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