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

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

Master's Research - McGill University

Master's Research - McGill University

PhD - McGill University

Master's Research - McGill University

yachen.li@mail.mcgill.ca

Master's Research - McGill University

Master's Research - McGill University

Master's Research - McGill University

Publications

Price discounting as a hidden risk factor of energy drink consumption

Hiroshi Mamiya

Erica E. M. Moodie

Alexandra M. Schmidt

Yu Ma

David Buckeridge

Global consumption of caffeinated energy drinks (CED) has been increasing dramatically despite increasing evidence of their adverse health e… (see more)ffects. Temporary price discounting is a rarely investigated but potentially powerful food marketing tactic influencing purchasing of CED. Using grocery transaction records generated by food stores in Montreal, we investigated the association between price discounting and purchasing of CED across socio-economic status operationalized by education and income levels in store neighbourhood. The outcome, log-transformed weekly store-level sales of CED, was modelled as a function of store-level percent price discounting, store- and neighbourhood-level confounders, and an interaction term between discounting and each of tertile education and income in store neighbourhood. The model was separately fit to transactions from supermarkets, pharmacies, supercentres, and convenience stores. There were 18,743, 12,437, 3965, and 49,533 weeks of CED sales from supermarkets, pharmacies, supercentres, and convenience stores, respectively. Percent price discounting was positively associated with log sales of CED for all store types, and the interaction between education and discounting was prominent in supercentres: −0.039 [95% confidence interval (CI): −0.051, −0.028] and −0.039 [95% CI: −0.057, −0.021], for middle- and high-education neighbourhoods relative to low-education neighbourhoods, respectively. Relative to low-income areas, the associations of discounting and log CED sales in supercentres for neighbourhoods with middle- and high-income tertile were 0.022 [95% CI: 0.010, 0.033] and 0.015 (95% CI: −0.001, 0.031), respectively. Price discounting is an important driver of CED consumption and has a varying impact across community education and income.

2021-03-16

Canadian Journal of Public Health (published)

Staying Ahead of the Epidemiologic Curve: Evaluation of the British Columbia Asthma Prediction System (BCAPS) During the Unprecedented 2018 Wildfire Season

Sarah B. Henderson

Kathryn T. Morrison

Kathleen E. McLean

Yue Ding

Jiayun Yao

Gavin Shaddick

David Buckeridge

2021-03-12

Frontiers in Public Health (published)

Concurrent prescriptions for opioids and benzodiazepines and risk of opioid overdose: protocol for a retrospective cohort study using linked administrative data

Erin Y Liu

Robyn Tamblyn

Kristian B Filion

David Buckeridge

2021-02-18

BMJ Open (published)

Predicting Infectiousness for Proactive Contact Tracing

Prateek Gupta

Nasim Rahaman

Martin Weiss

Tristan Deleu

Eilif Benjamin Muller

Meng Qu

Victor Schmidt

Pierre-Luc St-Charles

Hannah Alsdurf

Olexa Bilaniuk

David Buckeridge

gaetan caron

pierre luc carrier

Joumana Ghosn

satya ortiz gagne

Bernhard Schölkopf … (see 3 more)

abhinav sharma

andrew williams

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (see more)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

2021-01-12

ICLR.cc/2021/Conference (spotlight)

Guest Editorial Explainable AI: Towards Fairness, Accountability, Transparency and Trust in Healthcare

Arash Shaban-Nejad

Martin Michalowski

John S. Brownstein

David Buckeridge

2021-01-01

IEEE journal of biomedical and health informatics (published)

Assessing the Electronic Evidence System Needs of Canadian Public Health Professionals: A Cross-Sectional Study (Preprint)

