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New COVID-19 model reveals effectiveness of travel restrictions

Model examines global proximity in relation to disease spread

2021-06-07
(Press-News.org) TROY, N.Y. -- More strategic and coordinated travel restrictions likely could have reduced the spread of COVID-19 in the early stages of the pandemic. That's according to new research published in Communications Physics. This finding stems from new modeling conducted by a multidisciplinary team of scientists and engineers at Rensselaer Polytechnic Institute.

The researchers evaluated the distance between countries in terms of air travel, a more complex measurement than simply mapping physical distance. For instance, while China and Thailand may be geographically more proximate to one another, if there are significantly more flights between China and the United States, the chance of disease spread may be higher.

"This is considered a global problem," said Mamadou Diagne, an assistant professor of mechanical, aerospace, and nuclear engineering at Rensselaer, "so we wanted to know if coordinated action could be taken to mitigate contamination rates all across the world."

By mapping and analyzing the global mobility network through air traffic patterns, the researchers were able to determine the level of connection between various nations and develop a model that can predict which countries are closer to one another in terms of disease spread. The model was able to successfully predict when the virus arrived in the United States.

Using this approach, the team examined the effectiveness of various travel restrictions countries implemented in an effort to slow the transmission of the virus that causes COVID-19.

"For example, we found that the Chinese lockdown reduced the arrival time of the virus in uninfected countries by about 10 days," said Jianxi Gao, an assistant professor of computer science at Rensselaer. "And, it reduced the number of infections by 6 million globally."

Travel restrictions enacted by other nations, including entry bans, global travel bans, and lockdowns, also helped to reduce the global spread. However, the Rensselaer team found that these actions could have been significantly more effective had countries worked in concert with one another.

"According to the data we collected, about 63.2% percent of travel restrictions were ineffective," said Lu Zhong, a postdoctoral researcher in mechanical, aerospace, and nuclear engineering. "Because the travel restrictions were done in an uncoordinated way, they failed to contribute to the global good."

Diagne, Gao, and Zhong found that redundant or unnecessary travel restrictions also affected the global economy. A more efficient approach, they said, could mitigate economic harm.

INFORMATION:

Researchers started this work before the COVID-19 pandemic began, though they were focused on a different disease at that time. They believe the model could be applied to future pandemics in addition to alleviating some of the ongoing effects of the current one.

These findings were initially published in preprint on MedRxiv, an online repository of papers that have been screened but not peer-reviewed.

About Rensselaer Polytechnic Institute Founded in 1824, Rensselaer Polytechnic Institute is America's first technological research university. Rensselaer encompasses five schools, 32 research centers, more than 145 academic programs, and a dynamic community made up of more than 7,600 students and over 100,000 living alumni. Rensselaer faculty and alumni include more than 145 National Academy members, six members of the National Inventors Hall of Fame, six National Medal of Technology winners, five National Medal of Science winners, and a Nobel Prize winner in Physics. With nearly 200 years of experience advancing scientific and technological knowledge, Rensselaer remains focused on addressing global challenges with a spirit of ingenuity and collaboration. To learn more, please visit http://www.rpi.edu.



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[Press-News.org] New COVID-19 model reveals effectiveness of travel restrictions
Model examines global proximity in relation to disease spread