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Xia Y, Flores Anato JL, Colijn C, Janjua N, Irvine M, Williamson T, Varughese MB, Li M, Osgood N, Earn DJD, Sander B, Cipriano LE, Murty K, Xiu F, Godin A, Buckeridge D, Hurford A, Mishra S, Maheu-Giroux M. Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses. CANADIAN JOURNAL OF PUBLIC HEALTH = REVUE CANADIENNE DE SANTE PUBLIQUE 2024; 115:541-557. [PMID: 39060710 PMCID: PMC11382646 DOI: 10.17269/s41997-024-00910-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/31/2024] [Indexed: 07/28/2024]
Abstract
SETTING Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, 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.
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Affiliation(s)
- Yiqing Xia
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Jorge Luis Flores Anato
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Caroline Colijn
- Department of Mathematics, Faculty of Science, Simon Fraser University, Burnaby, BC, Canada
| | - Naveed Janjua
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Mike Irvine
- British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
| | - Tyler Williamson
- Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
- Centre for Health Informatics, University of Calgary, Calgary, AB, Canada
| | - Marie B Varughese
- Analytics and Performance Reporting Branch, Alberta Health, Edmonton, AB, Canada
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Michael Li
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Nathaniel Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - David J D Earn
- Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada
| | - Beate Sander
- Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Public Health Ontario, Toronto, ON, Canada
- ICES, Toronto, ON, Canada
| | - Lauren E Cipriano
- Ivey Business School, University of Western Ontario, London, ON, Canada
- Departments of Epidemiology & Biostatistics and Medicine, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada
| | - Kumar Murty
- Department of Mathematics, University of Toronto, Toronto, ON, Canada
| | - Fanyu Xiu
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Arnaud Godin
- Department of Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, QC, Canada
| | - David Buckeridge
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Amy Hurford
- Department of Biology and Department of Mathematics and Statistics, Faculty of Science, Memorial University of Newfoundland and Labrador, St. John's, NL, Canada
| | - Sharmistha Mishra
- Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
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Quinn GA, Connolly M, Fenton NE, Hatfill SJ, Hynds P, ÓhAiseadha C, Sikora K, Soon W, Connolly R. Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe. J Clin Med 2024; 13:334. [PMID: 38256468 PMCID: PMC10816378 DOI: 10.3390/jcm13020334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Most government efforts to control the COVID-19 pandemic revolved around non-pharmaceutical interventions (NPIs) and vaccination. However, many respiratory diseases show distinctive seasonal trends. In this manuscript, we examined the contribution of these three factors to the progression of the COVID-19 pandemic. METHODS Pearson correlation coefficients and time-lagged analysis were used to examine the relationship between NPIs, vaccinations and seasonality (using the average incidence of endemic human beta-coronaviruses in Sweden over a 10-year period as a proxy) and the progression of the COVID-19 pandemic as tracked by deaths; cases; hospitalisations; intensive care unit occupancy and testing positivity rates in six Northern European countries (population 99.12 million) using a population-based, observational, ecological study method. FINDINGS The waves of the pandemic correlated well with the seasonality of human beta-coronaviruses (HCoV-OC43 and HCoV-HKU1). In contrast, we could not find clear or consistent evidence that the stringency of NPIs or vaccination reduced the progression of the pandemic. However, these results are correlations and not causations. IMPLICATIONS We hypothesise that the apparent influence of NPIs and vaccines might instead be an effect of coronavirus seasonality. We suggest that policymakers consider these results when assessing policy options for future pandemics. LIMITATIONS The study is limited to six temperate Northern European countries with spatial and temporal variations in metrics used to track the progression of the COVID-19 pandemic. Caution should be exercised when extrapolating these findings.
