A composite symptoms severity score based on survey self-reports as a predictor of SARS-CoV-2 infection and viral load

Background Establishing 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. Methods In 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. Results S3 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). Conclusions Our 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.