Quantifying the shift in social contact patterns in response to non-pharmaceutical interventions

Simple item page

Simple item page

Full item details

dc.contributor.author

McCarthy, Zachary

Xiao, Yanyu

Scarabel, Francesca

Tang, Biao

Bragazzi, Nicola Luigi

Nah, Kyeongah

Heffernan, Jane M.

Asgary, Ali

Murty, V. Kumar

Ogden, Nicholas H.

Wu, Jianhong

dc.date.accessioned

2024-07-30T15:39:15Z

dc.date.available

2024-07-30T15:39:15Z

dc.date.issued

2020-12-01

dc.description.abstract - en

Social contact mixing plays a critical role in influencing the transmission routes of infectious diseases. Moreover, 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. Contact mixing patterns have been used to inform COVID-19 pandemic public health decision-making; but a rigorously justified methodology to identify setting-specific contact mixing patterns and their variations in a rapidly developing pandemic, which can be informed by readily available data, is in great demand and has not yet been established. Here we fill in this critical gap by developing and utilizing a novel methodology, integrating social contact patterns derived from empirical data with a disease transmission model, that enables the usage of age-stratified 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. We demonstrated the utility of this methodology by performing an analysis of the COVID-19 epidemic in Ontario, Canada. 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 contacts per day, with an increase in household contacts, following the implementation of control measures. We also estimated increasing trends by age in both the susceptibility to infection by SARS-CoV-2 and the proportion of symptomatic individuals diagnosed. 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.

dc.identifier.doi

10.1186/s13362-020-00096-y

dc.identifier.uri

https://open-science.canada.ca/handle/123456789/2760

dc.language.iso

en

dc.publisher

Springer

dc.rights - en

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights - fr

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights.openaccesslevel - en

Gold

dc.rights.openaccesslevel - fr

Or

dc.rights.uri - en

https://creativecommons.org/licenses/by/4.0/

dc.rights.uri - fr

https://creativecommons.org/licenses/by/4.0/deed.fr

dc.subject - en

Health

Coronavirus diseases

Modelling

dc.subject - fr

Santé

Maladie à coronavirus

Modélisation

dc.subject.en - en

Health

Coronavirus diseases

Modelling

dc.subject.fr - fr

Santé

Maladie à coronavirus

Modélisation

dc.title - en

Quantifying the shift in social contact patterns in response to non-pharmaceutical interventions

dc.type - en

Article

dc.type - fr

Article

local.acceptedmanuscript.articlenum

28

local.article.journalissue

1

local.article.journaltitle

Journal of Mathematics in Industry

local.article.journalvolume

10

local.peerreview - en

Yes

local.peerreview - fr

Oui

Download(s)

Original bundle

Now showing 1 - 1 of 1

Name: mccarthy-quantifying-shift-social-contact-patterns-response-non-pharmaceutical-interventions.pdf

Size: 3.64 MB

Format: PDF

Download file