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Affiliation(s)
- Gerry A. Quinn
- Centre for Molecular Biosciences, Ulster University, Coleraine BT52 1SA, UK
| | | | - Norman E. Fenton
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
| | | | - Paul Hynds
- Spatiotemporal Environmental Epidemiology Research (STEER) Group, Environmental Sustainability & Health Institute, Technological University Dublin, D07 H6K8 Dublin, Ireland
- Irish Centre for Research in Applied Geoscience, University College Dublin, D04 F438 Dublin, Ireland
| | - Coilín ÓhAiseadha
- Spatiotemporal Environmental Epidemiology Research (STEER) Group, Environmental Sustainability & Health Institute, Technological University Dublin, D07 H6K8 Dublin, Ireland
- Department of Public Health, Health Service Executive, Dr Steevens’ Hospital, D08 W2A8 Dublin, Ireland
| | - Karol Sikora
- Department of Medicine, University of Buckingham Medical School, Buckingham MK18 1EG, UK
| | - Willie Soon
- Institute of Earth Physics and Space Science (ELKH EPSS), H-9400 Sopron, Hungary
- Center for Environmental Research and Earth Sciences (CERES), Salem, MA 01970, USA
| | - Ronan Connolly
- Independent Researcher, D08 Dublin, Ireland
- Center for Environmental Research and Earth Sciences (CERES), Salem, MA 01970, USA
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López-Mendoza H, González-Álvarez MA, Montañés A. Assessing the effectiveness of international government responses to the COVID-19 pandemic. ECONOMICS AND HUMAN BIOLOGY 2024; 52:101353. [PMID: 38262187 DOI: 10.1016/j.ehb.2024.101353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/10/2023] [Accepted: 01/16/2024] [Indexed: 01/25/2024]
Abstract
This paper examines the effectiveness of non-pharmaceutical measures adopted by governments to control the evolution of the COVID-19 pandemic. Using a Panel VAR model for the OECD countries, we test for Granger causality between the 7-day cumulative incidence, mortality rate, and government response indexes. Granger-type statistics reveal evidence that the evolution of the COVID-19 pandemic influenced the measures taken by governments. However, limited or nonexistent evidence supports the reverse situation. This suggests that government measures were not highly effective in controlling the pandemic. While not implying total ineffectiveness, our results indicate a considerable lack of efficacy, emphasizing a lesson for governments to learn from and correct in preparation for similar events in the future.
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Affiliation(s)
- Héctor López-Mendoza
- CASSETEM Research Group, Department of Economic Analysis, University of Zaragoza, Zaragoza 50005, Spain; Instituto de Salud Pública de Navarra, Pamplona 31003, Spain
| | - María A González-Álvarez
- CASSETEM Research Group, Department of Economic Analysis, University of Zaragoza, Zaragoza 50005, Spain
| | - Antonio Montañés
- CASSETEM Research Group, Department of Economic Analysis, University of Zaragoza, Zaragoza 50005, Spain.
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Murphy C, Lim WW, Mills C, Wong JY, Chen D, Xie Y, Li M, Gould S, Xin H, Cheung JK, Bhatt S, Cowling BJ, Donnelly CA. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230132. [PMID: 37611629 PMCID: PMC10446910 DOI: 10.1098/rsta.2023.0132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Caitriona Murphy
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Wey Wen Lim
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cathal Mills
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jessica Y. Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Dongxuan Chen
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Yanmy Xie
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Mingwei Li
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Susan Gould
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Hualei Xin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Justin K. Cheung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Kobenhavn, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
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5
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Joffe AR, Elliott A. Long COVID as a functional somatic symptom disorder caused by abnormally precise prior expectations during Bayesian perceptual processing: A new hypothesis and implications for pandemic response. SAGE Open Med 2023; 11:20503121231194400. [PMID: 37655303 PMCID: PMC10467233 DOI: 10.1177/20503121231194400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 07/27/2023] [Indexed: 09/02/2023] Open
Abstract
This review proposes a model of Long-COVID where the constellation of symptoms are in fact genuinely experienced persistent physical symptoms that are usually functional in nature and therefore potentially reversible, that is, Long-COVID is a somatic symptom disorder. First, we describe what is currently known about Long-COVID in children and adults. Second, we examine reported "Long-Pandemic" effects that create a risk for similar somatic symptoms to develop in non-COVID-19 patients. Third, we describe what was known about somatization and somatic symptom disorder before the COVID-19 pandemic, and suggest that by analogy, Long-COVID may best be conceptualized as one of these disorders, with similar symptoms and predisposing, precipitating, and perpetuating factors. Fourth, we review the phenomenon of mass sociogenic (functional) illness, and the concept of nocebo effects, and suggest that by analogy, Long-COVID is compatible with these descriptions. Fifth, we describe the current theoretical model of the mechanism underlying functional disorders, the Bayesian predictive coding model for perception. This model accounts for moderators that can make symptom inferences functionally inaccurate and therefore can explain how to understand common predisposing, precipitating, and perpetuating factors. Finally, we discuss the implications of this framework for improved public health messaging during a pandemic, with recommendations for the management of Long-COVID symptoms in healthcare systems. We argue that the current public health approach has induced fear of Long-COVID in the population, including from constant messaging about disabling symptoms of Long-COVID and theorizing irreversible tissue damage as the cause of Long-COVID. This has created a self-fulfilling prophecy by inducing the very predisposing, precipitating, and perpetuating factors for the syndrome. Finally, we introduce the term "Pandemic-Response Syndrome" to describe what previously was labeled Long-COVID. This alternative perspective aims to stimulate research and serve as a lesson learned to avoid a repeat performance in the future.
