Portrait of David Buckeridge

David Buckeridge

Associate Academic Member
Full Professor, McGill University, Department of Epidemiology, Biostatistics and Occupational Health
Research Topics
Medical Machine Learning
Google Scholar
LinkedIn

Biography

David Buckeridge is a professor at the School of Population and Global Health at McGill University, as well as chief digital health officer for the McGill University Health Centre and executive scientific director of the Public Health Agency of Canada.

A Tier 1 Canada Research Chair in Health Informatics and Data Science, Buckeridge has projected health system demand for the Canadian province of Quebec, led data management and analytics for the Canadian Immunity Task Force, and supported the World Health Organization in monitoring global immunity to SARS-CoV-2. He has an MD from Queen's University, an MSc in epidemiology from the University of Toronto and a PhD in biomedical informatics from Stanford University. He is a Fellow of the Royal College of Physicians of Canada.

Current Students

Kalash Dave
Master's Research - McGill University
Eva Gao
PhD - McGill University
yachen li Li
Master's Research - McGill University
yachen.li@mail.mcgill.ca
Aidan Licoppe
Master's Research - McGill University
Github
Yannan Shen
Master's Research - McGill University

Publications

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data
Louis Mullie
Jonathan Afilalo
Patrick Archambault
Rima Bouchakri
Kip Brown
David Buckeridge
Yiorgos Alexandros Cavayas
Alexis F Turgeon
Denis Martineau
François Lamontagne
Martine Lebrasseur
Renald Lemieux
Jeffrey Li
Michaël Sauthier
Pascal St-Onge
An Tang
William Witteman
Michael Chassé
Abstract Objectives Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data po… (see more)oling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. Materials and methods We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. Results The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. Discussion and conclusion The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
2023-12-21
J. Am. Medical Informatics Assoc. (published)
doi.org
CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data
Louis Mullie
Jonathan Afilalo
Patrick Archambault
Rima Bouchakri
Kip Brown
David Buckeridge
Yiorgos Alexandros Cavayas
Alexis F Turgeon
Denis Martineau
François Lamontagne
Martine Lebrasseur
Renald Lemieux
Jeffrey Li
Michaël Sauthier
Pascal St-Onge
An Tang
William Witteman
Michael Chassé
Abstract Objectives Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data po… (see more)oling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. Materials and methods We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. Results The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. Discussion and conclusion The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
2023-12-21
J. Am. Medical Informatics Assoc. (published)
doi.org
TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare
Ziyang Song
Qincheng Lu
Hao Xu
Mike He Zhu
David Buckeridge
Yue Li
2023-11-29
ArXiv (preprint)
doi.org
arxiv.org
Estimating the population effectiveness of interventions against COVID-19 in France: a modelling study
Iris Ganser
David Buckeridge
Jane M. Heffernan
M. Prague
Rodolphe Thiébaut
Background Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty … (see more)persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness. Methods To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout. Results The first lockdown was the most effective, reducing transmission by 84% (95% confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74% (69-77) and 11% (9-18), respectively). A 6pm curfew was more effective than one at 8 pm (68% (66-69) vs. 48% (45-49) reduction), while school closures reduced transmission by 15% (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 194% (95% prediction interval (PI) 74-424) more deaths and 1,488,000 or 340% (136-689) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted. Conclusion Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the CEPI initiative for vaccine availability.
2023-09-14
medRxiv (preprint)
doi.org
Impact of a vaccine passport on first-dose SARS-CoV-2 vaccine coverage by age and area-level social determinants of health in the Canadian provinces of Quebec and Ontario: an interrupted time series analysis
Jorge Luis Flores Anato
Huiting Ma
M. Hamilton
Yiqing Xia
Sam Harper
David Buckeridge
Marc Brisson
Michael P. Hillmer
Kamil A. Malikov
Aidin Kerem
Reed Beall
Caroline E Wagner
S. Baral
Étienne Racine
Ève Dubé
Sharmistha Mishra
Mathieu Maheu-Giroux
2023-09-01
CMAJ Open (published)
doi.org
