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COMMENTARY | New research finds that limiting personal mobility through travel restrictions and similar tactics is effective only in the first phases of the epidemic, and reduces in proportion to the spread of infection across a population.
After the initial spread, travel bans do little to contain the spread of SARS-CoV-2, the virus that causes COVID-19, a new study shows.
Many countries have considered travel bans key to their efforts to control the spread of the virus, but the findings suggest reducing individual activity (such as social distancing, closing non-essential business, etc.) is far superior.
The research, aimed at providing a decision support system to Italian policymakers with implications for the United States and other countries, found that limiting personal mobility through travel restrictions and similar tactics is effective only in the first phases of the epidemic, and reduces in proportion to the spread of infection across a population.
In the study, published in Journal of the Royal Society Interface, researchers detail a data modeling framework for isolating the differential efficacy of different COVID-19 intervention policies.
Since their method benefits from a low computational load (it can easily run on a personal computer), it can serve as a valuable decision support system to policymakers, toward the implementation of combined containment actions that can protect citizens’ health, while avoiding total closures, with all their economic, social, and psychological consequences.
“While this project was focused specifically on Italy, the results are revelatory for virtually any country relying on travel restrictions to stem the spread of the pandemic. We look forward to using US data to tune the model and give specific answers to combat this delicate phase of the pandemic,” says Maurizio Porfiri, professor of mechanical and aerospace, biomedical and civil and urban engineering at New York University Tandon School of Engineering.
“We are particularly satisfied with this model, as it provides very detailed answers even though it relies only on aggregated sources of data—a further guarantee of people’s privacy,” adds Alessandro Rizzo, visiting professor in the Office of Innovation at NYU Tandon.
The work includes a realistic representation of demographic data and travel patterns of both commuters and those taking long-distance trips, using only aggregated and publicly available data, without resorting to individual tracking devices.
It follows a study on the spread of COVID-19 in New Rochelle, New York predicting the diffusion of COVID-19 in medium sized cities and provinces.
The investigators also found that selective lockdown policies, for example restriction only on the activity of the elderly, seems not to have a great effect on the overall transmission of the epidemic.
Deploying their algorithmic framework to model scenarios in which restrictions are lifted, the researchers found that lifting restrictions on social activity must happen gradually to avoid a second wave, while the timing and swiftness of removal of travel restrictions seem not to have a great effect on the transmission.
In view of the scarce resources and the inherent slowness of vaccination campaigns, the research group is now using the model to assess the effect of different vaccination policies, toward the definition of vaccination rollouts that will aim at providing an optimal outcome in spite of the limited resources in terms of vaccine doses and operators.
Additional coauthors are from the University of Groningen in The Netherlands. The US National Science Foundation, Compagnia di San Paolo, MAECI, the European Research Council, and The Netherlands Organisation for Scientific Research supported the work.