Investigation of SARS-CoV-2 Variants at Primer Binding Sites in Diagnostic Platforms and the Effect on Laboratory Diagnostic Samples

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The most accurate way to identify SARS-CoV-2 infection is nucleic acid amplification tests (NAATs), especially real-time PCR (RT-PCR) assays. However, changes in SARS-CoV-2 primer and probe binding sites might compromise the accuracy of these diagnostic tests and increase false-negative rates. Real-time PCR serves as the gold standard for SARS-CoV-2 detection but shows 2–29% false-negative rates. The present study analyzed ~ 26,000 SARS-CoV-2 genomic sequences from the Global Initiative on Sharing All Influenza Data (GISAID) database to shed light on genetic variants that affected the performance of ongoing setup RT-PCR primer and probe set. This study assesses 12 primer sets for detecting SARS-CoV-2 variants from late 2019 to early 2023 across four frameworks: chronological, geographical, variant-wise, and diagnostic metrics. We validated computational predictions using clinical specimens and Sanger sequencing. Our findings indicate a correlation between amplification failures and single-point mutations or other genetic alterations in the primer and probe binding sites, leading to false-negative results in RT-PCR testing. Our findings provide crucial data for RT-PCR assay design and enhancement. Specifically, our analysis provided quantitative mismatch rates (0.15–77.15%), identified critical binding site mutations causing RT-PCR failures, and established temporal performance patterns tracking variant-driven primer degradation. These results enable evidence-based primer selection and highlight the need for continuous surveillance in viral pandemics. These findings recommend implementing multiplex RT-PCR assays and continuous primer surveillance for reliable COVID-19 diagnosis. Biological sciences/Biological techniques Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Genetics Health sciences/Medical research Biological sciences/Microbiology Biological sciences/Molecular biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The COVID-19 epidemic had major effects on public health, economies, societies and became a global concern. Believed to have originated from an animal source, COVID-19—caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)—quickly spread among people through respiratory droplets and contact. SARS-CoV-2 primarily affects the respiratory tract, causing pneumonia, but it also impacts other physiological systems, including the gastrointestinal (GI), neurological, and cardiovascular systems. Furthermore, COVID-19 could show unusual symptoms including skin manifestations and sensory abnormalities including taste (ageusia) and anosmia, or loss of smell. While some patients suffer nasal congestion and rhinitis, the most often occurring symptoms are fever, coughing, and tiredness. By July 1st, 2023 SARS-CoV-2 was responsible for about 7 million deaths worldwide and over 767 million confirmed cases. SARS-CoV-2's high transmissibility among humans via respiratory droplets makes fast and accurate detection of it absolutely vital. Early virus identification can be quite helpful in preventing its spread by immediate medical intervention and immediate isolation for afflicted people. Healthcare professionals should be aware of the atypical symptoms and signs of COVID-19 in those without usual respiratory symptoms, since they assist in identifying and diagnosing the condition. In RT-PCR tests for SARS-CoV-2 infection, a diagnostic failure can have major effects on health program that are meant to control and stop community transmission. Controlling the spread of the virus depends on rapidly identifying and isolating infected people. To properly control the COVID-19 epidemic, dependable and accurate diagnostic tests for SARS-CoV-2 must thus be widely available and easily accessible. The average mutation frequency in the structural proteins of SARS-CoV-2 was calculated to be 0.027% for the S protein, 0.045% for the E protein; for the M protein is 0.53%; and for the N protein reported 0.088%[ 1 ]. High-frequency mutations in primer-targeted regions include D614G and N501Y in spike gene (> 70% prevalence)[ 2 ],and R203K, G204R in nucleocapsid gene (15–25% prevalence in Middle Eastern populations)[ 3 ] ,directly impacting diagnostic primer binding efficiency and highlighting the need for systematic primer surveillance. The most often used molecular assay for SARS-CoV-2 detection is Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR)[ 4 ]. Due to its high sensitivity and specificity, RT-PCR has become most reliable approach for SARS-CoV-2 identification. False-negative results can arise, from mismatches between primers, probes and the target sequences resulting from genetic variants and mutations in the viral genome. Thus, it is crucial to regularly evaluate the performance of RT-PCR diagnostic tests and track the development of new SARS-CoV-2 variants[ 5 ], [ 6 ]. By considering the high mutation rate in three key regions of E and N gene, and RdRp region in ORF1ab, significant changes have been observed compared to the wild type SARS-CoV-2 Detection techniques depending on a single target inside the viral genome find great difficulty in the presence of these mutations[ 7 ][ 8 ]. Previous studies have identified primer mismatch challenges in SARS-CoV-2 diagnostics. Khan and Cheung (2020) analyzed 17,026 sequences and found mismatches in seven RT-PCR assays, while Artesi et al. (2020) documented specific E gene mutations causing diagnostic failures. However, these studies were limited to early pandemic data and lacked comprehensive temporal analysis across multiple variants[ 9 ], [ 10 ]. Extensive data reveal that a single-point mutation in the primer-probe binding sites of SARS-CoV-2 genes can significantly hinder the amplification process, and in following affecting the RT-PCR results. One of the main concerns is the possibility of false-negative assays, which could result from viral variations that are undetectable with conventional diagnostic tests. This could have significant implications for the overall management of the COVID-19 epidemic[ 11 ]. Materials and Methods Bioinformatics Analysis Data Acquisition from GISAID: SARS-CoV-2 genomic sequences were retrieved from the GISAID[12] database (https://www.gisaid.org), which offers extensive access to genetic sequences and their corresponding metadata, including geographic location, collection date of the sample, viral variant, patient characteristics, and more. (Access to the data are provided through the University of Tehran.) Specific time intervals were selected based on epidemiological data from the World Health Organization COVID-19 Dashboard (https://covid19.who.int). They matched the peaks of COVID-19 cases and deaths globally. Sequence Processing, Primer Design, and Alignment with SnapGene: The FASTA sequences acquired were first processed in SnapGene software by GSL Biotech (https://www.snapgene.com). SnapGene facilitated the annotation of genetic features, primer design, and alignment processes crucial for this study. Primers to target specific areas of the SARS-CoV-2 genome were created in SnapGene and therefore enabled in silico simulation of PCR amplification and primer specificity analysis. Multiple Sequence Alignment with MEGA X: Multiple sequence alignment analysis was accomplished by using of MEGAX software. The software provides sophisticated algorithms for aligning large datasets of nucleotides and hence allowed large-scale comparison of genetic variations among various SARS-CoV-2 sequences acquired from GISAID. Primer and Probe Analysis Using Python: In this study, we developed a Python-based pipeline to evaluate primer–genome alignments across SARS-CoV-2 sequences. The framework integrates NumPy and Pandas for matrix-based statistics and data export, while leveraging custom modules (ReadingFasta and LoadPrimer) to manage genome sequences and primer definitions. The analysis focused on the possibility of primer and probe annealing within a 100 bp region upstream and downstream of the target sites in commercial RT-PCR kits. Table 1: Assessment Criteria for SARS-CoV-2 Primer Sets Objective Acceptance Criteria Details Primer Matching First 5 nucleotides at 3' end must match perfectly; ≤3 mismatches in the remaining sequence Ensures specific and efficient primer binding to the viral sequence Probe Matching <5 mismatches between probe and viral sequence Allows for stable hybridization of the probe Multiple Primer Sets Hybridization to at least one primer in the set Applicable when multiple primers target the same genomic region Melting Temperature Calculation Tm difference ≤5°C from the original Tm Tm calculated using Biopython's nearest-neighbor method; ensures appropriate annealing conditions Melting Temperature calculated based on the nearest-neighbor thermodynamic theory by the Biopython library[13], [14]. The integration of genetic and epidemiological data was done by matching every sequence to its corresponding metadata retrieved from GISAID. Python scripts used in this study are also available publicly on GitHub: https://github.com/Petotem/SARS_CoV_2_Mutations.git Data Management with Microsoft Excel: In order to efficiently sort, filter, and perform preliminary statistical analyses, the combined epidemiological and genetic datasets were organized and managed using Microsoft Excel. Experimental Analysis Sampling: Out of 50,000 SARS-CoV-2 samples tested in the clinical diagnostic laboratory, three samples were identified as positive results for the Spike gene and negative for the Nucleocapsid gene via the use of RT-PCR. To validate our workflow and the reliability of our database, we specifically examined these three false-negative sequences, designated S01, S12, and S18, to investigate the underlying cause of this discrepancy. RNA Extraction of SARS-CoV-2: Total RNA was extracted from nasopharyngeal swab samples using the QIAamp Viral RNA Mini Kit (Qiagen, Germany) following the manufacturer's protocol. Purified RNA was quantitated in a NanoDrop One spectrophotometer (Thermo Fisher Scientific, USA) for concentration and purity [13]. Reverse Transcription and PCR Amplification: Complementary DNA (cDNA) synthesis was carried out by the SuperScript IV First-Strand Synthesis System (Thermo Fisher Scientific, USA) with random hexamer primers. Target region amplification was carried out by Platinum II Hot-Start PCR Master Mix (Thermo Fisher Scientific, USA) with target-specific primers designed using SnapGene to target regions of interest in the SARS-CoV-2 genome. Three forward primers were designed: F1 (sequence: TGACCCGTGTCCTATTCACT), F2 (sequence: CTACTACCTAGGAACTGGGC), and F3 (sequence: TCGTGGTGGTGACGGTAA), along with