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Affiliation(s)
- Ari R Joffe
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - April Elliott
- Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada
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Vickers DM, Hardie J, Eberspaecher S, Chaufan C, Pelech S. Counterfactuals of effects of vaccination and public health measures on COVID-19 cases in Canada: what could have happened? Front Public Health 2023; 11:1173673. [PMID: 37228725 PMCID: PMC10203614 DOI: 10.3389/fpubh.2023.1173673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 03/30/2023] [Indexed: 05/27/2023] Open
Affiliation(s)
- David M. Vickers
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | | | | | - Claudia Chaufan
- Health Policy and Global Health, Faculty of Health, York University, Toronto, ON, Canada
| | - Steven Pelech
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
- Kinexus Bioinformatics Corporation, Vancouver, BC, Canada
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O’Brien SF, Caffrey N, Yi QL, Bolotin S, Janjua NZ, Binka M, Thanh CQ, Stein DR, Lang A, Colquhoun A, Pambrun C, Reedman CN, Drews SJ. Cross-Canada Variability in Blood Donor SARS-CoV-2 Seroprevalence by Social Determinants of Health. Microbiol Spectr 2023; 11:e0335622. [PMID: 36625634 PMCID: PMC9927354 DOI: 10.1128/spectrum.03356-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/07/2022] [Indexed: 01/11/2023] Open
Abstract
We compared the seroprevalence of SARS-CoV-2 anti-nucleocapsid antibodies in blood donors across Canadian regions in 2021. The seroprevalence was the highest in Alberta and the Prairies, and it was so low in Atlantic Canada that few correlates were observed. Being male and of young age were predictive of seropositivity. Racialization was associated with higher seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. Living in a materially deprived neighborhood predicted higher seroprevalence, but it was more linear across quintiles in Alberta and the Prairies, whereas in British Columbia and Ontario, the most affluent 60% were similarly low and the most deprived 40% similarly elevated. Living in a more socially deprived neighborhood (more single individuals and one parent families) was associated with lower seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. These data show striking variability in SARS-CoV-2 seroprevalence across regions by social determinants of health. IMPORTANCE Canadian blood donors are a healthy adult population that shows clear disparities associated with racialization and material deprivation. This underscores the pervasiveness of the socioeconomic gradient on SARS-CoV-2 infections in Canada. We identify regional differences in the relationship between SARS-CoV-2 seroprevalence and social determinants of health. Cross-Canada studies, such as ours, are rare because health information is under provincial jurisdiction and is not available in sufficient detail in national data sets, whereas other national seroprevalence studies have insufficient sample sizes for regional comparisons. Ours is the largest seroprevalence study in Canada. An important strength of our study is the interpretation input from a public health team that represented multiple Canadian provinces. Our blood donor seroprevalence study has informed Canadian public health policy at national and provincial levels since the start of the SARS-CoV-2 pandemic.
