Italy: Predicting the spread of SARS-CoV-2

Research shows the results of a new predictive model which includes key features of human behaviour and mobility for tracking the spread of COVID-19 at the province level.

The new model helps also the optimisation of vaccination plans

Research shows the results of a new predictive model which includes key features of human behaviour and mobility for tracking the spread of COVID-19 at the province level.

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The interest of the international scientific community in this research is high. The project is being carried out by an Italian-American team composed of the Dynamical Systems Laboratory, Tandon School of Engineering (New York University) directed by Professor Maurizio Porfiri,  Professor Alessandro Rizzo of the Department of Electronics and Telecommunications (Politecnico di Torino), doctoral student Francesco Parino (Politecnico di Torino) and Lorenzo Zino, post-doctoral researcher (University of Groningen, Netherlands).

The modelling study first looked at the spread of COVID-19 in New Rochelle (NY, USA), and reached the cover of a forthcoming issue of the journal Advanced Modelling and Simulations (Wiley). Now, the study on the spread of COVID-19 was applied to the Italian territory and has been published by the Royal Society Journal’s Interface, in the paper titled "Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading".

The new paper is focused on Italy, authored by Francesco Parino, Lorenzo Zino, Maurizio Porfiri and Alessandro Rizzo. It develops a new model to represent and predict the spread of COVID-19 across entire provinces, and includes salient features of human behaviour, together with a realistic representation of population demography and travel (such as commuting with longer distance and longer duration flows). Although the model is very accurate, it relies only on the study of easily available aggregated data at the province level, without relying on specific individual activity tracking devices to ensure privacy and data protection.

The model proved to be very effective in assessing the effects of different policy interventions concerning limits on specific activities and travel restrictions, and could be an adequate tool to guide policy makers in implementing strategies that safeguard health without stopping human activities altogether.

The scientific community agrees on the effectiveness of social distancing and travel restrictions, but local authorities are always looking for effective combinations of restrictions at different levels to avoid further total lockdowns and the social, economic, and psychological implications that follow.

The authors suggest that, while restrictions on social and economic activity, such as closing offices, schools, curfews, and restricting access to public places always have a significant effect on reducing the spread of the epidemic, restrictions on travel only have a significant effect if applied in the early stages of the outbreak, i.e. when there are low numbers of cases. If applied late, travel restrictions become ineffective and, in some cases, may be counterproductive. Finally, selective lockdown policies on certain strata of the population - such as the isolation of the elderly - do not seem to be very effective in stopping the spread of the epidemic.

Lastly, the model was used to carry out a study on re-openings: in this case, the gradualism for re-establishing social activities is fundamental to avert successive waves, while the timing of travel reactivation is not critical. According to the study, travel resumption can be carried out in a clear-cut manner.

In light of recent problems with vaccine supplies, the working group's current efforts are focused on evaluating different vaccination strategies, with the aim of providing valuable support in the design of efficient and effective vaccination rollouts.

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