Improved performance of nucleic acid-based assays for genetically diverse norovirus surveillance

Nucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of their genetic diversity. Here, we calculated the variability of nucleotides within the genomes of ten human viral species in silico and found that endemic viruses exhibit a high percentage of variable nucleotides (e.g., 51.4. This genetic diversity led to variable probability of detection of PCR assays (the proportion of viral sequences that contain the assay’s target sequences divided by the total number of viral sequences). We then experimentally confirmed that the probability of the target sequence detection is indicative of the number of mismatches between PCR assays and norovirus genomes. Next, we developed a degenerate PCR assay that detects 97 previously developed assays with 316.1 and 2.5 mismatches on average, respectively, which negatively impacted RNA quantification. Additionally, the two PCR assays with lower probability of detection also resulted in false negatives for wastewater-based epidemiology. Our findings suggest that the probability of detection serves as a simple metric for evaluating nucleic acid-based assays for genetically diverse virus surveillance. Importance Nucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of the rapid evolution and genetic variation of viruses. The study analyzed clinical and wastewater samples using multiple PCR assays and found significant performance variation among the PCR assays for genetically diverse norovirus surveillance. This finding suggests that some PCR assays may miss detecting certain virus strains, leading to a compromise in detection sensitivity. To address this issue, we propose a metric called the probability of detection, which can be simply calculated in silico using a code developed in this study, to evaluate nucleic acid-based assays for genetically diverse virus surveillance. This new approach can help improve the sensitivity and accuracy of virus detection, which is crucial for effective infectious disease surveillance and control.