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Milwid RM, Gabriele-Rivet V, Ogden NH, Turgeon P, Fazil A, London D, de Montigny S, Rees EE. A methodology for estimating SARS-CoV-2 importation risk by air travel into Canada between July and November 2021. BMC Public Health 2024; 24:1088. [PMID: 38641571 PMCID: PMC11027292 DOI: 10.1186/s12889-024-18563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 04/09/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Estimating rates of disease importation by travellers is a key activity to assess both the risk to a country from an infectious disease emerging elsewhere in the world and the effectiveness of border measures. We describe a model used to estimate the number of travellers infected with SARS-CoV-2 into Canadian airports in 2021, and assess the impact of pre-departure testing requirements on importation risk. METHODS A mathematical model estimated the number of essential and non-essential air travellers infected with SARS-CoV-2, with the latter requiring a negative pre-departure test result. The number of travellers arriving infected (i.e. imported cases) depended on air travel volumes, SARS-CoV-2 exposure risk in the departure country, prior infection or vaccine acquired immunity, and, for non-essential travellers, screening from pre-departure molecular testing. Importation risk was estimated weekly from July to November 2021 as the number of imported cases and percent positivity (PP; i.e. imported cases normalised by travel volume). The impact of pre-departure testing was assessed by comparing three scenarios: baseline (pre-departure testing of all non-essential travellers; most probable importation risk given the pre-departure testing requirements), counterfactual scenario 1 (no pre-departure testing of fully vaccinated non-essential travellers), and counterfactual scenario 2 (no pre-departure testing of non-essential travellers). RESULTS In the baseline scenario, weekly imported cases and PP varied over time, ranging from 145 to 539 cases and 0.15 to 0.28%, respectively. Most cases arrived from the USA, Mexico, the United Kingdom, and France. While modelling suggested that essential travellers had a higher weekly PP (0.37 - 0.65%) than non-essential travellers (0.12 - 0.24%), they contributed fewer weekly cases (62 - 154) than non-essential travellers (84 - 398 per week) given their lower travel volume. Pre-departure testing was estimated to reduce imported cases by one third (counterfactual scenario 1) to one half (counterfactual scenario 2). CONCLUSIONS The model results highlighted the weekly variation in importation by traveller group (e.g., reason for travel and country of departure) and enabled a framework for measuring the impact of pre-departure testing requirements. Quantifying the contributors of importation risk through mathematical simulation can support the design of appropriate public health policy on border measures.
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Affiliation(s)
- Rachael M Milwid
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Vanessa Gabriele-Rivet
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada.
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada.
| | - Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Patricia Turgeon
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Aamir Fazil
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Guelph, ON, Canada
| | - David London
- Physique Des Particules, Université de Montréal, Faculté Des Arts Et Des Sciences, Montréal, QC, Canada
| | - Simon de Montigny
- Emergency Management Branch, Global Public Health Intelligence Network Tiger Team, Public Health Agency of Canada, Ottawa, ON, Canada
| | - Erin E Rees
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
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Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
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Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
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Ogden NH, Turgeon P, Fazil A, Clark J, Gabriele-Rivet V, Tam T, Ng V. Counterfactuals of effects of vaccination and public health measures on COVID-19 cases in Canada: What could have happened? CANADA COMMUNICABLE DISEASE REPORT = RELEVE DES MALADIES TRANSMISSIBLES AU CANADA 2022; 48:292-302. [PMID: 37334255 PMCID: PMC10275398 DOI: 10.14745/ccdr.v48i78a01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This study illustrates what may have happened, in terms of coronavirus disease 2019 (COVID-19) infections, hospitalizations and deaths in Canada, had public health measures not been used to control the COVID-19 epidemic, and had restrictions been lifted with low levels of vaccination, or no vaccination, of the Canadian population. The timeline of the epidemic in Canada, and the public health interventions used to control the epidemic, are reviewed. Comparisons against outcomes in other countries and counterfactual modelling illustrate the relative success of control of the epidemic in Canada. Together, these observations show that without the use of restrictive measures and without high levels of vaccination, Canada could have experienced substantially higher numbers of infections and hospitalizations and almost a million deaths.
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Affiliation(s)
- Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC and Guelph, ON
| | - Patricia Turgeon
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC and Guelph, ON
| | - Aamir Fazil
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC and Guelph, ON
| | - Julia Clark
- Office of the Chief Public Health Officer, Public Health Agency of Canada, Ottawa, ON
| | - Vanessa Gabriele-Rivet
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC and Guelph, ON
| | - Theresa Tam
- Office of the Chief Public Health Officer, Public Health Agency of Canada, Ottawa, ON
| | - Victoria Ng
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC and Guelph, ON
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Gabriele-Rivet V, Spence KL, Ogden NH, Fazil A, Turgeon P, Otten A, Waddell LA, Ng V. Modelling the impact of age-stratified public health measures on SARS-CoV-2 transmission in Canada. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210834. [PMID: 34737875 PMCID: PMC8562391 DOI: 10.1098/rsos.210834] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/22/2021] [Indexed: 06/13/2023]
Abstract
Public health measures applied exclusively within vulnerable populations have been suggested as an alternative to community-wide interventions to mitigate SARS-CoV-2 transmission. With the population demography and healthcare capacity of Canada as an example, a stochastic age-stratified agent-based model was used to explore the progression of the COVID-19 epidemic under three intervention scenarios (infection-preventing vaccination, illness-preventing vaccination and shielding) in individuals above three age thresholds (greater than or equal to 45, 55 and 65 years) while lifting shutdowns and physical distancing in the community. Compared with a scenario with sustained community-wide measures, all age-stratified intervention scenarios resulted in a substantial epidemic resurgence, with hospital and ICU bed usage exceeding healthcare capacities even at the lowest age threshold. Individuals under the age threshold were severely impacted by the implementation of all age-stratified interventions, with large numbers of avoidable deaths. Among all explored scenarios, shielding older individuals led to the most detrimental outcomes (hospitalizations, ICU admissions and mortality) for all ages, including the targeted population. This study suggests that, in the absence of community-wide measures, implementing interventions exclusively within vulnerable age groups could result in unmanageable levels of infections, with serious outcomes within the population. Caution is therefore warranted regarding early relaxation of community-wide restrictions.
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Affiliation(s)
- Vanessa Gabriele-Rivet
- Public Health Risk Sciences Division, National Microbiology Laboratory, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Guelph, Ontario and St-Hyacinthe, Québec, Canada
| | - Kelsey L. Spence
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Nicholas H. Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Guelph, Ontario and St-Hyacinthe, Québec, Canada
| | - Aamir Fazil
- Public Health Risk Sciences Division, National Microbiology Laboratory, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Guelph, Ontario and St-Hyacinthe, Québec, Canada
| | - Patricia Turgeon
- Public Health Risk Sciences Division, National Microbiology Laboratory, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Guelph, Ontario and St-Hyacinthe, Québec, Canada
| | - Ainsley Otten
- Public Health Risk Sciences Division, National Microbiology Laboratory, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Guelph, Ontario and St-Hyacinthe, Québec, Canada
| | - Lisa A. Waddell
- Public Health Risk Sciences Division, National Microbiology Laboratory, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Guelph, Ontario and St-Hyacinthe, Québec, Canada
| | - Victoria Ng
- Public Health Risk Sciences Division, National Microbiology Laboratory, Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Guelph, Ontario and St-Hyacinthe, Québec, Canada
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