Research Fellow @ Massachusetts Institute of Technology, Resident Physician @ Mount Sinai Hospital

An Unsupervised Machine Learning Approach to Assess the ZIP Code Level Impact of COVID-19 in NYC

Fadoua Khmaissia, Pegah Sagheb Haghighi, Aarthe Jayaprakash, Zhenwei Wu, Sokratis Papadopoulos, Yuan Lai, Freddy T. Nguyen. arXiv 2020-09-18

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New York City has been recognized as the world's epicenter of the novel Coronavirus pandemic. To identify the key inherent factors that are highly correlated to the Increase Rate of COVID-19 new cases in NYC, we propose an unsupervised machine learning framework. Based on the assumption that ZIP code areas with similar demographic, socioeconomic, and mobility patterns are likely to experience similar outbreaks, we select the most relevant features to perform a clustering that can best reflect the spread, and map them down to 9 interpretable categories. We believe that our findings can guide policy makers to promptly anticipate and prevent the spread of the virus by taking the right measures.
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Physician-scientist with extensive experience developing and translating nanotechnologies and biomedical optical technologies from the bench to clinic in areas of genetics, oncology, and cardiovascular diseases. Extensive experience in community building in healthcare innovation, research, medical, and physician-scientist communities through various leadership roles.

Email: freddytn@mit.edu

Arnold O. Beckman Postdoctoral Fellow
Institute for Medical Engineering and Science

Research Fellow, MIT Innovation Initiative
Former Co-Director, MIT Hacking Medicine
Regional Director – Europe, MIT Hacking Medicine
Co-Director, MIT COVID-19 Challenge
Co-Director, MIT Hacking Racism Challenge

Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139

Email: freddy.nguyen@mountsinai.org

Resident Physician, PGY-3,
Department of Pathology, Molecular and Cell-Based Medicine

Icahn School of Medicine at Mount Sinai
Mount Sinai Hospital
One Gustave L. Levy Place, Box 1194
New York, NY 10029