Figure legend
Figure 1. Distribution of participants with positive and negative results of RAT according to sampling time post-symptoms in days (x- axis) and Ct values as determined by RT-qPCR (y -axis). Sampling time post-symptom onset was classified into early (0-7 d), middle (8-16 d) and late (>16 d). RT-qPCR categories are indicated on the right side of the graph.
Figure 2. Diagnostic performance of RAT A . Receiver operating characteristic curve (ROC) analyses showing the diagnostic performance of the RAT with an area under the curve (AUC) of 0.7B-C . Support vector machine model. B. Top ranked features based on their frequency of being selected after the cross validation. C. Plot showing the predictive accuracy of feature combination in predicting the COVID-19 positive subjects as determined by RT-qPCR. The most accurate classifier gave an accuracy of 59.3% for the top 3-feature as revealed in B . D. Predicted class probability analyses to evaluate the performance of the 3- features model. Each dot refer to average prediction of one subject after cross-validation. Dark and light colored dots indicates positive and negative cases by RT-qPCR. The misclassified subjects by the 3-feature model are labeled. The classification boundary for COVID-19 positive subjects lies at the center of x -axis (x = 0.5, vertical dotted line). Values > 0.5 indicate probability of COVID-19 positive and closer to 1 indicate high probability. Confusion matrix shows the summary of the model performance.
Figure S1. Random forest classification model showing the ranked importance of subjects’ demographic and clinical features in predicting the results of RAT and RT-qPCR assays. The features are ranked in an ascending order according to the mean decrease in accuracy (x -axis) when the respective feature was permuted.
Table S1. Demographic and clinical features of the study participants
Table S2. Diagnostic criteria for RAT for participant’s subgroups