two reverse primers: R2 (sequence: CTGCGTAGAAGCCTTTTGGC) and R3 (sequence: TCTGCGGTAAGGCTTGAGT). The binding sites of these primers on the Wuhan reference strain of SARS-CoV-2 are illustrated in Figure 2. They were named systematically for the sake of clarity and were used in the RT-PCR reactions to specifically amplify the nucleocapsid region, thereby forming the basis of the molecular studies presented in this work. Figure 2. Genomic locations of the designed primers mapped to the Wuhan reference genome of SARS-CoV-2. Sanger Sequencing Method: The PCR products were purified by ExoSAP-IT PCR Product Cleanup Reagent (Applied Biosystems, USA) and sequencing reactions were subcontracted and performed by Genesaze Company (Tehran, Iran) on the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, USA) following the manufacturer's instructions. Capillary electrophoresis was carried out on an ABI 3500 Genetic Analyzer (Applied Biosystems, USA). Sequencing data were collected and analyzed using Sequencing Analysis Software v6.0 (Applied Biosystems, USA) and aligned to the reference genome using MEGA X and SanpGene. Ethics approval and consent to participate: Archived nasopharyngeal swab samples used in this study were obtained from the Medical Virology Laboratory in Tehran, Iran, where they were originally collected from patients presenting for routine SARS-CoV-2 diagnostic testing and subsequently de-identified before being provided to the researchers. All methods were carried out in accordance with national ethical guidelines of the Islamic Republic of Iran. The study protocol was reviewed and approved by the Ethics Committee of the University of Tehran under approval number 160583 in December 2021. Owing to the retrospective use of anonymized residual clinical specimens, the requirement for written informed consent was waived by the Ethics Committee. Results The current research involved the evaluation of 25,658 SARS-CoV-2 genomic sequences obtained from the GISAID database. 12 various sets of primers (Table 2) for SARS-CoV-2 identification have been collected and tested[21], [22] . With a large sample size and various primer sets, provide comprehensive insights of the sensitivity and specificity concerning the diagnosis tests. Table 2: Primer Sets and Sequences Provider Target Site Primer Name Code Primer Sequence China CDC [23] nsp10 CCDC-ORF1-Fwd China CDC nsp10 f CCCTGTGGGTTTTACACTTAA CCDC-ORF1-Rev China CDC nsp10 r ACGATTGTGCATCAGCTGA CCDC-ORF1-Probe China CDC nsp10 p CCGTCTGCGGTATGTGGAAAGGTTATGG China CDC[23] Nucleocapsid CCDC-N-Fwd China CDC N f GGGGAACTTCTCCTGCTAGAAT CCDC-N-Rev China CDC N r CAGACATTTTGCTCTCAAGCTG CCDC-N-Probe China CDC N p TTGCTGCTGCTTGACAGATT Hong Kong [11], [24] nsp14 HKU-ORF1-Fwd Hong Kong N f TGGGGYTTTACRGGTAACCT HKU-ORF1-Rev Hong Kong N r AACRCGCTTAACAAAGCACTC HKU-ORF1-Probe Hong Kong N p TAGTTGTGATGCWATCATGACTAG Hong Kong202[11], [24] Nucleocapsid HKU-N-Fwd Hong Kong N f TAATCAGACAAGGAACTGATTA HKU-N-Rev Hong Kong N r CGAAGGTGTGACTTCCATG HKU-N-Probe Hong Kong N p GCAAATTGTGCAATTTGCGG Berlin [25] RdRp Berlin_RdRp_SARSr-F Berlin RdRp f GTGARATGGTCATGTGTGGCGG Berlin_RdRp_SARSr-R Berlin RdRp r CARATGTTAAASACACTATTAGCATA Berlin_RdRp_SARSr-P1 Berlin_RdRp_SARSr-P2 Berlin RdRp p1 Berlin RdRp p2 CAGGTGGAACCTCATCAGGAGATGC CCAGGTGGWACRTCATCMGGTGATGC Berlin[25] Envelope Berlin_E_Sarbeco_F Berlin E f ACAGGTACGTTAATAGTTAATAGCGT Berlin_E_Sarbeco_R Berlin E r ATATTGCAGCAGTACGCACACA Berlin_E_Sarbeco_P1 Berlin E p1 ACACTAGCCATCCTTACTGCGCTTCG US CDC [5], [26] Nucleocapsid 1 CDC_2019-nCoV_N1-F US CDC N1 f GACCCCAAAATCAGCGAAAT CDC_2019-nCoV_N1-R US CDC N1 r TCTGGTTACTGCCAGTTGAATCTG CDC_2019-nCoV_N1-P US CDC N1 p ACCCCGCATTACGTTTGGTGGACC US CDC [5], [26] Nucleocapsid 2 CDC_2019-nCoV_N2-F US CDC N2 f TTACAAACATTGGCCGCAAA CDC_2019-nCoV_N2-R US CDC N2 r GCGCGACATTCCGAAGAA CDC_2019-nCoV_N2-P US CDC N2 p ACAATTTGCCCCCAGCGCTTCAG US CDC[5], [26] Nucleocapsid 3 CDC_2019-nCoV_N3-F US CDC N3 f GGGAGCCTTGAATACACCAAAA CDC_2019-nCoV_N3-R US CDC N3 r TGTAGCACGATTGCAGCATTG CDC_2019-nCoV_N3-P US CDC N3 p AYCACATTGGCACCCGCAATCCTG Japan NIID[7] Nucleocapsid NIID_2019-nCOV_N_F2 Japan NIID N f AAATTTTGGGGACCAGGAAC NIID_2019-nCOV_N_R2 Japan NIID N r TGGCAGCTGTGTAGGTCAAC NIID_2019-nCOV_N_P2 Japan NIID N p ATGTCGCGCATTGGCATGGA Thailand NIH[4], [27] Nucleocapsid WH-NIC N-F Thiland NIH N f CGTTTGGTGGACCCTCAGAT WH-NIC N-R Thiland NIH N r CCCCACTGCGTTCTCCATT WH-NIC N-P Thiland NIH N p CAACTGGCAGTAACCA Charité[4] RdRp/Orf1 RdRp_SARSr-F Chartie RdRp f GTGARATGGTCATGTGTGGCGG RdRp_SARSr-R Chartie RdRp r CARATGTTAAASACACTATTAGCATA RdRp_SARSr-P2 Chartie RdRp P2 CAGGTGGAACCTCATCAGGAGATGC Figure 3: Illustrates the temporal trend in the performance of different primer sets from 2019 to 2023. The China CDC-B primer initially showed high performance (above 96%) up to early 2020, but then exhibited a dramatic decline in effectiveness, dropping close to zero in the following years. This sharp decrease reflects genetic changes in the virus at the primer’s target regions, rendering it almost useless for accurate detection. In contrast, high-performing primers like Japan NIID-P, US CDC-H, Hong Kong-D, and China CDC-A maintained stable and high performance (consistently above 98%) throughout the entire period. In order to conduct a thorough analysis of the diagnostic potential of these primer sets, the results were separated into four distinct categories with the following characteristics: 1. Chronological: Comparison of primer performance over various time periods to determine time-related differences in diagnostic accuracy. 2. Geographical classification: Analysis of primer efficiency in different geographical areas to identify regional disparities. 3. Variant-wise sorting: Analysis based on Variants Of Concern (VOC) and Variants Of Interest (VOI) as defined by the World Health Organization (WHO). 4. The total count of positive and negative samples each primer set detects: Comprehensive evaluation of diagnostic performance to establish reliability. Figure 4: Representation of the genomic positions of the 12 investigated primer sets on the Wuhan reference sequence of SARS-CoV-2. This figure illustrates the specific binding sites targeted by each primer set across the viral genome, providing a comprehensive overview of their distribution on the reference strain Chronological Analysis Chronological analysis revealed gradual increase in primer mismatches over time with considerable expansion particularly since 2021, demonstrating the impact of viral evolution on diagnostics[28]. One possible explanation for this phenomenon is that novel strains of SARS-CoV-2 that include primer-binding site mutation have emerged and propagated quickly. Table 3 presents the total count of the samples gathered every quarter (Q1, Q2, Q3, Q4) over the years 2019 to 2023, while Table 4 provides the percentage of discrepancies in each primer pair. Table 3: Total number of samples analyzed in each quarter from Q4 2019 to Q1 2023 2019.Q4 2020.Q1 2020.Q3 2020.Q4 2021.Q1 2021.Q2 2021.Q3 2022.Q1 2022.Q2 2022.Q3 2022.Q4 2023.Q1 Numer of Samples 22 1919 2787 1538 2840 1674 3730 3036 1731 1854 3223 1304 Table 4: Percentage of mismatches per Primer Set each quarter from Q4 2019 to Q1 2023 China CDC China CDC Hong Kong Hong Kong Berlin Berlin US CDC US CDC US CDC Japan NIID Thailand NIH Charité A B C D E F G H I P Q R 2019.Q4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2020.Q1 0.10 3.65 0.00 0.57 0.00 0.05 0.10 0.00 0.00 0.05 0.05 0.00 2020.Q3 0.11 61.75 0.04 1.11 0.18 0.32 0.18 0.25 0.93 0.36 0.54 0.18 2020.Q4 0.00 34.33 0.20 0.59 0.00 0.13 0.98 1.11 0.46 0.20 1.43 0.00 2021.Q1 0.04 38.42 0.04 0.32 0.00 0.11 0.18 0.11 1.58 0.60 0.56 0.00 2021.Q2 0.00 92.23 0.06 0.06 0.06 0.18 0.06 0.36 0.48 0.12 0.30 0.06 2021.Q3 0.21 99.68 0.16 0.48 0.08 0.08 0.27 0.16 0.24 0.00 0.08 0.08 2022.Q1 1.38 99.34 0.07 0.59 0.13 43.08 0.86 0.46 0.59 0.07 41.63 0.07 2022.Q2 0.00 100.00 0.00 0.35 0.06 99.77 0.69 0.17 0.46 0.06 99.88 0.06 2022.Q3 0.11 99.95 0.32 0.16 0.22 99.35 0.97 0.11 5.23 0.05 85.98 0.22 2022.Q4 0.34 99.94 0.16 0.16 0.68 98.60 0.47 0.09 15.85 0.12 90.23 0.65 2023.Q1 0.15 99.85 0.23 0.00 0.08 98.31 0.08 0.31 25.92 0.00 89.65 0.08 Geographical Analysis Geographical analysis indicated geographical variation in primer mismatches, with high levels of mismatches observed in areas where specific variants of SARS-CoV-2 were largely prevalent. This variation could be the result of differences in the evolution of the virus, differences in populations, or even sampling biases. Table 5 indicates the number of samples collected from each nation, and Table 6 indicates the percentage of mismatches for each primer set in different nations, where only countries with more than 500 sample data are shown and the complete dataset is available in the attachment (Supplementary Data, Table ST1) Table 5: Total Number of Samples per Country Country Total Sample Count(n) Country Total Sample Count Country Total Sample Count(n) Japan 949 Chile 24 Pakistan 8 Hong Kong 170 Panama 25 Cabo Verde 1 Vietnam 13 Iran 59 Colombia 4 United Kingdom 6770 Niger 1 Philippines 13 Thailand 76 Argentina 25 Greece 94 China 785 Morocco 9 Poland 276 Singapore 171 Saudi Arabia 28 Azerbaijan 2 Italy 419 Nigeria 1 Portugal 54 South Korea 435 Canary Islands 2 Myanmar 3 Malaysia 107 Saint Barthelemy 1 Botswana 4 USA 4001 Russia 256 Czech Republic 23 Denmark 2656 Mongolia 7 Slovakia 26 Taiwan 18 Ireland 127 Estonia 9 Mexico 102 South Africa 67 Mauritius 35 Australia 1027 Ghana 6 Brunei 28 Belgium 326 Bangladesh 36 Qatar 30 Spain 659 Latvia 2 Maldives 6 Norway 173 Ukraine 1 Croatia 27 Canada 329 Costa Rica 30 Reunion 43 Austria 306 Iceland 58 Bulgaria 12 Germany 942 Serbia 6 Jordan 4 Switzerland 362 Romania 47 Kazakhstan 2 Netherlands 216 Peru 22 U.S. Virgin Islands 1 Finland 41 Nicaragua 2 Ecuador 2 France 297 Slovenia 483 Guatemala 2 United Arab Emirates 18 Puerto Rico 7 Montenegro 2 Sweden 107 Papua New Guinea 4 El Salvador 1 India 771 Kenya 15 Paraguay 1 Cambodia 21 Indonesia 52 Liechtenstein 4 Senegal 1 Lithuania 11 Guam 5 Georgia 9 Equatorial Guinea 1 Hungary 2 Lebanon 32 Luxembourg 88 Algeria 1 New Zealand 355 Gabon 2 Mozambique 5 Oman 2 Northern Mariana Islands 4 Togo 3 Brazil 233 Egypt 14 Sri Lanka 1 Nepal 28 Turkey 414 Aruba 1 Israel 50 Guinea 2 Eswatini 1 Zambia 3 Burkina Faso 3 Table 6: Percentage of Mismatches per Primer Set by Country(Over 500 Sample Count) China CDC% China CDC% Hong Kong% Hong Kong% Berlin% Berlin% US CDC% US CDC% US CDC% Japan NIID% Thailand NIH% Charité% A B C D E F G H I P Q R Australia 0 96.4 0 0.19 0 35.15 0 0.1 0.39 0.1 34.96 0 China 0 5.48 0 0 0 4.59 0.13 0 1.02 0 2.04 0 Denmark 0.94 59.3 0 0.38 0 44.65 0.41 0.11 3.61 0 45.48 0 Germany 0 92.68 0 0.21 0 64.01 0.85 0.11 2.87 0.21 63.69 0 India 0 82.88 0.78 0 0.52 69.91 0.52 0.52 1.3 0 62 0.52 Japan 0.84 83.35 0 0 0 13.8 0.53 0 0.53 0.11 13.7 0 Spain 0 92.26 0 0 0 84.52 1.21 0.3 27.16 0.15 13.66 0 United Kingdom 0.3 90.28 0.13 0.31 0.4 16.35 0.16 0.31 4.36 0.06 16.62 0.38 USA 0.3 66.53 0.05 0.2 0.12 44.24 0.65 0.12 5.3 0.67 43.66 0.07 Variant-wise Analysis A comparison of the performance of primer pairs with SARS-CoV-2 VOC and VOI, as defined by the WHO, was also carried out using a variant-by-variant analysis. The presence of mutations in primer binding sites was found to be more prevalent in specific variations, which resulted in increased mismatch rates in particular primer sets. The total number of samples for each variant is presented in Table 7, and the percentage of mismatches for each primer pair broken down by variant is presented in Table 8. Table 7: Total Number of Samples per Variant Variants Alpha Beta Delta Epsilon Eta Gamma Kappa Lota Mu Omicron Other Zeta Number Of samples (N) 2492 39 603 43 1 49 5 58 1 5134 17206 27 Table 8: Percentage of Mismatches per Primer Set by Variant China CDC China CDC Hong Kong Hong Kong Berlin Berlin US CDC US CDC US CDC Japan NIID Thailand NIH Charité A B C D E F G H I P Q R Alpha 0.00 99.24 0.12 0.12 0.04 0.28 0.00 0.24 0.48 0.04 0.28 0.04 Beta 0.00 2.