Portrait de David Buckeridge

David Buckeridge

Membre académique associé
Professeur titulaire, McGill University, Département d'épidémiologie, biostatistique et santé au travail
Sujets de recherche
Apprentissage automatique médical
Google Scholar
LinkedIn

Biographie

David Buckeridge est professeur titulaire à l'École de santé des populations et de santé mondiale de l'Université McGill, responsable de la santé numérique au Centre universitaire de santé McGill et directeur scientifique exécutif pour l'Agence de la santé publique du Canada. Titulaire d'une chaire de recherche du Canada (niveau 1) en informatique de la santé et en science des données, il a établi les projections concernant la demande dans le système de santé du Québec, dirigé la gestion des données et l'analyse pour le groupe de travail sur l'immunité canadienne et aidé l'Organisation mondiale de la santé à surveiller l'immunité mondiale contre le SRAS-CoV-2. Il est titulaire d'un doctorat en médecine (Université Queen's), d'une maîtrise en épidémiologie (Université de Toronto) et d'un doctorat en informatique biomédicale (Université Stanford), et est membre du Collège royal des médecins du Canada.

Étudiants actuels

Kalash Dave
Maîtrise recherche - McGill
Github
Eva Gao
Doctorat - McGill
yachen li Li
Maîtrise recherche - McGill
yachen.li@mail.mcgill.ca
Aidan Licoppe
Maîtrise recherche - McGill
Github
Yannan Shen
Maîtrise recherche - McGill

Publications

TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction
Ziyang Song
Qincheng Lu
Mike He Zhu
David Buckeridge
Yue Li
In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (voir plus)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.
2024-10-10
NeurIPS.cc/2024/Workshop/TSALM (publié)
openreview.net
openreview.net
TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction
Ziyang Song
Qincheng Lu
Mike He Zhu
David Buckeridge
Yue Li
In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (voir plus)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.
2024-10-10
NeurIPS.cc/2024/Workshop/TSALM (publié)
openreview.net
openreview.net
TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
Ziyang Song
Qincheng Lu
Mike He Zhu
David Buckeridge
Yue Li
2024-10-03
ArXiv (prépublication)
doi.org
openreview.net
Correction: Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach
Jihoon Lim
W Alton Russell
Mariam El-Sheikh
David Buckeridge
Dimitra Panagiotoglou
2024-09-09
Harm Reduction Journal (publié)
doi.org
Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples
Yannan Shen
Russell Steele
Philip Abdelmalik
David Buckeridge
Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the uti… (voir plus)lity of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing 'gold standard' dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop 'gold standard' dates about the onset of outbreaks due to new viral variants.
2024-08-22
Medical Informatics Europe (publié)
doi.org
Canada's Provincial Covid-19 Pandemic Modelling Efforts: A Review of Mathematical Models and Their Impacts on the Responses
Yiqing Xia
Jorge Luis Flores Anato
Caroline Colijin
Naveed Janjua
Michael Otterstatter
Mike Irvine
Tyler Williamson
Marie B. Varughese
Michael Li
Nathaniel Osgood
David J. D. Earn
Beate Sander
Lauren E. Cipriano
Kumar Murty
Fanyu Xiu
Arnaud Godin
David Buckeridge
Amy Hurford
Sharmistha Mishra
Mathieu Maheu-Giroux
2024-07-25
Canadian Journal of Public Health (publié)
doi.org
Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses.
Yiqing Xia
Jorge Luis Flores Anato
Caroline Colijin
Naveed Janjua
Mike Irvine
Tyler Williamson
Marie B. Varughese
Michael Li
Nathaniel D. Osgood
David J. D. Earn
Beate Sander
Lauren E. Cipriano
Kumar Murty
Fanyu Xiu
Arnaud Godin
David Buckeridge
Amy Hurford
Sharmistha Mishra
Mathieu Maheu-Giroux
SETTING Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic traject… (voir plus)ories, 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.
2024-07-25
Canadian journal of public health (publié)
doi.org
Canada’s approach to SARS-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics
Sheila F. O’Brien
Michael Asamoah-Boaheng
Brian Grunau
Mel Krajden
David Buckeridge
David M. Goldfarb
Maureen Anderson
Marc Germain
Patrick Brown
Derek R. Stein
Kami Kandola
Graham Tipples
Philip Awadalla
Amanda Lang
Lesley Behl
Tiffany Fitzpatrick
Steven J. Drews
2024-07-09
Canadian journal of public health (publié)
doi.org
Canada's approach to SARS-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics.
Sheila F. O’Brien
Michael Asamoah-Boaheng
Brian Grunau
Mel Krajden
David Buckeridge
David M. Goldfarb
Maureen Anderson
Marc Germain
Patrick Brown
Derek R. Stein
Kami Kandola
Graham Tipples
Philip Awadalla
Amanda Lang
Lesley Behl
Tiffany Fitzpatrick
Steven J. Drews
SETTING In Canada's federated healthcare system, 13 provincial and territorial jurisdictions have independent responsibility to collect data… (voir plus) to inform health policies. During the COVID-19 pandemic (2020-2023), national and regional sero-surveys mostly drew upon existing infrastructure to quickly test specimens and collect data but required cross-jurisdiction coordination and communication. INTERVENTION There were 4 national and 7 regional general population SARS-CoV-2 sero-surveys. Survey methodologies varied by participant selection approaches, assay choices, and reporting structures. We analyzed Canadian pandemic sero-surveillance initiatives to identify key learnings to inform future pandemic planning. OUTCOMES Over a million samples were tested for SARS-CoV-2 antibodies from 2020 to 2023 but siloed in 11 distinct datasets. Most national sero-surveys had insufficient sample size to estimate regional prevalence; differences in methodology hampered cross-regional comparisons of regional sero-surveys. Only four sero-surveys included questionnaires. Sero-surveys were not directly comparable due to different assays, sampling methodologies, and time-frames. Linkage to health records occurred in three provinces only. Dried blood spots permitted sample collection in remote populations and during stay-at-home orders. IMPLICATIONS To provide timely, high-quality information for public health decision-making, routine sero-surveillance systems must be adaptable, flexible, and scalable. National capability planning should include consortiums for assay design and validation, defined mechanisms to improve test capacity, base documents for data linkage and material transfer across jurisdictions, and mechanisms for real-time communication of data. Lessons learned will inform incorporation of a robust sero-survey program into routine surveillance with strategic sampling and capacity to adapt and scale rapidly as a part of a comprehensive national pandemic response plan.
2024-07-09
Canadian journal of public health (publié)
doi.org
A Bayesian Non-Stationary Heteroskedastic Time Series Model for Multivariate Critical Care Data
Zayd Omar
David A. Stephens
Alexandra M. Schmidt
David Buckeridge
2024-07-02
Statistics in Medicine (publié)
doi.org
arxiv.org
Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach
Jihoon Lim
W Alton Russell
Mariam El-Sheikh
David Buckeridge
Dimitra Panagiotoglou
2024-06-28
Harm Reduction Journal (publié)
doi.org
Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors
Yuan Yu
Matthew J Knight
Diana Gibson
Sheila F O’Brien
David Buckeridge
W Alton Russell
Abstract Background In Canada’s largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. … (voir plus)No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Methods Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420 319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Results Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Conclusions Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.
2024-06-05
International Journal of Epidemiology (publié)
doi.org