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

2020/06/10 by Fadoua Khmaissia, Khmaissia, Fadoua, Pegah Sagheb Haghighi +11
Mathematics · Medicine · Social Sciences · #COVID-19 epidemiological studies #Computers and Society (cs.CY) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2006.08361

openalex publication_date 2020/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

New York City has been recognized as the world's epicenter of the novel\nCoronavirus pandemic. To identify the key inherent factors that are highly\ncorrelated to the Increase Rate of COVID-19 new cases in NYC, we propose an\nunsupervised machine learning framework. Based on the assumption that ZIP code\nareas with similar demographic, socioeconomic, and mobility patterns are likely\nto experience similar outbreaks, we select the most relevant features to\nperform a clustering that can best reflect the spread, and map them down to 9\ninterpretable categories. We believe that our findings can guide policy makers\nto promptly anticipate and prevent the spread of the virus by taking the right\nmeasures.\n

Citations

• The effect of human mobility and control measures on the COVID-19 epidemic in China
• Demographic science aids in understanding the spread and fatality rates of COVID-19
• Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area
• COVID-19 and Racial/Ethnic Disparities

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