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Delta 0.00 98.67 0.00 0.17 0.00 0.33 0.00 0.50 0.00 0.00 0.00 0.00 Epsilon 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Eta 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Gamma 0.00 100.00 0.00 0.00 0.00 14.29 0.00 0.00 2.04 0.00 14.29 0.00 Kappa 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20.00 0.00 0.00 Lota 0.00 0.00 0.00 0.00 0.00 0.00 1.72 0.00 0.00 0.00 0.00 0.00 Mu 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Omicron 0.47 99.96 0.16 0.18 0.33 99.59 0.64 0.14 0.64 0.06 92.36 0.31 Other 0.27 66.91 0.10 0.57 0.13 24.57 0.44 0.28 5.93 0.21 23.07 0.12 Zeta 0.00 100.00 0.00 0.00 0.00 3.70 3.70 3.70 0.00 0.00 3.70 0.00 Overall Performance The overall analysis of mismatches highlighted that some primer sets, particularly the China CDC Nucleocapsid gene primers (B), were less reliable due to high mutation rates in their target regions. This high rate of mismatch, as noted especially in variants like Omicron (99.96%) and Alpha (99.24%), suggests that such primers will not be able to detect a significant percentage of samples, thus promoting false-negative results in diagnostic tests. On the other hand, Primers pairs China CDC (A), Hong Kong (C), United States CDC (H), Japan NIID (P), and Charité (R) were extremely dependable, with match rates that exceeded 99% throughout the entire sample of 25,658 sequences. There is a presentation of the overall accuracy per primer set in Table 9, which includes both the match and mismatch percentages. Table 10: Total Count of Matches and Mismatches per Primer Set China CDC China CDC Hong Kong Hong Kong Berlin Berlin US CDC US CDC US CDC Japan NIID Thailand NIH Charité A B C D E F G H I P Q R Match 99.72 22.85 99.89 99.57 99.84 63.53 99.57 99.75 95.84 99.84 65.99 99.85 MisMatch 0.28 77.15 0.11 0.43 0.16 36.47 0.43 0.25 4.16 0.16 34.01 0.15 The resulting sequences were uploaded to the NCBI database under NCBI GenBank under the name of S01, S12, S18 with accessions OQ152808.1[15], OQ152122.1[16], and OP999655.1[17], respectively. and to GISAID under references EPI_ISL_16093679[18], EPI_ISL_16066187[19] and EPI_ISL_16066107[20]. All sequences are publicly accessible and viewable through these respective repositories. Our testing on these samples revealed a discordant result: amplification was successful for the Spike gene target, but the Nucleocapsid gene target failed to amplify. Alignment of the nucleocapsid sequences of three clinical specimens with the reference Wuhan strain of SARS-CoV-2 identified deletions and nucleotide substitutions at the exact locations where the nucleocapsid primers were meant to bind. These genetic changes prevented primer binding, thereby preventing successful amplification and accounting for the poor RT-PCR results for the Nucleocapsid gene in these samples. Figure 5: Alignment of the nucleocapsid sequences from the three samples with the Wuhan variant of SARS-CoV-2(Named the variants S01-S012-S18) The three nucleocapsid sequences were assessed using the available primer sets, and the corresponding results are summarized in the table 10. Discussion The present research was conducted to examine the impact of genetic variations in SARS-CoV-2 on the validity of RT-PCR diagnostic assays[29], [30], [31]. For this purpose, we analyzed 25,658 genomic sequences retrieved from the GISAID database. The research evaluated the performance of 12 widely utilized primer and probe sets, which were each intended to bind to distinct areas of the SARS-CoV-2 genome, including the nucleocapsid (N) gene, envelope (E) gene, RNA-dependent RNA polymerase (RdRp) gene. The results of this research demonstrate that primer and probe binding site mutations can significantly compromise the sensitivity of RT-PCR assays and thereby enhance the risk of false-negative findings[9], [10], [32], [33]. Importantly, mutations within the binding sites decreased significantly the detection capacity of some primers, with those targeting the N gene being of particular vulnerability[33], [34], [35]. For instance, the primer set B"China CDC N" showed a high incidence of mismatches during the later part of the pandemic, something that correlated with the emergence of new virus variants[28], [36]. This result is of particular importance considering that the N gene has been described as having the highest mutation rate among SARS-CoV-2 structural proteins[37], [38], representing a serious challenge to diagnostic tests relying on the exclusive utilization of this genomic target. Our findings extend previous investigations into SARS-CoV-2 genetic evolution's impact on RT-PCR diagnostics. Khan et al. (2020) analyzed 17,026 sequences against 27 RT-PCR assays and found mismatches in seven assays, with China CDC-N primers showing 18.8% mismatch rates[9]. Kocsis et al. (2022) examined over 1.2 million samples, identifying compromised performance in specific primer sets against Omicron variants[33]. However, our study significantly advances this field by analyzing 25,658 sequences across a longer timeframe (2019-2023), revealing dramatic temporal deterioration (B,China CDC primers declining from 100% to 0.15% match rates), and uniquely validating computational predictions through clinical specimens with Sanger sequencing confirmation of primer binding failures. These observations highlight the imperative to employ multiple genetic targets in RT-PCR assays for reducing false-negative outcomes resulting from mutations within a single target area [22], [39]. Chronological and Geographical analyses revealed that the prevalence of mismatches showed variability across regions and time periods[40], [41], most likely reflective of the advent and dissemination of different SARS-CoV-2 variants. Interestingly, a greater frequency of primer mismatches was detected in genomic sequences gathered during the later phases of the pandemic, as VOCs Alpha, Delta, and Omicron arose with each characterized by a constellation of mutations[42], [43]. This observation provides a temporal link between greater prevalence of mismatches and the emergence of VOCs, which are defined by their composite mutational profiles. Figure 6: Variant-specific primer failure rates showing differential impact on diagnostic performance The results of our laboratory experimental study have also corroborated the effect of mutations on the precision of diagnostics.[37], [44] [45]The in vitro experiments demonstrating that genetic changes in the SARS-CoV-2 genome can reduce the validity of diagnostic results. The confirmation highlights the vulnerability of molecular tests to virus evolution, which has significant implications for their application in clinical practice[46]. This discrepancy is indicative of a targeted defect in the detection assay, which could be attributed to genetic mutations that affect the interaction of particular primers with their targets. Following Sanger sequencing of the nucleocapsid region, mutations and deletions within the primer binding sites were identified, revealing the reason for the failure of amplification upon amplification with nucleocapsid-specific primers. This remark points to the practical repercussions of primer mismatches and emphasizes the fundamental requirement for constant monitoring of primer and probe sequences for their compatibility with existing viral genomes. The results demonstrate a concrete consequence of primer mismatches: the possibility of false-negative outcomes in clinical diagnostics, which can result in infections being missed. As such, this highlights the need for ongoing scrutiny and, where required, the redesign of primer and probe sequences to keep pace with the changing genetic diversity of SARS-CoV-2 populations. The findings highlight the need of selecting primer sets for diagnostic assays that have low mismatch rates, particularly in view of the fact that mutations in viruses are perpetual. In order to achieve efficient detection across variations and geographical locations, it is recommended that primer sets which are target conserved sections of the SARS-CoV-2 genome such as those used in the China CDC (A), Hong Kong (C), and Charité (R) assays, to ensure robust detection across diverse variants and geographical regions. Clinical Implications and Future Directions: The clinical implications of our findings extend beyond extend beyond diagnostic accuracy to public health surveillance and epidemic preparedness. False-negative results due to primer mismatches can lead to missed cases, inadequate contact tracing, and compromised pandemic response[47]. The 77.15% failure rate observed for China CDC-N(B) primers during Omicron waves demonstrates the need for diagnostic flexibility and continuous primer surveillance. Laboratories should implement quality control measures following established guidelines[13][14] and establish rapid primer update protocols. Future diagnostic strategies should prioritize universal primers targeting highly conserved genomic regions, combined with real-time bioinformatics monitoring of circulating variants against primer databases [8], [31]. This integrated approach represents a critical advancement for maintaining diagnostic accuracy during viral evolution. Our analysis offers important insights into how SARS-CoV-2 genetic diversity can compromise RT-PCR test efficiency and underscores the imperative need for both multiplex targeting strategies and continuous optimization of diagnostic reagents to match the evolutionary dynamics of the virus. Declarations Acknowledgments Special thanks the GISAID Initiative and all its data contributors for sharing SARS-CoV-2 genome