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Affiliation(s)
- Sheila F. O’Brien
- Epidemiology and Surveillance, Canadian Blood Services, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Niamh Caffrey
- Epidemiology and Surveillance, Canadian Blood Services, Ottawa, Ontario, Canada
| | - Qi-Long Yi
- Epidemiology and Surveillance, Canadian Blood Services, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Shelly Bolotin
- Center for Vaccine Preventable Disease, University of Toronto, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
| | - Naveed Z. Janjua
- BC Centre for Disease Control, British Columbia, Vancouver, Canada
- School of Population and Public Health, University of British Columbia, British Columbia, Vancouver, Canada
| | - Mawuena Binka
- BC Centre for Disease Control, British Columbia, Vancouver, Canada
| | - Caroline Quach Thanh
- Department of Microbiology, Infectious Diseases & Immunology, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Infection Prevention & Control, Clinical Department of Laboratory Medicine, CHU Sainte-Justine, Montreal, Quebec, Canada
| | - Derek R. Stein
- Cadham Provincial Laboratory, Winnipeg, Manitoba, Canada
- Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Amanda Lang
- Roy Romanow Provincial laboratory, Saskatchewan Health Authority, Regina, Saskatchewan, Canada
| | - Amy Colquhoun
- Population Health Assessment, Alberta Health, Edmonton, Alberta, Canada
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Chantale Pambrun
- Medical Affairs & Innovation, Canadian Blood Services, Ottawa, Ontario, Canada
- Department of Pathology & Laboratory Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Cassandra N. Reedman
- Epidemiology and Surveillance, Canadian Blood Services, Ottawa, Ontario, Canada
- Public Health Agency of Canada, Ottawa, Ontario, Canada
| | - Steven J. Drews
- Medical Microbiology Department, Canadian Blood Services, Edmonton, Alberta, Canada
- Department of Laboratory Medicine & Pathology, Division of Diagnostic and Applied Microbiology, University of Alberta, Edmonton, Alberta, Canada
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Rees EE, Avery BP, Carabin H, Carson CA, Champredon D, de Montigny S, Dougherty B, Nasri BR, Ogden NH. Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates. CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2022; 48:438-448. [PMID: 38162959 PMCID: PMC10756332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Background Non-pharmaceutical interventions (NPIs) aim to reduce the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections mostly by limiting contacts between people where virus transmission can occur. However, NPIs limit social interactions and have negative impacts on economic, physical, mental and social well-being. It is, therefore, important to assess the impact of NPIs on reducing the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations to justify their use. Methods Dynamic regression models accounting for autocorrelation in time series data were used with data from six Canadian provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Québec) to assess 1) the effect of NPIs (measured using a stringency index) on SARS-CoV-2 transmission (measured by the effective reproduction number), and 2) the effect of the number of hospitalized COVID-19 patients on the stringency index. Results Increasing stringency index was associated with a statistically significant decrease in the transmission of SARS-CoV-2 in Alberta, Saskatchewan, Manitoba, Ontario and Québec. The effect of stringency on transmission was time-lagged in all of these provinces except for Ontario. In all provinces except for Saskatchewan, increasing hospitalization rates were associated with a statistically significant increase in the stringency index. The effect of hospitalization on stringency was time-lagged. Conclusion These results suggest that NPIs have been effective in Canadian provinces, and that their implementation has been, in part, a response to increasing hospitalization rates of COVID-19 patients.
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Affiliation(s)
- Erin E Rees
- Public Health Risk Sciences Division, National Microbiology Laboratory (PHRSD), Public Health Agency of Canada, Saint-Hyacinthe, QC and Guelph, ON
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
| | - Brent P Avery
- Food-borne Disease and Antimicrobial Resistance Surveillance Division, Centre for Food-borne and Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON
| | - Hélène Carabin
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
- Faculty of Veterinary Medicine, Université de Montréal, Montréal, QC
| | - Carolee A Carson
- Food-borne Disease and Antimicrobial Resistance Surveillance Division, Centre for Food-borne and Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON
| | - David Champredon
- Public Health Risk Sciences Division, National Microbiology Laboratory (PHRSD), Public Health Agency of Canada, Saint-Hyacinthe, QC and Guelph, ON
| | - Simon de Montigny
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- School of Public Health, Université de Montréal, Montréal, QC
- Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, QC
| | - Brendan Dougherty
- Food-borne Disease and Antimicrobial Resistance Surveillance Division, Centre for Food-borne and Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON
| | - Bouchra R Nasri
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
- School of Public Health, Université de Montréal, Montréal, QC
| | - Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory (PHRSD), Public Health Agency of Canada, Saint-Hyacinthe, QC and Guelph, ON
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC
- Centre de recherche en santé publique (CReSP), Université de Montréal, Montréal, QC
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Meintrup D, Nowak-Machen M, Borgmann S. A Comparison of Germany and the United Kingdom Indicates That More SARS-CoV-2 Circulation and Less Restrictions in the Warm Season Might Reduce Overall COVID-19 Burden. LIFE (BASEL, SWITZERLAND) 2022; 12:life12070953. [PMID: 35888043 PMCID: PMC9322333 DOI: 10.3390/life12070953] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 12/03/2022]
Abstract
(1) Background: Between March 2020 and January 2022 severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) caused five infection waves in Europe. The first and the second wave was caused by wildtype SARS-CoV-2, while the following waves were caused by the variants of concern Alpha, Delta, and Omicron respectively. (2) Methods: In the present analysis, the first four waves were compared in Germany and the UK, in order to examine the COVID-19 epidemiology and its modulation by non-pharmaceutical interventions (NPI). (3) Results: The number of COVID-19 patients on intensive care units and the case fatality rate were used to estimate disease burden, the excess mortality to assess the net effect of NPI and other measures on the population. The UK was more severely affected by the first and the third wave while Germany was more affected by the second wave. The UK had a higher excess mortality during the first wave, afterwards the excess mortality in both countries was nearly identical. While most NPI were lifted in the UK in July 2021, the measures were kept and even aggravated in Germany. Nevertheless, in autumn 2021 Germany was much more affected, nearly resulting in a balanced sum of infections and deaths compared to the UK. Within the whole observation period, in Germany the number of COVID-19 patients on ICUs was up to four times higher than in the UK. Our results show that NPI have a limited effect on COVID-19 burden, seasonality plays a crucial role, and a higher virus circulation in a pre-wave situation could be beneficial. (4) Conclusions: Although Germany put much more effort and resources to fight the pandemic, the net balance of both countries was nearly identical, questioning the benefit of excessive ICU treatments and of the implementation of NPI, especially during the warm season.
