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Quantifying the Shift in Social Contact Patterns in Response to Non-Pharmaceutical Interventions

25 Pages Posted: 13 Oct 2020

See all articles by Zachary McCarthy

Zachary McCarthy

York University - Laboratory for Industrial and Applied Mathematics

Yanyu Xiao

University of Cincinnati - Department of Mathematical Sciences

Francesca Scarabel

York University - Laboratory for Industrial and Applied Mathematics

Biao Tang

York University - Laboratory for Industrial and Applied Mathematics

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Kyeongah Nah

York University - Laboratory for Industrial and Applied Mathematics

Jane M. Heffernan

York University - Centre for Diseases Modeling (CDM)

Ali Asgary

York University - Disaster & Emergency Management

V. Kumar Murty

University of Toronto - Department of Mathematics

Nicholas Ogden

Public Health Agency Canada - Public Health Risk Sciences Division

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics

More...

Abstract

Background: Social contact mixing plays a critical role in influencing the transmission routes of infectious diseases. Quantifying social contact mixing patterns and their variations in a rapidly evolving pandemic intervened by changing public health measures is key for retroactive evaluation and proactive assessment of the effectiveness of different age- and setting-specific interventions. 

Methods: We performed an analysis of the intervention escalation phase of the COVID-19 epidemic in Ontario, Canada. Specifically, we integrated social contact patterns derived from empirical data with a disease transmission model, that enabled the usage of age-stratified COVID-19 incidence data to infer age-specific susceptibility, daily contact mixing patterns in workplace, household, school, and community settings; and transmission acquired in these settings under different physical distancing measures. 

Findings: We quantified the age- and setting (household, workplace, community, and school)-specific mixing patterns and their evolution during the escalation of public health interventions in Ontario, Canada. We estimated a reduction in the average individual contact rate from 12·27 to 6·58 per day, with an increase in household contacts, following the implementation of control measures. We estimated increasing trends by age in both the susceptibility to infection by SARS-CoV-2 and the proportion of symptomatic individuals diagnosed. 

Interpretation: Inferring the age- and setting-specific social contact mixing and key age-stratified epidemiological parameters, in the presence of evolving control measures, is critical to inform decision- and policy-making for the current COVID-19 pandemic.

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

Declaration of Interests: The authors declare that they have no conflict of interest.

Keywords: social contact patterns, non-pharmaceutical interventions, COVID-19

Suggested Citation

McCarthy, Zachary and Xiao, Yanyu and Scarabel, Francesca and Tang, Biao and Bragazzi, Nicola Luigi and Nah, Kyeongah and Heffernan, Jane M. and Asgary, Ali and Murty, V. Kumar and Ogden, Nicholas and Wu, Jianhong, Quantifying the Shift in Social Contact Patterns in Response to Non-Pharmaceutical Interventions. Available at SSRN: https://ssrn.com/abstract=3706041 or http://dx.doi.org/10.2139/ssrn.3706041

Zachary McCarthy

York University - Laboratory for Industrial and Applied Mathematics

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Yanyu Xiao

University of Cincinnati - Department of Mathematical Sciences ( email )

Cincinnati, OH 45221-0389
United States

Francesca Scarabel

York University - Laboratory for Industrial and Applied Mathematics

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Biao Tang

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

Canada

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Canada

Kyeongah Nah

York University - Laboratory for Industrial and Applied Mathematics

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Jane M. Heffernan

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

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Ali Asgary

York University - Disaster & Emergency Management ( email )

Canada

V. Kumar Murty

University of Toronto - Department of Mathematics ( email )

Toronto, Ontario M5S 3G3
Canada

Nicholas Ogden

Public Health Agency Canada - Public Health Risk Sciences Division ( email )

Ontario
Canada

Jianhong Wu (Contact Author)

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

Canada

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