sequences. We also acknowledge the technical support provided by Genesaze Company in sequencing analyses. Data Availability Statement The three SARS‑CoV‑2 nucleocapsid gene sequences generated in this study have been deposited in GenBank under accession numbers OQ152808.1, OQ152122.1 and OP999655.1(Supplementary Data, Table ST2). The SARS‑CoV‑2 genomic sequences and metadata analysed in this study were obtained from the GISAID database (https://www.gisaid.org) under controlled‑access agreements and must be requested directly from GISAID. The multiple‑sequence alignment file and the associated metadata table (mdata.tsv, including accession IDs, collection dates, countries and variant annotations), are available in the Zenodo repository (https://doi.org/10.5281/zenodo.18447903). The Python scripts for primer–genome alignment and mismatch analyses are available at https://github.com/Petotem/SARS_CoV_2_Mutations.git. Additional de‑identified clinical RT‑PCR and Sanger sequencing results are available from the corresponding author upon reasonable request. Funding Statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contributions Statement E.A. conceived and designed the study, supervised the project, provided overall guidance on methodology, and critically revised the manuscript. K.R. developed the Python scripts and performed the bioinformatics data analysis. M.M. contributed to the design of the bioinformatics workflow and assisted in the analysis and interpretation of bioinformatics data. M.K. assisted with the experimental procedures and contributed to data analysis and interpretation. F.S. contributed to the experimental work and sample processing. P.M. performed the RT-PCR experiments, analyzed and interpreted the results, drafted the manuscript, and coordinated the overall study. All authors read and approved the final version of the manuscript. Competing interests The authors declare no competing interests. References Abavisani, M. et al. Mutations in SARS-CoV-2 structural proteins: a global analysis. Virol. J. 19 , 220. 10.1186/s12985-022-01951-7 (2022). Gellenoncourt, S. et al. The Spike-Stabilizing D614G Mutation Interacts with S1/S2 Cleavage Site Mutations to Enhance SARS-CoV-2 Infectivity. mBio 13 (5), e02068–e02022. 10.1128/mbio.02068-22 (2022). Abbasian, M. H., Asgari, Y., Asgari, S. & Masoudi-Nejad, A. 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The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55 (4). 10.1373/clinchem.2008.112797 (2009). Vandenberg, O., Martiny, D., Rochas, O., van Belkum, A. & Kozlakidis, Z. Considerations for quantitative PCR as an accurate molecular diagnostic tool. Clin. Microbiol. Infect. 21 (4), 283–290. 10.1016/j.cmi.2014.12.015 (2015). GenBank, N. Severe acute respiratory syndrome coronavirus 2 isolate SARS-CoV-2/human/Iran/S01/2022, complete genome - OQ152808, [Online]. (2023). Available: https://www.ncbi.nlm.nih.gov/nuccore/OQ152808 GenBank, N. Severe acute respiratory syndrome coronavirus 2 isolate SARS-CoV-2/human/Iran/S12/2022, complete genome - OQ152122.1, [Online]. (2023). Available: https://www.ncbi.nlm.nih.gov/nuccore/OQ152122 GenBank, N. Severe acute respiratory syndrome coronavirus 2 isolate SARS-CoV-2/human/Iran/S18/2022, complete genome - OP999655, [Online]. (2023). Available: https://www.ncbi.nlm.nih.gov/nuccore/OP999655 Contributors, G. SARS-CoV-2 genome sequence EPI_ISL_16093679, [Online]. Available: https://gisaid.org (2023). Contributors, G. SARS-CoV-2 genome sequence EPI_ISL_16066187, [Online]. Available: https://gisaid.org (2023). Contributors, G. SARS-CoV-2 genome sequence EPI_ISL_16066107, [Online]. Available: https://gisaid.org (2023). Nalla, A. K. et al. Comparative Performance of SARS-CoV-2 Detection Assays Using Seven Different Primer-Probe Sets and One Assay Kit. J. Clin. Microbiol. 58 (6), e00557–e00520. 10.1128/JCM.00557-20 (2020). Vogels, C. B. F. et al. Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT-qPCR primer-probe sets. Nat. Microbiol. 5 (10), 1299–1305. 10.1038/s41564-020-0761-6 (2020). Yang, Y. et al. Laboratory diagnosis and monitoring the viral shedding of SARS-CoV-2 infection, Innovation , vol. 1, no. 3, p. 100061, (2020). 10.1016/j.xinn.2020.100061 Corman, V. M. et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance 25 (3), 2000045. 10.2807/1560-7917.ES.2020.25.3.2000045 (2020). Corman, V. M. et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance 25 (3), 2000045. 10.2807/1560-7917.ES.2020.25.3.2000045 (2020). Lu, X. et al. US CDC real-time reverse transcription PCR panel for detection of severe acute respiratory syndrome Coronavirus 2. Emerg. Infect. Dis. 26 (8). 10.3201/eid2608.201246 (2020). Leaungwutiwong, S. et al. The establishment and application of RT-PCR assays for the detection of SARS-CoV-2 by the Department of Medical Sciences, Ministry of Public Health, Thailand. PLoS One . 15 (9), e0238669. 10.1371/journal.pone.0238669 (2020). Borges, V. et al. Tracking SARS-CoV-2 lineage B.1.1.7 dissemination: insights from nationwide spike gene target failure (SGTF) and spike gene late detection (SGLD) data, Portugal, week 49 2020 to week 3 2021, Eurosurveillance , vol. 26, no. 10, p. 2100130, (2021). 10.2807/1560-7917.ES.2021.26.10.2100130 Vankadari, N. Overwhelming mutations or SNPs of SARS-CoV-2: A point of caution, (2020). 10.1016/j.gene.2020.144792 Udugama, B. et al. Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano . 14 (4), 3822–3835. 10.1021/acsnano.0c02624 (2020). Kevadiya, B. D. et al. Diagnostics for SARS-CoV-2 infections. Nat. Mater. 20 (5), 593–605. 10.1038/s41563-020-00906-z (2021). Bakhshandeh, B. et al. Mutations in SARS-CoV-2; Consequences in structure, function, and pathogenicity of the virus, (2021). 10.1016/j.micpath.2021.104831 Kocsis, B. et al. Identification of mutations in SARS-CoV-2 PCR primer regions. Sci. Rep. 12 , 18715. 10.1038/s41598-022-21953-3 (2022). Hassan, S. S., Choudhury, P. P., Roy, B. & Jana, S. S. Missense mutations in SARS-CoV2 genomes from Indian patients. Genomics 112 (6). 10.1016/j.ygeno.2020.08.021 (2020). Bourassa, L. et al. A SARS-CoV-2 nucleocapsid variant that affects antigen test performance. J. Clin. Virol. 141 , 104900. 10.1016/j.jcv.2021.104900 (2021). Metzger, C. et al. PCR performance in the SARS-CoV-2 Omicron variant of concern? medRxiv 10.1101/2021.12.24.21268382 (2021). Tastanova, A. et al. A Comparative Study of Real-Time RT-PCR–Based SARS-CoV-2 Detection Methods and Its Application to Human-Derived and Surface Swabbed Material. J. Mol. Diagn. 23 (7). 10.1016/j.jmoldx.2021.04.009 (2021). Li, D., Zhang, J. & Li, J. Primer design for quantitative real-time PCR for the emerging Coronavirus SARS-CoV-2, (2020). 10.7150/thno.47649 Peñarrubia, L. et al. Multiple assays in a real-time RT-PCR SARS-CoV-2 panel can mitigate the risk of loss of sensitivity by target gene mutation. Sci. Rep. 10 , 18876. 10.1038/s41598-020-75942-8 (2020). Yavarian, J. et al. May,., Whole genome sequencing of SARS-CoV2 strains circulating in Iran during five waves of pandemic, PLoS One , vol. 17, no. 5 (2022). 10.1371/journal.pone.0267847 Ghosh, S. & Chakraborty, S. Phylogenomics analysis of SARS-COV2 genomes reveals distinct selection pressure on different viral strains, Biomed Res Int , vol. 2020, (2020). 10.1155/2020/5746461 Liu, H., Wei, P., Kappler, J. W., Marrack, P. & Zhang, G. SARS-CoV-2 Variants of Concern and Variants of Interest Receptor Binding Domain Mutations and Virus Infectivity, (2022). 10.3389/fimmu.2022.825256 Plante, J. A. et al. The variant gambit: COVID-19’s next move, (2021). 10.1016/j.chom.2021.02.020 Gdoura, M. et al. SARS-CoV2 RT-PCR assays: In vitro comparison of 4 WHO approved protocols on clinical specimens and its implications for real laboratory practice through variant emergence. Virol. J. 19 (1). 10.1186/s12985-022-01784-4 (2022). Wölfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature 581 (7809), 465–469. 10.1038/s41586-020-2196-x (2020). Singanayagam, A. et al. Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020. Eurosurveillance 25 , 2001483. 10.2807/1560-7917.ES.2020.25.32.2001483 (2020). Dahdouh, E., Lázaro-Perona, F., Romero-Gómez, P., Mingorance, J. & García-Rodriguez, C. Ct values and COVID-19 transmission: inconvenient truths and uncomfortable questions. Am. J. Infect. Control . 49 (8), 1052–1053. 10.1016/j.ajic.2021.04.001 (2021). Table Table 10 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files MardaniSupplementaryRevised.docx Table10.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 16 Apr, 2026 Editor invited by journal 31 Mar, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 01 Feb, 2026 First submitted to journal 01 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8703149","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624136935,"identity":"1420ff7c-cfe6-4637-b635-1eed1294e229","order_by":0,"name":"Pedram Mardani","email":"","orcid":"","institution":"Molecular Virology Lab, Department of Microbiology, School of Biology, College of Science, University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Pedram","middleName":"","lastName":"Mardani","suffix":""},{"id":624136936,"identity":"4721b6d7-bb42-4eb5-81a9-1c9e32111919","order_by":1,"name":"Karim Rahimian","email":"","orcid":"","institution":"Institute of Biochemistry and Biophysics (IBB), University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Karim","middleName":"","lastName":"Rahimian","suffix":""},{"id":624136937,"identity":"59508daa-240e-4bb6-b691-9adb462bc9a0","order_by":2,"name":"Mohammadamin Mahmanzar","email":"","orcid":"","institution":"Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa","correspondingAuthor":false,"prefix":"","firstName":"Mohammadamin","middleName":"","lastName":"Mahmanzar","suffix":""},{"id":624136938,"identity":"b840b52c-5082-4f1b-b31f-a36ebfce795a","order_by":3,"name":"Mahdi Karimi","email":"","orcid":"","institution":"Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"","lastName":"Karimi","suffix":""},{"id":624136943,"identity":"c3d6e738-59f5-44fd-8cca-c78c8d89d064","order_by":4,"name":"Fatemeh Saadatpour","email":"","orcid":"","institution":"Molecular Virology Lab, Department of Microbiology, School of Biology, College of Science, University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Saadatpour","suffix":""},{"id":624136944,"identity":"bf53bdcd-33af-42f4-9424-62cf1d88e762","order_by":5,"name":"Ehsan Arefian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYJCCDwkMDHJQtgRROhhnALUYM7CRpAVIJDawEeso/tmHDzY8qLmTvuF+A+OHHwwW+QS1SJxLS2xIOPYsd8MxBmbJHgYJywaCes7wmD9IYDsM0sIgDTTCgKAO+TP8HxsS/h1ONwDa8psoLQZneBgbEtsOJwC1sBFni+EZNsOGxL7DhjOPJbZZ9hgQoUXuDPPDxh/fDsvzHT58+MaPijrCWpAAYwPQnaRoGAWjYBSMglGAEwAA9tU5iIVl9scAAAAASUVORK5CYII=","orcid":"","institution":"Department of Stem Cells Technology and Tissue Regeneration, School of Biology, College of Science, University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Ehsan","middleName":"","lastName":"Arefian","suffix":""}],"badges":[],"createdAt":"2026-01-26 18:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8703149/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8703149/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107661493,"identity":"a15d390c-fb22-48b8-89b5-27b7ae9a9b4b","added_by":"auto","created_at":"2026-04-23 17:10:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":302080,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical workflow for evaluating SARS-CoV-2 RT-PCR primer performance.