Unravelling Causal Associations between Population Mobility and COVID-19 Cases in Spain: a Transfer Entropy Analysis

Abstract

Human mobility is a well-known factor in the spread of infectious diseases. During the COVID-19 pandemic, the rapid spread of the SARS-CoV-2 virus led to healthcare systems collapsing in numerous countries, such as Spain and Italy, resulting in a significant number of deaths. To avoid such disastrous outcomes in the future, it is vital to understand how population mobility is linked to the spread of infectious diseases. To assess that, we applied an information theoretic approach called transfer entropy (TE) to measure the influence of the number of infected people travelling between two localities on the future number of infected people in the destination. We first validated our approach using simulated data from a SIR epidemiological model and found that the mobility-based TE was effective in filtering out non-causal influences that could otherwise arise, thereby successfully recovering the epidemic’s spreading patterns and the mobility network topology. We then applied the mobility-based TE to analyse the COVID-19 pandemic in Spain. We identified which regions acted as the main drivers of the pandemic at different periods, both globally and locally. Our results unravelled significant epidemiological events such as the outbreak in Lleida during the Summer of 2020, caused by the influx of temporary workers. We also analysed the effects of a non-pharmaceutical intervention in Catalunya, using mobility- based TE to compare the infection dynamics with a control region. These results help clarify how human mobility influences the dynamic spread of infectious diseases and could be used to inform future non-pharmaceutical interventions.