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

Apeksha Mewani^1, , Vincent Jones II² and Alejandro Sanchez³

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

Cite this paper:
Apeksha Mewani, Vincent Jones II and 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

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.

Keywords:
COVID-19 Machine learning emergency preparedness predictive modeling seropositivity health education

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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