David Buckeridge

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… (see more)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 (published)

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… (see more) 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 (published)

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 (published)

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 (published)

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 (published)

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. … (see more)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 (published)

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. … (see more)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.

2024-04-11

International Journal of Epidemiology (published)

doi.org

Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning

Ziyang Song

Qincheng Lu

He Zhu

David Buckeridge

Yue Li

Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in … (see more)the healthcare domain. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on biosignals and longitudinal clinical records by both next-token and previous-token prediction in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignals and longitudinal clinical records, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from biosignal time-series sequences, even more so after fine-tuning on the task.

2024-02-14

ArXiv (preprint)

arxiv.org