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. 2021 Jul 20;12:708184. doi: 10.3389/fimmu.2021.708184

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Copyright © 2021 Shi, Weng, Wu, Dai, Luo, Chen, Zhu, Lu, Cheng, Chen, Liu, Wu, Wu, Jin, Guo, Chen, Wu, Yao and Zheng

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Random Forest efficacy with various indicators. We constructed a random forest model to predict the Nab titers (divided by 1:20) from the tested immunoglobulins. ROC curve of IgG (A), IgM (B), and IgA (C) as a single indicator. (D) ROC curve of the indicator combinations. (E) Performance of RBD/S2-IgG/IgM in the random forest plot, every dot represents a sample, red, green, purple, and blue represent true positive, true negative, false positive, false negative respectively. AUC, area under curve; Ig, immunoglobulin; N, nucleocapsid protein; Nab, neutralizing antibodies; RBD, receptor-binding domain; ROC, receiver operating characteristic; S, spike.