Journal Article

Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era

Jit, Mark, Prem, Kiesha, Davies, Nicholas G., Klepac, Petra, Eggo, Rosalind M., Cook, Alex R., Zandvoort, Kevin van

2021

Overview

Mathematical models have played a key role in understanding the spread of directly-transmissible infectious diseases such as Coronavirus Disease 2019 (COVID-19), as well as the effectiveness of public health responses. As the risk of contracting directly-transmitted infections depends on who interacts with whom, mathematical models often use contact matrices to characterise the spread of infectious pathogens. These contact matrices are usually generated from diary-based contact surveys. However, the majority of places in the world do not have representative empirical contact studies, so synthetic contact matrices have been constructed using more widely available setting-specific survey data on household, school, classroom, and workplace composition combined with empirical data on contact patterns in Europe. In 2017, the largest set of synthetic contact matrices to date were published for 152 geographical locations. In this study, we update these matrices for populations up to age 80 with the most recent data and extend our analysis to 177 geographical locations. Due to the observed geographic differences within countries, we also quantify contact patterns in rural and urban settings where data is available. Further, we compare both the 2017 and 2020 synthetic matrices to out-of-sample empirically-constructed contact matrices, and explore the effects of using both the empirical and synthetic contact matrices when modelling physical distancing interventions for the COVID-19 pandemic. We found that the synthetic contact matrices show qualitative similarities to the contact patterns in the empirically-constructed contact matrices. Models parameterised with the empirical and synthetic matrices generated similar findings with few differences observed in age groups where the empirical matrices have missing or aggregated age groups. This finding means that synthetic contact matrices may be used in modelling outbreaks in settings for which empirical studies have yet to be conducted.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Age Distribution / Age groups / Contact tracing / Coronaviruses / COVID-19 / COVID-19 - epidemiology / COVID-19 - virology / Demography / Disease transmission / Distribution / Earth Sciences / Education / Empirical analysis / Empirical Research / Epidemics / Europe - epidemiology / Fertility / GDP / Geographical locations / Geography / Gross Domestic Product / Health risks / Households / Humans / Infectious diseases / Mathematical analysis / Mathematical models / Matrices (mathematics) / Medicine and Health Sciences / Methods / Mortality / Pandemics / People and Places / Polls & surveys / Population / Public health / Research and Analysis Methods / Rural areas / Rural Population / SARS-CoV-2 - isolation & purification / Severe acute respiratory syndrome coronavirus 2 / Social Sciences / Students / Surveys / Teachers / United Kingdom / Urban areas / Urban environments / Urban Population / Viral diseases