@article{ajeid20251312,
author={{Mewani, Apeksha and II, Vincent Jones and Sanchez, Alejandro},
title={Machine Learning Modeling to Predict COVID seropositivity; AI for Pandemic Preparedness},
journal={American Journal of Epidemiology and Infectious Disease},
volume={13},
number={1},
pages={10--18},
year={2025},
url={https://pubs.sciepub.com/ajeid/13/1/2},
issn={2333-1275},
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.},
doi={10.12691/ajeid-13-1-2}
publisher={Science and Education Publishing}
}