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

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

doi:10.1186/s13362-020-00096-y

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.

Highlights

In response to the current COVID-19 pandemic, interventions aimed at controlling local transmission such as school and non-essential business closures, physical distancing, contact tracing, enhanced surveillance and diagnostic testing have been adopted throughout many nations of the world [1]
Key factors associated with demographic heterogeneities such as age-dependent social contact mixing and susceptibility to infection and their implications on transmission patterns of COVID-19 have been explored in prior works [9,10,11]
2 Methods 2.1 Transmission model We extended the COVID-19 transmission dynamics model introduced in prior studies [2,3,4,5] to include age structure

Summary

Introduction

In response to the current COVID-19 pandemic, interventions aimed at controlling local transmission such as school and non-essential business closures, physical distancing, contact tracing, enhanced surveillance and diagnostic testing have been adopted throughout many nations of the world [1]. The efficacy of these measures and their influence on the trajectory of local epidemics has been quantified in a series of mechanistic modelling studies [2,3,4,5], as well as in systematic reviews and meta-analyses [6,7,8]. A comprehensive modelling approach that integrates key heterogeneities by age/setting and a generalized intervention package accounting for evolving non-pharmaceutical interventions, diagnostic testing, contact tracing, and case isolation may be utilized for a broad spectrum of risk assessment, preparedness planning, reopening measures, scenario analysis and intervention evaluation

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