Enhancing COVID-19 Detection Accuracy: Optimal Gene Combinations, Kit Performance, and Reliable Detection Intervals

A significant challenge and threat to public health have been created by the COVID-19 pandemic for the entire global population. The study aimed to compare the SARS-CoV-2 RNA detection capabilities of available primers and probes to identify the most reliable, efficient, and affordable method. From 200 previously detected samples of SARS CoV-2, 94 samples were selected randomly and used for the optimization of our primers and probes. We compared our results with two kits that have been approved by the health authority. In addition, we evaluated the detectability of each gene. The study compared the diagnostic performance of different gene combinations for COVID-19 detection using kits A and B and a novel approach combining RdRp, N, and E genes. Results showed that the combined approach exhibited superior discriminatory power, particularly with the inclusion of the E gene, boasting area under the curve (AUC) values of 83.3%, 79.1%, and 93.7% for the respective genes. Kit B, with Orf1ab and N genes, outperformed Kit A (RdRp and S genes), with AUC values of 81.2% and 90.6% versus 80.2% and 75%, respectively. The chart representation highlighted gene detection frequencies across various cycle threshold (Ct) ranges, demonstrating robust identification within the 20.1–30 Ct range across all kits and genes, emphasizing the reliability of detection within specific intervals. Combining RdRp, N, and E genes showed the highest accuracy for COVID-19 diagnosis, particularly with the E gene. Detection was most reliable within the 20.1–30 Ct range across all gene combinations and kits.