De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study.

COVID-19 mathematical model pandemics physical and social distancing relaxation reopening

Journal

Biology
ISSN: 2079-7737
Titre abrégé: Biology (Basel)
Pays: Switzerland
ID NLM: 101587988

Informations de publication

Date de publication:
16 May 2020
Historique:
received: 01 05 2020
revised: 14 05 2020
accepted: 14 05 2020
entrez: 21 5 2020
pubmed: 21 5 2020
medline: 21 5 2020
Statut: epublish

Résumé

Since the beginning of the COVID-19 pandemic, most Canadian provinces have gone through four distinct phases of social distancing and enhanced testing. A transmission dynamics model fitted to the cumulative case time series data permits us to estimate the effectiveness of interventions implemented in terms of the contact rate, probability of transmission per contact, proportion of isolated contacts, and detection rate. This allows us to calculate the control reproduction number during different phases (which gradually decreased to less than one). From this, we derive the necessary conditions in terms of enhanced social distancing, personal protection, contact tracing, quarantine/isolation strength at each escalation phase for the disease control to avoid a rebound. From this, we quantify the conditions needed to prevent epidemic rebound during de-escalation by simply reversing the escalation process.

Identifiants

pubmed: 32429450
pii: biology9050100
doi: 10.3390/biology9050100
pmc: PMC7284446
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Biao Tang (B)

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.
The Interdisciplinary Research Center for Mathematics and Life Sciences, Xi'an Jiaotong University, Xi'an 710049, China.

Francesca Scarabel (F)

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.
CDLab-Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy.

Nicola Luigi Bragazzi (NL)

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.

Zachary McCarthy (Z)

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.

Michael Glazer (M)

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.

Yanyu Xiao (Y)

Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221-0025, USA.

Jane M Heffernan (JM)

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.
Modelling Infection and Immunity Lab, Centre for Disease Modelling, Department of Mathematics & Statistics, York University, Toronto, ON M3J 1P3, Canada.

Ali Asgary (A)

Disaster & Emergency Management, School of Administrative Studies & Advanced Disaster & Emergency Rapid-response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada.

Nicholas Hume Ogden (NH)

Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC J2S 2M2, Canada.

Jianhong Wu (J)

Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.

Classifications MeSH