Comparative analysis of frequentist, Bayesian, and machine learning models for predicting SARS-CoV-2 PCR positivity

The machine learning approach (random forest) outperformed the logistic regression models in predictive accuracy. Bayesian regression confirmed the reliability of key predictors and allowed quantification of uncertainty. These findings highlight that simple, routinely collected symptom and exposure data can support rapid, resource-conscious screening for SARS-CoV-2, particularly when laboratory testing capacity is limited.

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2025-12-03
DOI
https://doi.org/10.3389/frai.2025.1668477
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
0.00
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article

Comparative analysis of frequentist, Bayesian, and machine learning models for predicting SARS-CoV-2 PCR positivity

Easter Godwin Nwokah, Francis Chukwuebuka Ihenetu, Makuochukwu Maryann Ozoude, Emeka Henry Okechukwu et al.
Frontiers in Artificial Intelligence
SARS-CoV-2 detection and testing
article

Comparative analysis of frequentist, Bayesian, and machine learning models for predicting SARS-CoV-2 PCR positivity

Easter Godwin Nwokah, Francis Chukwuebuka Ihenetu, Makuochukwu Maryann Ozoude, Emeka Henry Okechukwu, Chinyere Ihuarulam Okoro
article en

Abstract

The machine learning approach (random forest) outperformed the logistic regression models in predictive accuracy. Bayesian regression confirmed the reliability of key predictors and allowed quantification of uncertainty. These findings highlight that simple, routinely collected symptom and exposure data can support rapid, resource-conscious screening for SARS-CoV-2, particularly when laboratory testing capacity is limited.

Frontiers in Artificial IntelligenceVol. 8
Federal University of Technology Owerri (NG), Imo State University (NG), Zaporizhzhia State Medical and Pharmaceutical University (UA), Rivers State University (NG)
Openalex Percentile: Top 56%
SARS-CoV-2 detection and testing
DOIOpen Access PDFOpenAlex
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