Estimating social contact rates for the COVID-19 pandemic using Google mobility and pre-pandemic contact surveys

Abstract

During the COVID-19 pandemic, aggregated mobility data was frequently used to estimate changing social contact rates. By taking pre-pandemic contact matrices, and transforming these using pandemic-era mobility data, infectious disease modellers attempted to predict the effect of large-scale behavioural changes on contact rates. This study explores the most accurate method for this transformation, using pandemic-era contact surveys as ground truth. We compared four methods for scaling synthetic contact matrices: two using fitted regression models and two using "na & iuml;ve" mobility or mobility squared models. The regression models were fitted using the CoMix contact survey and Google mobility data from the UK over March 2020-March 2021. The four models were then used to scale synthetic contact matrices-a representation of pre-pandemic behaviour-using mobility data from the UK, Belgium and the Netherlands to predict the number of contacts expected in "work" and "other" settings for a given mobility level. We then compared partial reproduction numbers estimated from the four models with those calculated directly from CoMix contact matrices across the three countries. The accuracy of each model was assessed using root mean squared error. The fitted regression models had substantially more accurate predictions than the na & iuml;ve models, even when models were applied to out-of-sample data from the UK, Belgium and the Netherlands. Across all countries investigated, the linear fitted regression model was the most accurate and the na & iuml;ve model using mobility alone was the least accurate. When attempting to estimate social contact rates during a pandemic without the resources available to conduct contact surveys, using a model fitted to data from another pandemic context is likely to be an improvement over using a "na & iuml;ve" model based on mobility data alone. If a na & iuml;ve model is to be used, mobility squared may be a better predictor of contact rates than mobility per se.This research was conducted as part of the first authors (Em Prestige) pre-doctoral fellowship funding by the National Institute for Health and Care Research (NIHR); grant number: NIHR301994. Funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Em Prestige is funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Modelling and Health Economics, a partnership between the UK Health Security Agency, Imperial College London and LSHTM (grant code NIHR200908). Disclaimer: “The views expressed are those of the author(s) and not necessarily those of the NIHR, UK Health Security Agency or the Department of Health and Social Care.