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

TitleAn Unsupervised Machine Learning Approach to Assess the ZIP Code Level Impact of COVID-19 in NYC
Publication TypeJournal Article
Year of Publication2020
AuthorsKhmaissia, Fadoua, Haghighi Pegah Sagheb, Jayaprakash Aarthe, Wu Zhenwei, Papadopoulos Sokratis, Lai Yuan, and Nguyen Freddy T.
Date Published2020
Other NumbersarXiv:2006.08361 [cs.CY]
Abstract

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.

URLhttps://arxiv.org/abs/2006.08361
Refereed DesignationNon-Refereed