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Affiliation(s)
- David Meintrup
- Faculty of Engineering and Management, University of Applied Sciences Ingolstadt, 85049 Ingolstadt, Germany
- Correspondence:
| | - Martina Nowak-Machen
- Department of Anaesthesia and Intensive Care Medicine, Ingolstadt Hospital, 85049 Ingolstadt, Germany;
- Department of Anesthesiology and Intensive Care Medicine, Teaching Faculty, University Hospital Tuebingen, Eberhard-Karls-University, 72076 Tuebingen, Germany
| | - Stefan Borgmann
- Department of Infectious Diseases and Infection Control, Ingolstadt Hospital, 85049 Ingolstadt, Germany;
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Carvalho MS, Bastos LS, Fuller T, Cruz OG, Damasceno L, Calvet G, Resende PC, Smith C, Whitworth J, Siqueira M, Brasil P. Incidence of SARS-CoV-2 over four epidemic waves in a low-resource community in Rio de Janeiro, Brazil: A prospective cohort study. LANCET REGIONAL HEALTH. AMERICAS 2022; 12:100283. [PMID: 35663637 PMCID: PMC9135359 DOI: 10.1016/j.lana.2022.100283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Background Incidence rates of SARS-CoV-2 infections in low-resource communities can inform vaccination strategies and non-pharmaceutical interventions (NPIs). Our objective was to estimate incidence over four epidemic waves in a slum in Rio de Janeiro, a proxy for economically deprived areas in the Global South. Methods Prospective cohort of children and household contacts screened for SARS-CoV-2 by PCR and serology (IgG). The incidence density of PCR positive infections estimated for each wave - the first wave, Zeta, Gamma and Delta - was compared to an index combining NPIs and vaccination coverage. Findings 718 families and 2501 individuals were enrolled, from May 2020 to November 2021. The incidence density of SARS-CoV-2 infection due to the first wave was 2, 3 times that of the other waves. The incidence among children was lower than that of older participants, except in later waves, when vaccination of the elderly reached 90%. Household agglomeration was significantly associated with incidence only during the first wave. Interpretation The incidence of infection greatly exceeded rates reported in similar cohorts. The observed reduction in incidence in the elderly during the Delta variant wave, in spite of the rollback of NPIs, can be attributed to increased vaccine coverage. The high incidence in young people reinforces the importance of vaccination in this age group, a policy that has yet to receive the full support of some sectors of society. Funding UK Medical Research Council, Foundation for the Advancement of Science of the State of Rio de Janeiro, National Council for Scientific and Technological Development.
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Affiliation(s)
- Marilia Sa Carvalho
- Scientific Computation Program, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil
| | | | - Trevon Fuller
- Acute Febrile Illnesses Laboratory, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, Av. Brasil, 4365 - Manguinhos, Rio de Janeiro, RJ 21040-900, Brazil
| | | | - Luana Damasceno
- Acute Febrile Illnesses Laboratory, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, Av. Brasil, 4365 - Manguinhos, Rio de Janeiro, RJ 21040-900, Brazil
| | - Guilherme Calvet
- Acute Febrile Illnesses Laboratory, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, Av. Brasil, 4365 - Manguinhos, Rio de Janeiro, RJ 21040-900, Brazil
| | - Paola Cristina Resende
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Brazil
| | - Chris Smith
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jimmy Whitworth
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Marilda Siqueira
- Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Brazil
| | - Patricia Brasil
- Acute Febrile Illnesses Laboratory, Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation, Av. Brasil, 4365 - Manguinhos, Rio de Janeiro, RJ 21040-900, Brazil,Corresponding author.
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