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/fe777aafa765d05d2fba17e9.png"},{"id":107661490,"identity":"5457eebf-35c6-4580-838f-62d719b4d16b","added_by":"auto","created_at":"2026-04-23 17:10:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80532,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic locations of the designed primers mapped to the Wuhan reference genome of SARS-CoV-2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/b9c3bf39e52366b858b25fe5.png"},{"id":107661502,"identity":"b279c659-5fe8-46d2-a92e-366e8d1d8631","added_by":"auto","created_at":"2026-04-23 17:10:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":203985,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the temporal trend in the performance of different primer sets from 2019 to 2023. The China CDC-B primer initially showed high performance (above 96%) up to early 2020, but then exhibited a dramatic decline in effectiveness, dropping close to zero in the following years. This sharp decrease reflects genetic changes in the virus at the primer’s target regions, rendering it almost useless for accurate detection. In contrast, high-performing primers like Japan NIID-P, US CDC-H, Hong Kong-D, and China CDC-A maintained stable and high performance (consistently above 98%) throughout the entire period.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/5e2c8730bbb73690b4d399cc.png"},{"id":107661503,"identity":"f4d0d80a-1692-4168-8aa4-4468aca47590","added_by":"auto","created_at":"2026-04-23 17:10:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":477277,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of the genomic positions of the 12 investigated primer sets on the Wuhan reference sequence of SARS-CoV-2. This figure illustrates the specific binding sites targeted by each primer set across the viral genome, providing a comprehensive overview of their distribution on the reference strain\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/c5572acf3125b0901739efd9.png"},{"id":107661499,"identity":"71cfcd7e-b6e4-4fed-881d-df5d3b027dcc","added_by":"auto","created_at":"2026-04-23 17:10:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49498,"visible":true,"origin":"","legend":"\u003cp\u003eAlignment of the nucleocapsid sequences from the three samples with the Wuhan variant of SARS-CoV-2(Named the variants S01-S012-S18)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/899de3ee59129b4e2192a941.png"},{"id":107661491,"identity":"a58a94c0-f8e5-4db2-950e-cf7e824aa578","added_by":"auto","created_at":"2026-04-23 17:10:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62118,"visible":true,"origin":"","legend":"\u003cp\u003eVariant-specific primer failure rates showing differential impact on diagnostic performance\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/acbdb6e7283b0db529045008.png"},{"id":107706166,"identity":"c65298ea-f46f-4b5b-9175-a78960a68b5b","added_by":"auto","created_at":"2026-04-24 09:17:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2009978,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/6369ef8d-4d68-42f5-a770-84cf80674d65.pdf"},{"id":107661492,"identity":"2c5da1c7-2ecf-46a1-bbdf-f3e38343a420","added_by":"auto","created_at":"2026-04-23 17:10:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31687,"visible":true,"origin":"","legend":"","description":"","filename":"MardaniSupplementaryRevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/ce5073e39880fdbb11fad9a2.docx"},{"id":107661504,"identity":"5ff2ee36-9c40-412b-8464-409c73d452df","added_by":"auto","created_at":"2026-04-23 17:10:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17080,"visible":true,"origin":"","legend":"","description":"","filename":"Table10.docx","url":"https://assets-eu.researchsquare.com/files/rs-8703149/v1/69ee3294cd1e7f975c351844.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of SARS-CoV-2 Variants at Primer Binding Sites in Diagnostic Platforms and the Effect on Laboratory Diagnostic Samples","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 epidemic had major effects on public health, economies, societies and became a global concern. Believed to have originated from an animal source, COVID-19\u0026mdash;caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)\u0026mdash;quickly spread among people through respiratory droplets and contact.\u003c/p\u003e \u003cp\u003eSARS-CoV-2 primarily affects the respiratory tract, causing pneumonia, but it also impacts other physiological systems, including the gastrointestinal (GI), neurological, and cardiovascular systems. Furthermore, COVID-19 could show unusual symptoms including skin manifestations and sensory abnormalities including taste (ageusia) and anosmia, or loss of smell. While some patients suffer nasal congestion and rhinitis, the most often occurring symptoms are fever, coughing, and tiredness.\u003c/p\u003e \u003cp\u003eBy July 1st, 2023 SARS-CoV-2 was responsible for about 7\u0026nbsp;million deaths worldwide and over 767\u0026nbsp;million confirmed cases. SARS-CoV-2's high transmissibility among humans via respiratory droplets makes fast and accurate detection of it absolutely vital. Early virus identification can be quite helpful in preventing its spread by immediate medical intervention and immediate isolation for afflicted people.\u003c/p\u003e \u003cp\u003eHealthcare professionals should be aware of the atypical symptoms and signs of COVID-19 in those without usual respiratory symptoms, since they assist in identifying and diagnosing the condition.\u003c/p\u003e \u003cp\u003eIn RT-PCR tests for SARS-CoV-2 infection, a diagnostic failure can have major effects on health program that are meant to control and stop community transmission. Controlling the spread of the virus depends on rapidly identifying and isolating infected people. To properly control the COVID-19 epidemic, dependable and accurate diagnostic tests for SARS-CoV-2 must thus be widely available and easily accessible.\u003c/p\u003e \u003cp\u003eThe average mutation frequency in the structural proteins of SARS-CoV-2 was calculated to be 0.027% for the S protein, 0.045% for the E protein; for the M protein is 0.53%; and for the N protein reported 0.088%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. High-frequency mutations in primer-targeted regions include D614G and N501Y in spike gene (\u0026gt;\u0026thinsp;70% prevalence)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e],and R203K, G204R in nucleocapsid gene (15\u0026ndash;25% prevalence in Middle Eastern populations)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] ,directly impacting diagnostic primer binding efficiency and highlighting the need for systematic primer surveillance.\u003c/p\u003e \u003cp\u003eThe most often used molecular assay for SARS-CoV-2 detection is Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Due to its high sensitivity and specificity, RT-PCR has become most reliable approach for SARS-CoV-2 identification. False-negative results can arise, from mismatches between primers, probes and the target sequences resulting from genetic variants and mutations in the viral genome. Thus, it is crucial to regularly evaluate the performance of RT-PCR diagnostic tests and track the development of new SARS-CoV-2 variants[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy considering the high mutation rate in three key regions of E and N gene, and RdRp region in ORF1ab, significant changes have been observed compared to the wild type SARS-CoV-2\u003c/p\u003e \u003cp\u003eDetection techniques depending on a single target inside the viral genome find great difficulty in the presence of these mutations[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have identified primer mismatch challenges in SARS-CoV-2 diagnostics. Khan and Cheung (2020) analyzed 17,026 sequences and found mismatches in seven RT-PCR assays, while Artesi et al. (2020) documented specific E gene mutations causing diagnostic failures. However, these studies were limited to early pandemic data and lacked comprehensive temporal analysis across multiple variants[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive data reveal that a single-point mutation in the primer-probe binding sites of SARS-CoV-2 genes can significantly hinder the amplification process, and in following affecting the RT-PCR results. One of the main concerns is the possibility of false-negative assays, which could result from viral variations that are undetectable with conventional diagnostic tests. This could have significant implications for the overall management of the COVID-19 epidemic[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eBioinformatics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Acquisition from GISAID:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2 genomic sequences were retrieved from the GISAID[12] database (https://www.gisaid.org), which offers extensive access to genetic sequences and their corresponding metadata, including geographic location, collection date of the sample, viral variant, patient characteristics, and more. (Access to the data are provided through the University of Tehran.)\u003c/p\u003e\n\u003cp\u003eSpecific time intervals were selected based on epidemiological data from the World Health Organization COVID-19 Dashboard (https://covid19.who.int). They matched the peaks of COVID-19 cases and deaths globally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequence Processing, Primer Design, and Alignment with SnapGene:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FASTA sequences acquired were first processed in SnapGene software by GSL Biotech (https://www.snapgene.com). SnapGene facilitated the annotation of genetic features, primer design, and alignment processes crucial for this study. Primers to target specific areas of the SARS-CoV-2 genome were created in SnapGene and therefore enabled in silico simulation of PCR amplification and primer specificity analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple Sequence Alignment with MEGA X:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple sequence alignment analysis was accomplished by using of MEGAX software. The software provides sophisticated algorithms for aligning large datasets of nucleotides and hence allowed large-scale comparison of genetic variations among various SARS-CoV-2 sequences acquired from GISAID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimer and Probe Analysis Using Python:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we developed a Python-based pipeline to evaluate primer\u0026ndash;genome alignments across SARS-CoV-2 sequences. The framework integrates NumPy and Pandas for matrix-based statistics and data export, while leveraging custom modules (ReadingFasta and LoadPrimer) to manage genome sequences and primer definitions. The analysis focused on the possibility of primer and probe annealing within a 100 bp region upstream and downstream of the target sites in commercial RT-PCR kits.