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Quantifying the shift in social contact patterns in response to non-pharmaceutical interventions.
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.
  • McCarthy Z; Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada.
  • Xiao Y; Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada.
  • Scarabel F; Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH USA.
  • Tang B; Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada.
  • Bragazzi NL; Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada.
  • Nah K; CDLab-Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy.
  • Heffernan JM; Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada.
  • Asgary A; Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada.
  • Murty VK; Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada.
  • Ogden NH; Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada.
  • Wu J; Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada.
J Math Ind ; 10(1): 28, 2020.
Article in English | MEDLINE | ID: covidwho-961355
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study Language: English Journal: J Math Ind Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study Language: English Journal: J Math Ind Year: 2020 Document Type: Article