The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false negative viral PCR test results. Such tests are also susceptible to false positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses and non-viral conditions (n=318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to predict COVID-19. Optimal classifiers achieve an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n=553). We show that a classifier relying on a single interferon-stimulated gene, such as IFI6 or IFI44, measured in RT-qPCR assays (n=144) achieves AUC values as high as 0.88. Addition of a second gene, such as GBP5, significantly improves the specificity compared to other respiratory viruses. The performance of a clinically practical 2-gene RT-qPCR classifier is robust across common SARS-CoV-2 variants, including Omicron, and is unaffected by cross-contamination, demonstrating its utility for improving accuracy of COVID-19 diagnostics.