Abstract
AbstractDuring the COVID-19 pandemic, aggregated mobility data was frequently used to estimate changing social contact rates. By taking contact matrices estimated pre-pandemic, and transforming these using pandemic-era mobility data, epidemiologists attempted to predict the number of contacts individuals were expected to have during large-scale restrictions. This study explores the most effective method for this transformation, comparing it to the accuracy of pandemic-era contact surveys. We compared four methods for scaling synthetic contact matrices: two using fitted regression models and two using “naïve” mobility or mobility squared models. The regression models were fitted using 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 naïve models, even when the regression models were applied to Belgium and the Netherlands. Across all countries investigated, the naïve model using mobility alone was the least accurate, followed by the naïve model using mobility squared. 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 “naïve” model based on raw mobility data. If a naïve model is to be used, mobility squared may be a better predictor of contact rates than mobility per se.
Publisher
Cold Spring Harbor Laboratory