Assessing the impact of varying levels of case detection and contact tracing on COVID-19 transmission in Canada during lifting of restrictive closures using a dynamic compartmental mode
- DOI
- Language of the publication
- English
- Date
- 2020-11-05
- Type
- Article
- Author(s)
- Ludwig, Antoinette
- Berthiaume, Philippe
- Orpana, Heather
- Nadeau, Claude
- Diasparra, Maikol
- Barnes, Joel
- Hennessy, Deirdre
- Otten, Ainsley
- Ogden, Nicholas
- Publisher
- CCDR
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic began with a detected cluster of pneumonia cases in Wuhan, China in December 2019. Endemic transmission was recognized in Canada in early February 2020, making it urgent for public health stakeholders to have access to robust and reliable tools to support decision-making for epidemic management. The objectives of this paper are to present one of these tools—an aged-stratified dynamic compartmental model developed by the Public Health Agency of Canada in collaboration with Statistics Canada—and to model the impact of non-pharmaceutical interventions on the attack rate of COVID-19 infection in Canada. METHODS: This model simulates the impact of different levels of non-pharmaceutical interventions, including case detection/isolation, contact tracing/quarantine and changes in the level of physical distancing in Canada, as restrictive closures began to be lifted in May 2020. RESULTS: This model allows us to highlight the importance of a relatively high level of detection and isolation of cases, as well as tracing and quarantine of individuals in contact with those cases, in order to avoid a resurgence of the epidemic in Canada as restrictive closures are lifted. Some level of physical distancing by the public will also likely need to be maintained. CONCLUSION: This study underlines the importance of a cautious approach to lifting restrictive closures in this second phase of the epidemic. This approach includes efforts by public health to identify cases and trace contacts, and to encourage Canadians to get tested if they are at risk of having been infected and to maintain physical distancing in public areas.
Plain language summary
The objectives of this paper are to present an aged-stratified dynamic compartmental model developed by the Public Health Agency of Canada in collaboration with Statistics Canada and to model the impact of non-pharmaceutical interventions on the attack rate of COVID-19 infection in Canada. This model simulates the impact of different levels of non-pharmaceutical interventions, including case detection/isolation, contact tracing/quarantine and changes in the level of physical distancing in Canada, as restrictive closures began to be lifted in May 2020. This model allows us to highlight the importance of a relatively high level of detection and isolation of cases, as well as tracing and quarantine of individuals in contact with those cases, in order to avoid a resurgence of the epidemic in Canada as restrictive closures are lifted. Some level of physical distancing by the public will also likely need to be maintained. This study underlines the importance of a cautious approach to lifting restrictive closures in this second phase of the epidemic. This approach includes efforts by public health to identify cases and trace contacts, and to encourage Canadians to get tested if they are at risk of having been infected and to maintain physical distancing in public areas.
Subject
- Health
Keywords
- Covid-19,
- case detection,
- contact tracing,
- dynamic compartmental model,
- Canada
Rights
Peer review
Yes
Open access level
Gold
Article
- Journal title
- Canada Communicable Disease Report
- Journal volume
- 46
- Journal issue
- 11/12
Relation
- Is translation of:
- https://open-science.canada.ca/handle/123456789/2514
Citation(s)
Ludwig A, Berthiaume P, Orpana H, et al. Assessing the impact of varying levels of case detection and contact tracing on COVID-19 transmission in Canada during lifting of restrictive closures using a dynamic compartmental model. Canada communicable disease report. 2020;46(11-12):409-421. doi:https://doi.org/10.14745/ccdr.v46i1112a08