Abstract
To date, the only effective means to respond to the spreading of the COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reducing their social and economic consequences. Here, we introduce a meta-population model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and applied to evaluate the outcomes of these two types of NPIs. Our approach combines the advantages of granular spatial modelling of meta-population models with the ability to realistically describe social contacts via activity-driven networks. We focus on disentangling the impact of these two different types of NPIs: those aiming at reducing individuals’ social activity, for instance through lockdowns, and those that enforce mobility restrictions. We provide a valuable framework to assess the effectiveness of different NPIs, varying with respect to their timing and severity. Results suggest that the effects of mobility restrictions largely depend on the possibility of implementing timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritized afterwards.
Highlights
Following the first report of the novel coronavirus (SARS-CoV-2) in Wuhan, China, COVID-19 has risen above 83 million cases and 1 831 703 reported deaths as of 3 January 2021 [1]
We explicitly incorporated two types of non-pharmaceutical interventions (NPIs): actions aiming at reducing individuals’ activity and policies to restrict individuals’ mobility
We calibrated the model with data on the ongoing COVID-19 outbreak in Italy [32]
Summary
Following the first report of the novel coronavirus (SARS-CoV-2) in Wuhan, China, COVID-19 has risen above 83 million cases and 1 831 703 reported deaths as of 3 January 2021 [1]. The ongoing pandemic quickly reached Europe during February and March 2020, forcing most of the countries to implement unprecedented non-pharmaceutical interventions (NPIs) to fight the spread [2–6]. Some of these interventions promote policies to reduce human-to-human interactions, for example by enforcing social distancing, halting non-essential activities, closing schools and banishing large gatherings [2,5,6]. Owing to the considerable economic and social cost associated with the implementation of both of these types of policies [8–10], it is crucial to assess their effectiveness. Mathematical and computational epidemic models are key to being able to accurately evaluate a wide range of what/if scenarios and to predict the evolution of the pandemic for different choices of NPIs [7,11–18]
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