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Publications

Journal Article Multicenter Study Observational Study Epublish

PubMed: 40642241 DOI: 10.3389/fpubh.2025.1416273

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.

Front Public Health 13:1416273

Authors: Ahmed Shaheen, Nour Shaheen, Sheikh Shoib, Fahimeh Saeed, Mudathiru Buhari, Vishal Bharmauria, Oliver Flouty

Affiliations

  • Alexandria Faculty of Medicine, Alexandria, Egypt.
  • Psychosis Research Centre, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
  • Department of Health Services, Srinagar, India.
  • Division of Infectious Disease, University of South Florida, Tampa, FL, United States.
  • The Tampa Human Neurophysiology Lab and Department of Neurosurgery, Brain and Spine, University of South Florida, Morsani College of Medicine, Tampa, FL, United States.
  • Center for Vision Research and Center for Integrative and Applied Neuroscience, York University, Toronto, ON, Canada.

Abstract

Background

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic affecting millions worldwide. This study aims to bridge the knowledge gap between acute and chronic symptoms, vaccination impact, and associated factors in patients across different low- and middle-income countries (LMICs).

Materials and methods

The study included 2,445 participants aged 18 years and older, testing positive for COVID-19. Data collection involved screening for medical histories, testing records, symptomatology, and persistent symptoms. Validated instruments, including the DePaul Symptom Questionnaire (DSQ-2) and the Patient Health Questionnaire-9 (PHQ-9), were used. We applied a self-supervised and unsupervised deep neural network to extract features from the questionnaire. Gradient boosted machines (GBM) model was used to build a risk calculator for chronic fatigue syndrome (CFS), depression, and prolonged COVID-19 symptoms.

Results

Out of the study cohort, 68.1% of the patients had symptoms lasting longer than 2 weeks. The most frequent symptoms were loss of smell (46.8%), dry cough (40.1%), loss of taste (37.8%), headaches (37.2%), and sore throat (28.9%). The patients also reported high rates of depression (47.7%), chronic fatigue (6.5%), and infection after vaccination (23.7%). Factors associated with CFS included sex, age, and smoking. Vaccinated individuals demonstrated lower odds of experiencing prolonged COVID-19 symptoms, CFS, and depression. The predictive models achieved a high area under the curve (AUC) scores of 0.87, 0.82, and 0.74, respectively.

Conclusion

The findings underscore the significant burden of long-term symptoms such as chronic fatigue and depression, affecting a considerable proportion of individuals post-infection. Moreover, the study reveals promising insights into the potential benefits of vaccination in mitigating the risk of prolonged COVID-19 symptoms, CFS, and depression. Overall, this research contributes valuable knowledge towards comprehensive management and prevention efforts amidst the ongoing global pandemic.

Clinical trial registration

Clinical trials.gov, NCT05059184.

MeSH

Depression, Patient Health Questionnaire, Research Design, Sars-Cov-2, Hospitalization, Vaccines, Income, Fatigue Syndrome, Chronic, Public Health, Vaccination, Signs And Symptoms, Covid-19, Deep Learning, Fatigue, Pandemics, Learning, Infections, Post-Acute Covid-19 Syndrome