Testing lockdown measures in epidemic outbreaks through mean-field models considering the social structure

Abstract

Lately, concepts such as lockdown, quarantine, and social distancing have become very relevant since they have been associated with essential measures in the prevention and mitigation of COVID-19. While some conclusions about the effectiveness of these measures could be drawn from field observations, many mathematical models aimed to provide some clues. However, the reliability of these models is questioned, especially if the social structure is not included in them. In this work, we propose a mesoscopic model that allows the evaluation of the effect of measures such as social distancing and lockdown when the social topology is taken into account. The model is able to predict successive waves of infections without the need to account for reinfections, and it can qualitatively reproduce the wave patterns observed across many countries during the COVID-19 pandemic. Subsequent waves can have a higher peak of infections if the restrictiveness of the lockdown is above a certain threshold. The model is flexible and can implement various social distancing strategies by adjusting the restrictiveness and the duration of lockdown measures or specifying whether they occur once or repeatedly. It also includes the option to consider essential workers that do not isolate during a lockdown.