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

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.

Estimating data-driven COVID-19 mitigation strategies for safe university reopening

“…Since the COVID outbreak, studies have shown that encouraging people to get vaccinated and continuing nonpharmaceutical control policies are effective ways to suppress the disease spread [35][36][37]. Consistently, we conclude that it is possible to ensure a healthy campus community associated with NPIs even at a lower immune level of 30%.…”

Designing the Safe Reopening of US Towns Through High‐Resolution Agent‐Based Modeling

“…As more Since the inception of the worldwide COVID-19 pandemic in January 2020, mathematical models have emerged as powerful tools to combat its spread. [5][6][7] In the first phase of the pandemic, models have been largely adopted to conduct what-if analyses on the effect of nonpharmaceutical interventions (NPIs) for the containment of the spread, [8][9][10][11][12] also considering their socio-economic and psychological impact. [13][14][15] More recently, models are gaining traction as decision support systems to design efficient vaccination campaigns.…”

Interplay between population density and mobility in determining the spread of epidemics in cities

“…Nevertheless, an advantage of our formalism is its relative simplicity, paving the way to extend the results to more general scenarios. For example, the homogeneous assumption concerning the contacts of individuals within each subpopulation can be improved to account for the heterogeneous nature of human contacts 62,63 . In addition, we have restricted our study to the morphology of urban flows by neglecting the contribution coming from movements between different urban areas.…”