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 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.

arXiv comment45 Pages (including 18 pages of Supplementary Information); Under review

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• Preserving system activity while controlling epidemic spreading in adaptive temporal networks
• Competitive epidemic networks with multiple survival-of-the-fittest outcomes
• Data-informed modeling of the formation, persistence, and evolution of social norms and conventions
• A Human-Vector Susceptible-Infected-Susceptible Model for Analyzing and Controlling the Spread of Vector-Borne Diseases
• A Quantum Approach for Optimal Transient Control in Network-Based Epidemic Models
• The role of inter-regional mobility in forecasting SARS-CoV-2 transmission
• Multiplex mobility network and metapopulation epidemic simulations of Italy based on Open Data
• Contagion-diffusion processes with recurrent mobility patterns of distinguishable agents
• Predicting the effects of waning vaccine immunity against COVID-19 through high-resolution agent-based modeling