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

Biology, Forthcoming

15 Pages Posted: 18 May 2020

See all articles by Biao Tang

Biao Tang

York University - Laboratory for Industrial and Applied Mathematics

Francesca Scarabel

York University - Laboratory for Industrial and Applied Mathematics

Nicola Luigi Bragazzi

York University

Zachary McCarthy

York University - Laboratory for Industrial and Applied Mathematics

Michael Glazer

York University

Yanyu Xiao

University of Cincinnati - Department of Mathematical Sciences

Jane M. Heffernan

York University - Centre for Diseases Modeling (CDM)

Ali Asgary

York University

Nicholas Ogden

Public Health Agency of Canada

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics

Date Written: May 1, 2020

Abstract

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 is gradually decreasing until it becomes smaller than one). Therefore, we can derive the necessary and sufficient 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. This research aims to quantify these conditions for de-escalation by simply reversing the escalation process.

Note: Funding: This research has been funded by the Canadian Institute of Health Research (CIHR) 2019 Novel Coronavirus (COVID-19) rapid research program.

Conflict of Interest: The authors declare no conflict of interest.

Suggested Citation

Tang, Biao and Scarabel, Francesca and Bragazzi, Nicola Luigi and McCarthy, Zachary and Glazer, Michael and Xiao, Yanyu and Heffernan, Jane M. and Asgary, Ali and Ogden, Nicholas and Wu, Jianhong, 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 (May 1, 2020). Biology, Forthcoming , Available at SSRN: https://ssrn.com/abstract=3589954

Biao Tang

York University - Laboratory for Industrial and Applied Mathematics ( email )

Canada

Francesca Scarabel

York University - Laboratory for Industrial and Applied Mathematics

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Nicola Luigi Bragazzi (Contact Author)

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Zachary McCarthy

York University - Laboratory for Industrial and Applied Mathematics

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Michael Glazer

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Yanyu Xiao

University of Cincinnati - Department of Mathematical Sciences ( email )

Cincinnati, OH 45221-0389
United States

Jane M. Heffernan

York University - Centre for Diseases Modeling (CDM) ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Ali Asgary

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Nicholas Ogden

Public Health Agency of Canada ( email )

Canada

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics ( email )

Canada

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