Julian Ruggiero, a Fulbright scholar in the Data Analytics and Visualization program, recently conducted an analysis of the occurrence of COVID-19 in the boroughs of New York City and found that people residing in zip codes predominantly populated by low socioeconomic groups, including African Americans and Hispanics, had a higher incidence of the virus, reflecting a significant relationship between infection rates and economic status.
He used many variables in his research, including mean income, median income, population and population density, to predict the positive number of cases per thousand people in each zip code. His analysis showed that in some zip codes in Queens, up to 45 people per thousand tested positive whereas only 5 per thousand tested positive in Manhattan.
“The association between positive tests and zip codes is almost certainly driven by economic factors and suggests that COVID is not, in fact, a great equalizer in the New York City area,” said Julian’s advisor, Sergey Fogelson, who teaches the course Computational Math and Statistics.
Julian used linear regression models on publicly available datasets—New York City Department of Health and Mental Hygiene, U.S. zip code databases and U.S. Census Bureau reports—and combined the data to make inferences about what demographic factors had an impact on the incidence of COVID-19 cases in New York City as of May 23.
Julian, who hails from Cordoba, Argentina, graduated from the National Technological University of Cordoba with a systems analyst degree. In 2018, he obtained an Argentine Presidential Fellowship in Science and Technology, sponsored by the Fulbright Commission, to pursue a master’s degree in the United States.
“I chose the Katz School because of the project-oriented courses and its great faculty,” he said. “The more hands-on experience I get, the more prepared I will be for a future data analyst job using a project portfolio, so Katz was the perfect choice.”
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