\u003c/p\u003e\n\u003cp\u003eTable 1: Assessment Criteria for SARS-CoV-2 Primer Sets\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAcceptance Criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDetails\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimer Matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFirst 5 nucleotides at 3\u0026apos; end must match perfectly; \u0026le;3 mismatches in the remaining sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEnsures specific and efficient primer binding to the viral sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProbe Matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;5 mismatches between probe and viral sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAllows for stable hybridization of the probe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMultiple Primer Sets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHybridization to at least one primer in the set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eApplicable when multiple primers target the same genomic region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMelting Temperature Calculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTm difference \u0026le;5\u0026deg;C from the original Tm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTm calculated using Biopython\u0026apos;s nearest-neighbor method; ensures appropriate annealing conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMelting Temperature calculated based on the nearest-neighbor thermodynamic theory by the Biopython library[13], [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe integration of genetic and epidemiological data was done by matching every sequence to its corresponding metadata retrieved from GISAID.\u003c/p\u003e\n\u003cp\u003ePython scripts used in this study are also available publicly on GitHub: https://github.com/Petotem/SARS_CoV_2_Mutations.git\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Management with Microsoft Excel:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to efficiently sort, filter, and perform preliminary statistical analyses, the combined epidemiological and genetic datasets were organized and managed using Microsoft Excel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 50,000 SARS-CoV-2 samples tested in the clinical diagnostic laboratory, three samples were identified as positive results for the Spike gene and negative for the Nucleocapsid gene via the use of RT-PCR. To validate our workflow and the reliability of our database, we specifically examined these three false-negative sequences, designated S01, S12, and S18, to investigate the underlying cause of this discrepancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Extraction of SARS-CoV-2:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from nasopharyngeal swab samples using the QIAamp Viral RNA Mini Kit (Qiagen, Germany) following the manufacturer\u0026apos;s protocol. Purified RNA was quantitated in a NanoDrop One spectrophotometer (Thermo Fisher Scientific, USA) for concentration and purity [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse Transcription and PCR Amplification:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComplementary DNA (cDNA) synthesis was carried out by the SuperScript IV First-Strand Synthesis System (Thermo Fisher Scientific, USA) with random hexamer primers. Target region amplification was carried out by Platinum II Hot-Start PCR Master Mix (Thermo Fisher Scientific, USA) with target-specific primers designed using SnapGene to target regions of interest in the SARS-CoV-2 genome. Three forward primers were designed: F1 (sequence: TGACCCGTGTCCTATTCACT), F2 (sequence: CTACTACCTAGGAACTGGGC), and F3 (sequence: TCGTGGTGGTGACGGTAA), along with two reverse primers: R2 (sequence: CTGCGTAGAAGCCTTTTGGC) and R3 (sequence: TCTGCGGTAAGGCTTGAGT). The binding sites of these primers on the Wuhan reference strain of SARS-CoV-2 are illustrated in Figure 2. They were named systematically for the sake of clarity and were used in the RT-PCR reactions to specifically amplify the nucleocapsid region, thereby forming the basis of the molecular studies presented in this work.\u003c/p\u003e\n\u003cp\u003eFigure 2. Genomic locations of the designed primers mapped to the Wuhan reference genome of SARS-CoV-2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSanger Sequencing Method:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCR products were purified by ExoSAP-IT PCR Product Cleanup Reagent (Applied Biosystems, USA) and sequencing reactions were subcontracted and performed by Genesaze Company (Tehran, Iran) on the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, USA) following the manufacturer\u0026apos;s instructions. Capillary electrophoresis was carried out on an ABI 3500 Genetic Analyzer (Applied Biosystems, USA). Sequencing data were collected and analyzed using Sequencing Analysis Software v6.0 (Applied Biosystems, USA) and aligned to the reference genome using MEGA X and SanpGene.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArchived nasopharyngeal swab samples used in this study were obtained from the Medical Virology Laboratory in Tehran, Iran, where they were originally collected from patients presenting for routine SARS-CoV-2 diagnostic testing and subsequently de-identified before being provided to the researchers. All methods were carried out in accordance with national ethical guidelines of the Islamic Republic of Iran. The study protocol was reviewed and approved by the Ethics Committee of the University of Tehran under approval number 160583\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ein December 2021. Owing to the retrospective use of anonymized residual clinical specimens, the requirement for written informed consent was waived by the Ethics Committee.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe current research involved the evaluation of 25,658 SARS-CoV-2 genomic sequences obtained from the GISAID database. 12 various sets of primers (Table 2) for SARS-CoV-2 identification have been collected and tested[21], [22] . With a large sample size and various primer sets, provide comprehensive insights of the sensitivity and specificity concerning the diagnosis tests.\u003c/p\u003e\n\u003cp\u003eTable 2: Primer Sets and Sequences\u003c/p\u003e\n\u003ctable style=\"float: left;width: 4.7e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvider\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eChina CDC [23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003ensp10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCDC-ORF1-Fwd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina CDC nsp10 f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCCTGTGGGTTTTACACTTAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCDC-ORF1-Rev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina CDC nsp10 r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACGATTGTGCATCAGCTGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCDC-ORF1-Probe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina CDC nsp10 p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCGTCTGCGGTATGTGGAAAGGTTATGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eChina CDC[23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eNucleocapsid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCDC-N-Fwd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina CDC N f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGGGGAACTTCTCCTGCTAGAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCDC-N-Rev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina CDC N r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCAGACATTTTGCTCTCAAGCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCDC-N-Probe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina CDC N p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTTGCTGCTGCTTGACAGATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eHong Kong [11], [24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003ensp14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHKU-ORF1-Fwd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHong Kong N f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTGGGGYTTTACRGGTAACCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHKU-ORF1-Rev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHong Kong N r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAACRCGCTTAACAAAGCACTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHKU-ORF1-Probe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHong Kong N p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTAGTTGTGATGCWATCATGACTAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eHong Kong202[11], [24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eNucleocapsid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHKU-N-Fwd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHong Kong N f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTAATCAGACAAGGAACTGATTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHKU-N-Rev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHong Kong N r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCGAAGGTGTGACTTCCATG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHKU-N-Probe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHong Kong N p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGCAAATTGTGCAATTTGCGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eBerlin [25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eRdRp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBerlin_RdRp_SARSr-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBerlin RdRp f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGTGARATGGTCATGTGTGGCGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBerlin_RdRp_SARSr-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBerlin RdRp r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCARATGTTAAASACACTATTAGCATA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBerlin_RdRp_SARSr-P1\u003c/p\u003e\n \u003cp\u003eBerlin_RdRp_SARSr-P2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBerlin RdRp p1\u003c/p\u003e\n \u003cp\u003eBerlin RdRp p2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCAGGTGGAACCTCATCAGGAGATGC\u003c/p\u003e\n \u003cp\u003eCCAGGTGGWACRTCATCMGGTGATGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eBerlin[25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eEnvelope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBerlin_E_Sarbeco_F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBerlin E f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACAGGTACGTTAATAGTTAATAGCGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBerlin_E_Sarbeco_R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBerlin E r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eATATTGCAGCAGTACGCACACA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBerlin_E_Sarbeco_P1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBerlin E p1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACACTAGCCATCCTTACTGCGCTTCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eUS CDC [5], [26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eNucleocapsid 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N1-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N1 f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGACCCCAAAATCAGCGAAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N1-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N1 r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTCTGGTTACTGCCAGTTGAATCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N1-P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N1 p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACCCCGCATTACGTTTGGTGGACC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eUS CDC [5], [26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eNucleocapsid 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N2-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N2 f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTTACAAACATTGGCCGCAAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N2-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N2 r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGCGCGACATTCCGAAGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N2-P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N2 p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eACAATTTGCCCCCAGCGCTTCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eUS CDC[5], [26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eNucleocapsid 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N3-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N3 f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGGGAGCCTTGAATACACCAAAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N3-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N3 r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTGTAGCACGATTGCAGCATTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCDC_2019-nCoV_N3-P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUS CDC N3 p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAYCACATTGGCACCCGCAATCCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eJapan NIID[7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eNucleocapsid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNIID_2019-nCOV_N_F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJapan NIID N f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAAATTTTGGGGACCAGGAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNIID_2019-nCOV_N_R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJapan NIID N r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTGGCAGCTGTGTAGGTCAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNIID_2019-nCOV_N_P2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJapan NIID N p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eATGTCGCGCATTGGCATGGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eThailand NIH[4], [27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eNucleocapsid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWH-NIC N-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThiland NIH N f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCGTTTGGTGGACCCTCAGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWH-NIC N-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThiland NIH N r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCCCCACTGCGTTCTCCATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWH-NIC N-P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThiland NIH N p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCAACTGGCAGTAACCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eCharit\u0026eacute;[4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eRdRp/Orf1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRdRp_SARSr-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChartie RdRp f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGTGARATGGTCATGTGTGGCGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRdRp_SARSr-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChartie RdRp r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCARATGTTAAASACACTATTAGCATA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRdRp_SARSr-P2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChartie RdRp P2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCAGGTGGAACCTCATCAGGAGATGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3: Illustrates the temporal trend in the performance of different primer sets from 2019 to 2023. The China CDC-B primer initially showed high performance (above 96%) up to early 2020, but then exhibited a dramatic decline in effectiveness, dropping close to zero in the following years. This sharp decrease reflects genetic changes in the virus at the primer\u0026rsquo;s target regions, rendering it almost useless for accurate detection. In contrast, high-performing primers like Japan NIID-P, US CDC-H, Hong Kong-D, and China CDC-A maintained stable and high performance (consistently above 98%) throughout the entire period.\u003c/p\u003e\n\u003cp\u003eIn order to conduct a thorough analysis of the diagnostic potential of these primer sets, the results were separated into four distinct categories with the following characteristics:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Chronological: Comparison of primer performance over various time periods to determine time-related differences in diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003e2. Geographical classification: Analysis of primer efficiency in different geographical areas to identify regional disparities.\u003c/p\u003e\n\u003cp\u003e3. Variant-wise sorting: Analysis based on Variants Of Concern (VOC) and Variants Of Interest (VOI) as defined by the World Health Organization (WHO).\u003c/p\u003e\n\u003cp\u003e4. The total count of positive and negative samples each primer set detects: Comprehensive evaluation of diagnostic performance to establish reliability.\u003c/p\u003e\n\u003cp\u003eFigure 4: Representation of the genomic positions of the 12 investigated primer sets on the Wuhan reference sequence of SARS-CoV-2. This figure illustrates the specific binding sites targeted by each primer set across the viral genome, providing a comprehensive overview of their distribution on the reference strain\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChronological\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronological\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eanalysis revealed gradual increase in primer mismatches over time with considerable expansion particularly since 2021, demonstrating the impact of viral evolution on diagnostics[28]. One possible explanation for this phenomenon is that novel strains of SARS-CoV-2 that include primer-binding site mutation have emerged and propagated quickly. Table 3 presents the total count of the samples gathered every quarter (Q1, Q2, Q3, Q4) over the years 2019 to 2023, while Table 4 provides the percentage of discrepancies in each primer pair.\u003c/p\u003e\n\u003cp\u003eTable 3:\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eTotal number of samples analyzed in each quarter from Q4 2019 to Q1 2023\u003c/p\u003e\n\u003ctable style=\"float: left;width: 5.0e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.8477%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 7.2481%;\"\u003e\u003cstrong\u003e2019.Q4\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong style='font-weight: 700; color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: nowrap; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;'\u003e2020.Q1\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2020.Q3\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2020.Q4\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2021.Q1\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2021.Q2\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2021.Q3\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2022.Q1\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2022.Q2\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2022.Q3\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2022.Q4\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cstrong\u003e2023.Q1\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 15.8477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumer of Samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 7.2481%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Percentage of mismatches per Primer Set each quarter from Q4 2019 to Q1 2023\u003c/p\u003e\n\u003ctable style=\"width: 5.3e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eJapan NIID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eThailand\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNIH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharit\u0026eacute;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019.Q4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020.Q1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020.Q3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e61.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020.Q4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e34.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021.Q1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e38.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021.Q2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e92.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021.Q3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022.Q1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e43.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e41.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022.Q2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022.Q3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e85.