ArticleBMC infectious diseases2025
A composite symptoms severity score based on survey self-reports as a predictor of SARS-CoV-2 infection and viral load.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEstablishing a strong correlation between active SARS-CoV-2 infection and COVID-19 severity could enhance early risk assessment, predict disease outcomes, and identify patients needing urgent treatment.
methodsIn this prospective SARS-CoV-2 transmission cohort study, we introduce the potential of a symptoms severity score (S3) based on patient self-reported symptoms and further evaluate its utility for predicting SARS-CoV-2 infection status and viral load. The S3 construct, derived from a participant survey using pre-defined scales (Cronbach's alpha=0.7), was categorized as asymptomatic, mild to moderate, or severe. This analysis comprised nine household contacts, contributing 1,410 qualitative and 89 quantitative visit‑test observations.
resultsS3 showed a high correlation with total symptoms (Pearson r = 0.963, p < 0.0001). The categorized version (S3C) also correlated strongly with the number of symptoms (Spearman's r = 0.988, p < 0.0001). A generalized estimating equation (GEE) model revealed that participants with severe symptoms had 6.5 times higher odds of having an active SARS-CoV-2 infection than those with no symptoms (Odds Ratio = 6.5, 95% CI: 3.5 to 12.4, p < 0.0001). Similar significant results were found for severe vs. mild to moderate symptoms (OR = 2.3, CI: 1.3 to 4.1, p = 0.0025) and mild to moderate vs. asymptomatic (OR = 2.8, 95% CI: 1.4 to 5.4, p = 0.0030).
conclusionsOur findings demonstrate that self-reported symptom severity and number of symptoms are robust predictors of SARS-CoV-2 infection and viral load, providing potential utility in clinical risk stratification. However, limitations, including a small sample size for viral load analyses and reliance on self-reported data, should be considered.
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