Fig. 1

The workflow of the study: The RNA-Seq datasets related to whole blood (WB) and nasopharyngeal (NP) samples from patients with COVID-19 infection and other similar disease conditions including viral and non-viral acute respiratory illnesses (ARI) as well as healthy controls were acquired from GEO database. Data were integrated and the batch effects were eliminated. Subsequently, the datasets were subjected to pathway enrichment and GO analyses. Furthermore, the candidate diagnostic biomarker panels were identified using machine learning methods on train datasets and validated on independent cohorts to introduce the best biomarker combinations. Besides, the RF-based generic prediction models were generated by using all combinations of 3 to 9 markers related to 23 common WB/NP DEGs was done. Finally, the results of two prediction models, including the LASSO feature-based prediction model and RF-based generic prediction model were compared. WB whole blood, NP nasopharyngeal, ARI acute respiratory illnesses, RF random forest