Researchers at the Harvard T.H. Chan School of Public Health found that COVID-19 prevalence rates in regions of New York City were associated with work-related commuting, according to a study published earlier this month.
The study, published in the renowned peer-reviewed publication Nature Communications, was co-first authored by five researchers, including postdoctoral research fellow Stephen M. Kissler and research fellow Nishant Kishore, and co-led by Associate Professor of Epidemiology Caroline O. Buckee and Yonatan H. Grad, Assistant Professor of Immunology and Infectious Diseases.
The experiment compared COVID-19 prevalence rates in four of New York City’s boroughs — Manhattan, the Bronx, Brooklyn, and Queens — with movement patterns of roughly one million local users obtained from Facebook’s Data for Good program. The researchers selected data demonstrating travel between boroughs thought to be associated with commuting.
The researchers used COVID-19 test results administered to 1,746 pregnant women hospitalized for delivery throughout those boroughs between March 22 and May 3 as a proxy for population prevalence rates, which they calculated using a statistical model.
The study found that Manhattan, the borough with the highest reduction in commuting — 68.7 percent — also had the lowest estimated COVID-19 infection rate — 11.3 percent. On the other extreme, South Queens experienced the lowest reduction in commuting — 41.4 percent — and the highest prevalence rate of COVID-19 — 26.0 percent.
The study joined Kishore and Kissler’s different areas of expertise to offer insight into how New York City experienced the coronavirus pandemic. At Harvard, Kishore studies human mobility data while Kissler’s research focuses on mathematical modeling related to infectious diseases.
“We were able to sort of put together this story where it really looked like the variation in prevalence of COVID-19 across New York City was closely related to basically whether or not people can stay home from work,” Kissler said.
Kishore explained that the study chose pregnant women as a sample population because available data on COVID-19 hospitalization and mortality rates does not accurately reflect natural infection levels in a population.
“People that will come in are generally people that are sick because those are the people that will get tested. But that doesn’t necessarily mean that those are all the people that have COVID in a given area,” he said. “So this allowed us to universally sample people coming in for a procedure that should be generally representative of the areas from which they come.”
Kishore also said the study’s findings confirm anecdotal evidence that suggests essential workers are at higher risk of contracting the coronavirus.
“I don’t think that it’s earth shattering or incredibly new news that there are certain segments of the population that are unable to physically distance that are essential workers and go on the train or travel in some way to go to work,” he said. “It makes sense that these would also be the population that are at higher risk of being affected by COVID.”
Kissler said the study reveals there can be vast differences in the effects of the coronavirus within one region.
“In New York City, which up until recently was the epicenter of the outbreak in the United States, different neighborhoods in New York City were affected hugely differently,” he said. “It’s really difficult to generalize anything either across the country or between cities or even within cities, because different communities suffer from this outbreak so differently.”
Still, he emphasized that everyone can do their part to reduce transmission.
“It’s really important to keep in mind that this is something we’re going to be dealing with for a while longer, at the very least the next six to 12 months,” he said. “We have a pretty good sense of what we can do to help. Physical distancing and masks and these sorts of things are all very effective.”
—Staff Writer Ema R. Schumer can be reached at email@example.com. Follow her on Twitter @emaschumer.