Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading

TL;DR

This paper introduces a meta-population model using temporal networks to evaluate the impact of social distancing and travel restrictions on COVID-19 spread, emphasizing the importance of timing and severity of NPIs.

Contribution

It presents a novel framework combining granular spatial modeling with activity-driven networks to assess non-pharmaceutical interventions during COVID-19.

Findings

Early implementation of mobility restrictions is crucial.

Activity reduction policies are more effective in later outbreak phases.

Model accurately predicts NPI outcomes based on timing and severity.

Abstract

To date, the only effective means to respond to the spreading of 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 reduce their social and economical consequences. Here, we introduce a meta-population model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and apt 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 provide a valuable framework to assess the viability of different NPIs, varying with respect to their timing and severity. Results suggest that the effects of mobility restrictions largely depend on the possibility to implement timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritised afterwards.