Modelling scenarios of the epidemic of COVID-19 in Canada.

Autor:	Ogden NH; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON., Fazil A; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON., Arino J; Department of Mathematics & Data Science NEXUS, University of Manitoba, Winnipeg, MB., Berthiaume P; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON., Fisman DN; Dalla Lana School of Public Health, University of Toronto, Toronto, ON., Greer AL; Department of Population Medicine, University of Guelph, Guelph, ON., Ludwig A; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON., Ng V; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON., Tuite AR; Dalla Lana School of Public Health, University of Toronto, Toronto, ON., Turgeon P; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON., Waddell LA; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON., Wu J; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON.; Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, ON.
Jazyk:	angličtina
Zdroj:	Canada communicable disease report = Releve des maladies transmissibles au Canada [Can Commun Dis Rep] 2020 Jun 04; Vol. 46 (8), pp. 198-204. Date of Electronic Publication: 2020 Jun 04 (Print Publication: 2020).
DOI:	10.14745/ccdr.v46i06a08
Abstrakt:	Background: Severe acute respiratory syndrome virus 2 (SARS-CoV-2), likely a bat-origin coronavirus, spilled over from wildlife to humans in China in late 2019, manifesting as a respiratory disease. Coronavirus disease 2019 (COVID-19) spread initially within China and then globally, resulting in a pandemic. Objective: This article describes predictive modelling of COVID-19 in general, and efforts within the Public Health Agency of Canada to model the effects of non-pharmaceutical interventions (NPIs) on transmission of SARS-CoV-2 in the Canadian population to support public health decisions. Methods: The broad objectives of two modelling approaches, 1) an agent-based model and 2) a deterministic compartmental model, are described and a synopsis of studies is illustrated using a model developed in Analytica 5.3 software. Results: Without intervention, more than 70% of the Canadian population may become infected. Non-pharmaceutical interventions, applied with an intensity insufficient to cause the epidemic to die out, reduce the attack rate to 50% or less, and the epidemic is longer with a lower peak. If NPIs are lifted early, the epidemic may rebound, resulting in high percentages (more than 70%) of the population affected. If NPIs are applied with intensity high enough to cause the epidemic to die out, the attack rate can be reduced to between 1% and 25% of the population. Conclusion: Applying NPIs with intensity high enough to cause the epidemic to die out would seem to be the preferred choice. Lifting disruptive NPIs such as shut-downs must be accompanied by enhancements to other NPIs to prevent new introductions and to identify and control any new transmission chains.
Databáze:	MEDLINE
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