Researchers from Georgia Tech’s colleges of engineering and computer science have completed the first published study of the link between public transportation use in the United States and Covid-19 cases early in the pandemic.
Using data from the Federal Highway Administration’s National Household Travel Survey, the team looked at the nation’s 52 largest metropolitan areas and each community’s likelihood of taking buses and trains. They then compared the numbers with the 838,000 confirmed Covid cases on the Johns Hopkins Center for Systems Science and Engineering dashboard from January 22 to May 1, 2020.
The period covers the early days, weeks and months of the pandemic, before mask mandates were put in place and before widespread social distancing. Ventilation on public transport had not yet been addressed, along with other public health measures that have since become standard.
The study found that cities with heavily used public transport systems had a higher per capita incidence of Covid. This was true when other factors, such as education, poverty levels and household overcrowding, were taken into account. The association continued to be statistically significant even when the model was run without data from transit-friendly New York City.
The article, “Investigating the Association Between Public Transportation Adoption and COVID-19 Infections in United States Metropolitan Areas,” is published in the journal Total Environmental Science. Although the researchers don’t suggest that transit is the sole cause of the high incidence rates, they say it could have been an important factor early in the pandemic.
“That’s what we expected, but we wanted to run the models to be sure. Policy makers shouldn’t make decisions based on what they assume to be true,” said Michael Thomas, one co-authors of the study and holder of a Ph.D. D. student at the School of Computational Science and Engineering at Georgia Tech. “This study is similar to dusting off a dinosaur dig site and finding a leg bone. It’s not all about the dinosaur. There are many ways to make the argument about the spread of Covid, and transit is only part of it.”
The team came up with the idea to track transit and Covid cases after watching early reports from Wuhan, China, and thinking about how differences in public transport systems can be taken into account in the pandemic spread patterns. As assumptions were made about how US cities should respond based on traffic patterns on the other side of the globe, Professor John Taylor thought the pandemic shouldn’t be treated as a ‘one size fits all’ situation. “.
“In the early months of the pandemic, models were developed here at home based on incidence rates in Wuhan. But, in terms of public transportation usage behavior, China’s may be very different from what we see in American cities,” said Taylor, Frederick Law Olmsted Professor and Associate Chair for Curriculum Graduate Studies and Research Innovation at the School of Civil and Environmental Engineering. “For example, people in Chinese urban areas often stand in long lines waiting for trains and buses. We don’t. Different patterns of spread may develop due to differences in transportation behaviors. in common.”
Taylor’s main research focuses on the dynamics that can occur at the intersection of human and technical networks, such as how people change electricity consumption behaviors and mobility patterns in the event of a natural disaster. Pandemics were on his research radar before Covid became a household name, as Taylor wanted to create better models to predict the spread of disease. His first research effort in this direction was to follow the Ebola virus which reached Texas in 2014.
In the fall of 2019, Thomas was working as a biostatistician at the Georgia Department of Public Health when he spoke with Taylor about pursuing his doctorate. Thomas submitted his application to Georgia Tech in November – just four months before Covid shut down America.
The two, along with study co-author and senior research engineer Neda Mohammadi, are now creating models to predict the spread of future diseases among populations. They also seek to demonstrate how researchers can modify these models for better accuracy.
“If engineers and scientists can better understand the drivers of community spread, policy makers can make faster and more accurate decisions to protect public health,” Thomas said. “In transport, for example, this could lead to faster decisions to limit the number of people on buses. Or policies to stagger vehicle departure times more consistently. Studies like ours provide a basis for these decisions.
According to the researchers, having more accurate models also takes into account different human behaviors. Just as people in Wuhan expect public transportation differently than here in America, cities can differ from each other.
“Your pandemic is different from your neighbor’s,” Mohammadi said. “The spread of the pandemic is not the same from one city to another, nor is the traffic. Decision makers often look to other communities to see how they react to shape their actions. This n “isn’t always accurate. Models need to be customizable because populations don’t react uniformly. Our goal is to improve decision-making to be easier, faster, and more accurate for the next pandemic.” .