities.


Caroline Colijn, Simon Fraser; David Earn, McMaster

  • Alberta: Rob Deardon, Tyler Williamson (U Calgary), Matt Croxen (U Alberta)
  • Atlantic/Yukon: Amy Hurford (Memorial), Ted McDonald (UNB), Lisa Kanary (Yukon U), Brian Gaas (Yukon Health and S Services), Sara McPhee-Knowles (Yukon U)
  • British Columbia: Sarah Otto (UBC), Dan Coombs (UBC) Naveed Janjua, Natalie Prystajecky, Michael Otterstatter (BCCDC), James Colliander (UBC, PIMS), Derek Bingham (SFU, CANSII), Paul Tupper (SFU)
  • Ontario: Jonathan Dushoff, Ben Bolker, Mark Loeb (McMaster); Beate Sander, Ashleigh Tuite, David Fisman (U Toronto) Sharmistha Mishra (U Toronto and St Michael’s Hospital), Samira Mubareka (U Toronto and Sunnybrook Research Institute); Troy Day (Queen’s U); Amy Greer (U Guelph); Michael Wolfson, Doug Manuel (U Ottawa), Diego Bassani (Sick Kids), Sean Cornelius (Ryerson)
  • Quebec: Marc Brisson (Université Laval), Mathieu Maheu-Giroux (McGill), Sandrine Moreira (Laboratoire de Santé Publique du Québec), Jesse Shapiro, Nicole Basta (McGill)
  • Saskatchewan: Nathaniel Osgood (University of Saskatchewan)

(This list is incomplete).

Networks »Networks
Alexander Rutherford »Alexander Rutherford
Amy Greer »Amy Greer
Amy Hurford »Amy Hurford
Ashleigh Tuite »Ashleigh Tuite
B. Jesse Shapiro »B. Jesse Shapiro
Beate Sander »Beate Sander
Benjamin Bolker »Benjamin Bolker
Caroline Colijn »Caroline Colijn
Caroline E Wagner »Caroline E Wagner
Christopher McCabe »Christopher McCabe
Daniel Coombs »Daniel Coombs
David Buckeridge »David Buckeridge
David Earn »David Earn
David Fisman »David Fisman
Diego Bassani »Diego Bassani
James Colliander »James Colliander
Javier Sanchez »Javier Sanchez
Joan Hu »Joan Hu
Jonathan Dushoff »Jonathan Dushoff
Julien Arino »Julien Arino
Lisa Kanary »Lisa Kanary
Marc Brisson »Marc Brisson
Mark Lewis »Mark Lewis
Mark Loeb »Mark Loeb
Mathieu Maheu-Giroux »Mathieu Maheu-Giroux
Michael Wolfson »Michael Wolfson
Natalie Anne Prystajecky »Natalie Anne Prystajecky
Nathaniel Osgood »Nathaniel Osgood
Naveed Zafar Janjua »Naveed Zafar Janjua
Nicole Basta »Nicole Basta
Paul Tupper »Paul Tupper
Rafal Kustra »Rafal Kustra
Rebecca Tyson »Rebecca Tyson
Rob Deardon »Rob Deardon
Samira Mubareka »Samira Mubareka
Sara McPhee-Knowles »Sara McPhee-Knowles
Sarah Otto »Sarah Otto
Sean Paul Cornelius »Sean Paul Cornelius
Sharmistha Mishra »Sharmistha Mishra
Troy Day »Troy Day
Tyler Williamson »Tyler Williamson
Dionne Aleman »Dionne Aleman
Cedric Chauve »Cedric Chauve
Colin Daniel »Colin Daniel
Donald Estep »Donald Estep
Dean Karlen »Dean Karlen
Matthew MacLeod »Matthew MacLeod
Doug Manuel »Doug Manuel
Sandrine Moreira »Sandrine Moreira
Xuekui Zhang »Xuekui Zhang
Michael WZ Li »Michael WZ Li
Alan Diener »Alan Diener
Ellen Rafferty »Ellen Rafferty
Timothy Caulfield »Timothy Caulfield
Brian Gaas »Brian Gaas
Man Wah Yeung »Man Wah Yeung
Kate Harback »Kate Harback
Larry Svenson »Larry Svenson
Sasha Van Katwyk »Sasha Van Katwyk
Jean-Paul Soucy »Jean-Paul Soucy
Shelby Sturrock »Shelby Sturrock
Shashi Shahi »Shashi Shahi
Jeff Round »Jeff Round
+Comments (0)
+Citations (1)