The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020–2023
Tanya J. Murphy
Hanna Swail
Jaspreet Jain
Maureen Anderson
Philip Awadalla
Lesley Behl
P. Brown
C. Charlton
Karen Colwill
S. Drews
A. Gingras
Deena Hinshaw
P. Jha
J. Kanji
Victoria A. Kirsh
Amanda Lang
Marc-andré Langlois
Stephen Lee
Antoine Lewin
Sheila F O’Brien … (see 10 more)
Chantale Pambrun
Kimberly Skead
David A. Stephens
Derek R. Stein
G. Tipples
Paul G. Van Caeseele
Timothy Grant Evans
Olivia Oxlade
Bruce D. Mazer
David Buckeridge
Background: During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Altho… (see more)ugh high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. Methods: Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. Results: By November 2021, 9.0% (95% credible interval [CrI] 7.3%–11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant — by Mar. 15, 2023, 76% (95% CrI 74%–79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. Interpretation: Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.
2023-08-13
Canadian Medical Association Journal (published)
doi.org
Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: a systematic review and meta-regression
Niklas Bobrovitz
Harriet Ware
Xiaomeng Ma
Zihan Li
Reza Hosseini
Christian Cao
Anabel Selemon
Mairead Whelan
Zahra Premji
Hanane Issa
Brianna Cheng
Laith J Abu Raddad
David Buckeridge
Maria D Van Kerkhove
Vanessa Piechotta
Melissa M Higdon
Annelies Wilder-Smith
Isabel Bergeri
Daniel R Feikin
Rahul K. Arora … (see 2 more)
Minal K Patel
Lorenzo Subissi
2023-05-01
The Lancet Infectious Diseases (published)
doi.org
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
andrew williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David Buckeridge
Chris Pal
Bernhard Schölkopf
Yoshua Bengio
2023-03-13
PLOS Digital Health (published)
doi.org
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
andrew williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David Buckeridge
Chris Pal
Bernhard Schölkopf
Yoshua Bengio
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting d… (see more)aily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
2023-03-01
PLOS Digital Health (published)
doi.org
A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions (preprint)
Dirk Douwes-Schultz
Alexandra M. Schmidt
Yannan Shen
David Buckeridge
2023-02-05
ArXiv (preprint)
arxiv.org
arxiv.org
Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world
Arash Shaban-Nejad
Martin Michalowski
Simone Bianco
John S. Brownstein
David Buckeridge
Robert L Davis
2022-11-25
Experimental Biology and Medicine (published)
doi.org
Assessing the potential for virtualizable care in the pediatric emergency department.
Esli Osmanlliu
Brett Burstein
Robyn Tamblyn
David Buckeridge
INTRODUCTION There is increasing interest for patient-to-provider telemedicine in pediatric acute care. The suitability of telemedicine (vir… (see more)tualizability) for visits in this setting has not been formally assessed. We estimated the proportion of in-person pediatric emergency department (PED) visits that were potentially virtualizable, and identified factors associated with virtualizable care. METHODS This was a retrospective analysis of in-person visits at the PED of a Canadian tertiary pediatric hospital (02/2018-12/2019). Three definitions of virtualizable care were developed: (1) a definition based on "resource use" classifying visits as virtualizable if they resulted in a home discharge, no diagnostic testing, and no return visit within 72 h; (2) a "diagnostic definition" based on primary ED diagnosis; and (3) a stringent "combined definition" by which visits were classified as virtualizable if they met both the resource use and diagnostic definitions. Multivariable logistic regression was used to identify factors associated with telemedicine suitability. RESULTS There were 130,535 eligible visits from 80,727 individual patients during the study period. Using the most stringent combined definition of telemedicine suitability, 37.9% (95% confidence interval (CI) 37.6%-38.2%) of in-person visits were virtualizable. Overnight visits (adjusted odds ratio (aOR) 1.16-1.37), non-Canadian citizenship (aOR 1.10-1.18), ethnocultural vulnerability (aOR 1.14-1.22), and a consultation for head trauma (aOR 3.50-4.60) were associated with higher telemedicine suitability across definitions. DISCUSSION There is a high potential for patient-to-provider telemedicine in the PED setting. Local patient and visit-level characteristics must be considered in the design of safe and inclusive telemedicine models for pediatric acute care.
2022-11-20
Journal of Telemedicine and Telecare (published)
doi.org