Bandna Dhaliwal

Sarah E Neil-Sztramko

Nikita Boston-Fisher

David Buckeridge

Maureen Dobbins

BACKGROUND True evidence-informed decision making in public health relies on incorporating evidence from a number of sources in addition to… (see more) traditional scientific evidence. Lack of access to these types of data, as well as ease of use and interpretability of scientific evidence contribute to limited uptake of evidence-informed decision making in practice. An electronic evidence system that includes multiple sources of evidence and potentially novel computational processing approaches or artificial intelligence holds promise as a solution to overcoming barriers to evidence-informed decision making in public health. OBJECTIVE To understand the needs and preferences for an electronic evidence system among public health professionals in Canada. METHODS An invitation to participate in an anonymous online survey was distributed via listservs of two Canadian public health organizations. Eligible participants were English or French speaking individuals currently working in public health. The survey contained both multiple choice and open-ended questions about needs and preferences relevant to an electronic evidence system. Quantitative responses were analyzed to explore differences by public health role. Inductive and deductive analysis methods were used to code and interpret the qualitative data. Ethics review was not required by the host institution. RESULTS Respondents (n = 371) were heterogeneous, spanning organizations, positions, and areas of practice within public health. Nearly all (98.0%) respondents indicated that an electronic evidence system would support their work. Respondents had high preferences for local contextual data, research and intervention evidence, and information about human and financial resources. Qualitative analyses identified a number of concerns, needs, and suggestions for development of such a system. Concerns ranged from personal use of such a system, to the ability of their organization to use such a system. Identified needs spanned the different sources of evidence including local context, research and intervention evidence, and resources and tools. Additional suggestions were identified to improve system usability. CONCLUSIONS Canadian public health professionals have positive perceptions towards an electronic evidence system that would bring together evidence from the local context, scientific research, and resources. Elements were also identified to increase the usability of an electronic evidence system.

2020-12-14

JMIR Public Health and Surveillance (published)

Global Surveillance of COVID-19 by mining news media using a multi-source dynamic embedded topic model

Pratheeksha Nair

Zhi Wen

Imane Chafi

Anya Okhmatovskaia

Guido Powell

Yannan Shen

David Buckeridge

2020-11-10

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (published)

Effectiveness of quarantine and testing to prevent COVID-19 transmission from arriving travelers

Russell Wa

David Buckeridge

2020-11-04

medRxiv (preprint)

Explainability and Interpretability: Keys to Deep Medicine

Arash Shaban-Nejad

Martin Michalowski

David Buckeridge

2020-11-03

Explainable AI in Healthcare and Medicine (published)

Association between extreme precipitation, drinking water and acute gastrointestinal illness in the Great Lakes

R. Graydon

M. Mezzacapo

J. Boehme

David Buckeridge

S. Foldy

T. Edge

J. Brubacher

L. Chan

M. Dellinger

E. Faustman

J. Rose

T. Takaro

2020-10-26

ISEE Conference Abstracts (published)

Importation of SARS-CoV-2 following the "semaine de relache" and Quebec's (Canada) COVID-19 burden - a mathematical modeling study

Arnaud Godin

Yiqing Xia

David Buckeridge

Sharmistha Mishra

Dirk Douwes-Schultz

Yannan Shen

Maxime Lavigne

Mélanie Drolet

Alexandra M. Schmidt

Marc Brisson

Mathieu Maheu-Giroux

Background: The Canadian epidemics of COVID-19 exhibit distinct early trajectories, with Quebec bearing a very high initial burden. The sema… (see more)ine de relache, or March break, took place two weeks earlier in Quebec as compared to the rest of Canada. This event may have played a role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to examine the role of case importation in the early transmission dynamics of SARS-CoV-2 in Quebec. Methods: Using detailed surveillance data, we developed and calibrated a deterministic SEIR-type compartmental model of SARS-CoV-2 transmission. We explored the impact of altering the number of imported cases on hospitalizations. Specifically, we investigated scenarios without case importation after March break, and as scenarios where cases were imported with the same frequency/timing as neighboring Ontario. Results: A total of 1,544 and 1,150 returning travelers were laboratory-confirmed in Quebec and Ontario, respectively (with symptoms onset before 2020-03-25). The cumulative number of hospitalizations could have been reduced by 55% (95% credible interval [95%CrI]: 51-59%) had no cases been imported after Quebec's March break. However, had Quebec experienced Ontario's number of imported cases, cumulative hospitalizations would have only been reduced by 12% (95%CrI: 8-16%). Interpretation: Our results suggest that case importation played an important role in the early spread of COVID-19 in Quebec. Yet, heavy importation of SARS-CoV-2 in early March could be insufficient to resolve interprovincial heterogeneities in cumulative hospitalizations. The importance of other factors -public health preparedness, responses, and capacity- should be investigated.

2020-10-25

International Journal of Infectious Diseases (published)

The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada—A mathematical modeling study of surveillance data

Arnaud Godin

Yiqing Xia

David Buckeridge

Sharmistha Mishra

Dirk Douwes-Schultz

Yannan Shen

Maxime Lavigne

Mélanie Drolet

Alexandra M. Schmidt

Marc Brisson

Mathieu Maheu-Giroux

2020-10-25

International Journal of Infectious Diseases (published)