Nematbakhsh, Shayan, Mohammadifard, Noushin, Riahi, Roya et al. · Journal of infection and public health · 2026 · DOI
Researchers used computer algorithms to identify factors associated with persistent fatigue after COVID-19 in a study of 3,850 people. They found that anxiety, depression, BMI, and memory problems were among the strongest associations with post-COVID fatigue. While this preliminary analysis suggests these factors may help identify at-risk individuals, it is important to note that the study is observational and does not establish which factors actually cause fatigue.
By analogy, this study's approach of using machine learning to identify multi-factor associations with post-COVID fatigue may be relevant to understanding ME/CFS phenotypes and risk stratification, since post-COVID fatigue and ME/CFS share symptom overlap and diagnostic uncertainty. However, relevance to ME/CFS is unclear because the study cohort is post-COVID specific, case definitions differ, and most identified predictors (e.g. recent viral recovery context) may not directly transfer to established ME/CFS populations.
This study does not establish causation—the cross-sectional design only identifies associations observed at a single time point. It does not validate the model on an independent cohort, so generalisability remains uncertain. It does not confirm that any of the 37 identified factors are mechanisms of fatigue, nor does it establish whether similar associations would be observed in ME/CFS populations or other post-infectious conditions.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Nematbakhsh, Shayan, Mohammadifard, Noushin, Riahi, Roya, Mohammadkhani, Mahdi, & Hosseini, Mohsen (2026). Prediction of post-COVID chronic fatigue syndrome using data mining and machine learning techniques in Isfahan COVID cohort study.. Journal of infection and public health. https://doi.org/10.1016/j.jiph.2026.103218
BibTeX
@article{mecfsatlas-nematbakhsh-2026-prediction-post,
author = {Nematbakhsh, Shayan and Mohammadifard, Noushin and Riahi, Roya and Mohammadkhani, Mahdi and Hosseini, Mohsen},
title = {Prediction of post-COVID chronic fatigue syndrome using data mining and machine learning techniques in Isfahan COVID cohort study.},
journal = {Journal of infection and public health},
year = {2026},
doi = {10.1016/j.jiph.2026.103218},
note = {PubMed: 42000587},
url = {https://www.mecfsatlas.com/evidence/nematbakhsh-2026-prediction-post},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-04-21. https://www.mecfsatlas.com/evidence/nematbakhsh-2026-prediction-post
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