Background: The COVID-19 pandemic's diverse symptomatology, driven by variants, underscores the critical need for a comprehensive understanding. Employing stochastic models, our study evaluates symptom sequences across SARS-CoV-2 variants on aggregated data, yielding essential insights for targeted interventions.
Methods: We conducted a meta-analysis based on research literature published before December 9, 2022, from PubMed, LitCovid, Google Scholar, and CNKI databases, to investigate the prevalence of COVID-19 symptoms during the acute phase. Registered in PROSPERO (CRD42023402568), we performed random-effects meta-analyses using the R software to estimate pooled prevalence and 95% CI. Based on our findings, we introduced the Stochastic Progression Model and Sequential Pattern Discovery using Equivalence classes (SPADE) algorithm to analyze patterns of symptom progression across different variants.
Results: Encompassing a total of 430,100 patients from east and southeast Asia, our results reveal the highest pooled estimate for cough/dry cough across wild-type, Delta, and Omicron variants, with fever (78.18%; 95% CI: 67-89%) being the most prominent symptom for the Alpha variant. Symptoms associated with the Omicron variant primarily manifested in upper respiratory tracts, cardiovascular, and neuropsychiatric systems. Stochastic models indicate early symptoms including dry cough and fever, followed by subsequent development of sleep disorders, fatigue, and more.
Conclusion: Our study underscores the evolving symptomatology across SARS-CoV-2 variants, emphasizing similarities in fever, cough, and fatigue. The Omicron variant presents a distinct profile characterized by milder symptoms yet heightened neuropsychological challenges. Advanced analytical models validate the observed sequential progression of symptoms, reinforcing the consistency of disease trajectory.
Keywords: Disease progression; SARS-CoV-2 variants; Stochastic model; Symptom trajectories.
© 2024. The Author(s).