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New York City Department of Health and Mental Hygiene (2020) Community Health Survey—Methodology. New York, NY: Department of Health and Mental Hygiene.

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Article

Machine Learning Modeling to Predict COVID seropositivity; AI for Pandemic Preparedness

1Department of Health Equity, Administration, and Technology, Lehman College, CUNY

2Department of Health and Human Performance, York College, CUNY, USA

3School of Medicine, American University of the Caribbean, St. Marteen


American Journal of Epidemiology and Infectious Disease. 2025, Vol. 13 No. 1, 10-18
DOI: 10.12691/ajeid-13-1-2
Copyright © 2025 Science and Education Publishing

Cite this paper:
Apeksha Mewani, Vincent Jones II, Alejandro Sanchez. Machine Learning Modeling to Predict COVID seropositivity; AI for Pandemic Preparedness. American Journal of Epidemiology and Infectious Disease. 2025; 13(1):10-18. doi: 10.12691/ajeid-13-1-2.

Correspondence to: Apeksha  Mewani, Department of Health Equity, Administration, and Technology, Lehman College, CUNY. Email: abcdef@gmail.com

Abstract

This study determines the best machine learning (ML) models to predict the most accurate results in COVID-19 seropositivity using existing data. The study used the New York City Community Health Survey (NYC CHS) 2020 dataset for the analysis, and a predictive modeling approach to develop and select an optimal ML model that would accurately predict COVID-19 seropositivity from various ML algorithms. Thus, the LightGBM was found to have the highest Area Under the Curve and overall test metrics including accuracy, precision, and recall, and was therefore selected as the best-performing machine learning model.

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