Shaheen, Ahmed, Shaheen, Nour, Long COVID Collaboration Study Group in the LMICs et al. · Frontiers in public health · 2025 · DOI
Researchers studied over 2,400 COVID-19 patients in low- and middle-income countries to understand why some people develop long-lasting symptoms like chronic fatigue and depression after infection. They used advanced computer analysis to identify which patients were at highest risk for these prolonged symptoms. The study found that vaccination reduced the risk of developing these long-term problems, and that older age, being female, and smoking were linked to higher risk of chronic fatigue.
This study addresses a critical gap in understanding long COVID and CFS in underrepresented populations (LMICs), where research is scarce. The development of risk calculators could help clinicians identify vulnerable patients early for targeted intervention, and the confirmation of vaccination's protective effect provides important epidemiological evidence for post-pandemic public health strategies.
The cross-sectional design cannot prove that vaccination prevents long COVID/CFS development—it only shows an association. The study cannot determine whether protective factors cause reduced risk or whether reverse causality or selection bias explains the associations. Additionally, the low CFS prevalence may reflect misclassification rather than true disease burden in these populations.
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
Shaheen, Ahmed, Shaheen, Nour, Long COVID Collaboration Study Group in the LMICs, Shoib, Sheikh, Saeed, Fahimeh, Buhari, Mudathiru, et al. (2025). Deep learning analysis of long COVID and vaccine impact in low- and middle-income countries (LMICs): development of a risk calculator in a multicentric study.. Frontiers in public health. https://doi.org/10.3389/fpubh.2025.1416273
BibTeX
@article{mecfsatlas-shaheen-2025-deep-learning,
author = {Shaheen, Ahmed and Shaheen, Nour and Long COVID Collaboration Study Group in the LMICs and Shoib, Sheikh and Saeed, Fahimeh and Buhari, Mudathiru and Bharmauria, Vishal and Flouty, Oliver},
title = {Deep learning analysis of long COVID and vaccine impact in low- and middle-income countries (LMICs): development of a risk calculator in a multicentric study.},
journal = {Frontiers in public health},
year = {2025},
doi = {10.3389/fpubh.2025.1416273},
note = {PubMed: 40642241},
url = {https://www.mecfsatlas.com/evidence/shaheen-2025-deep-learning},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-26. https://www.mecfsatlas.com/evidence/shaheen-2025-deep-learning
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