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

DOI10.1186/s13362-020-00096-yzbMath1469.92119OpenAlexW3110352068WikidataQ113179285 ScholiaQ113179285MaRDI QIDQ1980863

V. Kumar Murty, Zachary McCarthy, Jianhong Wu, Nicholas H. Ogden, Biao Tang, Yanyu Xiao, Francesca Scarabel, Nicola Luigi Bragazzi, Jane M. Heffernan, Ali Asgary, Kyeongah Nah

Publication date: 8 September 2021

Published in: Journal of Mathematics in Industry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/s13362-020-00096-y

zbMATH Keywords

mathematical modellingheterogeneous mixingtransmission modelCOVID-19non-pharmaceutical interventionsintervention evaluation

Mathematics Subject Classification ID

Epidemiology (92D30) Social networks; opinion dynamics (91D30)

Related Items (3)

Modeling SARS-CoV-2 spread with dynamic isolation ⋮ COVID-19 in Ontario: modelling the pandemic by age groups incorporating preventative rapid-testing ⋮ A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian province

Cites Work

• Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada

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