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022.Q4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e98.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e90.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023.Q1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e98.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e25.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e89.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGeographical \u0026nbsp;Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeographical\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eanalysis indicated geographical variation in primer mismatches, with high levels of mismatches observed in areas where specific variants of SARS-CoV-2 were largely prevalent. This variation could be the result of differences in the evolution of the virus, differences in populations, or even sampling biases. Table 5 indicates the number of samples collected from each nation, and Table 6 indicates the percentage of mismatches for each primer set in different nations, where only countries with more than 500 sample data are shown and the complete dataset is available in the attachment\u0026nbsp;(Supplementary Data, Table ST1)\u003c/p\u003e\n\u003cp\u003eTable 5: Total Number of Samples per Country\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable style=\"width: 4.2e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample Count(n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample Count(n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHong Kong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePanama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCabo Verde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVietnam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eColombia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNiger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eThailand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArgentina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMorocco\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAzerbaijan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCanary Islands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMyanmar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSaint Barthelemy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBotswana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRussia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCzech Republic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMongolia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSlovakia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIreland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEstonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMauritius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBrunei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBangladesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQatar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLatvia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMaldives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUkraine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCroatia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCosta Rica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReunion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAustria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIceland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBulgaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSerbia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRomania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePeru\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eU.S. Virgin Islands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNicaragua\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEcuador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSlovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGuatemala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUnited Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePuerto Rico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMontenegro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePapua New Guinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEl Salvador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eParaguay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCambodia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLiechtenstein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSenegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLithuania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGuam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGeorgia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEquatorial Guinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHungary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLebanon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLuxembourg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAlgeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNew Zealand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGabon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMozambique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eOman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNorthern Mariana Islands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTogo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSri Lanka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNepal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAruba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGuinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEswatini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eZambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBurkina Faso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 6: Percentage of Mismatches per Primer Set by Country(Over 500 Sample Count)\u003c/p\u003e\n\u003ctable style=\"width: 4.6e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eJapan NIID%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eThailand \u0026nbsp;NIH%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharit\u0026eacute;%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eAustralia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e96.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e35.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e34.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenmark\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e59.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e44.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e45.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eGermany\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e92.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e64.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e63.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e82.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e69.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eJapan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e83.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e92.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e84.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e27.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnited Kingdom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e90.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e66.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e44.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e43.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eVariant-wise Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparison of the performance of primer pairs with SARS-CoV-2 VOC and VOI, as defined by the WHO, was also carried out using a variant-by-variant analysis. The presence of mutations in primer binding sites was found to be more prevalent in specific variations, which resulted in increased mismatch rates in particular primer sets. The total number of samples for each variant is presented in Table 7, and the percentage of mismatches for each primer pair broken down by variant is presented in Table 8.\u003c/p\u003e\n\u003cp\u003eTable 7: Total Number of Samples per Variant\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable style=\"float: left; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpsilon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eEta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eGamma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eKappa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eLota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmicron\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eZeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003esamples (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 8: Percentage of Mismatches per Primer Set by Variant\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable style=\"float: left;width: 5.2e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eJapan NIID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eThailand\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNIH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharit\u0026eacute;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e98.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpsilon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eEta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eGamma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eKappa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eLota\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmicron\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e92.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e66.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e23.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eZeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe overall analysis of mismatches highlighted that some primer sets, particularly the China CDC Nucleocapsid gene primers (B), were less reliable due to high mutation rates in their target regions. This high rate of mismatch, as noted especially in variants like Omicron (99.96%) and Alpha (99.24%), suggests that such primers will not be able to detect a significant percentage of samples, thus promoting false-negative results in diagnostic tests. On the other hand, Primers pairs China CDC (A), Hong Kong (C), United States CDC (H), Japan NIID (P), and Charit\u0026eacute; (R) were extremely dependable, with match rates that exceeded 99% throughout the entire sample of 25,658 sequences. There is a presentation of the overall accuracy per primer set in Table 9, which includes both the match and mismatch percentages.\u003c/p\u003e\n\u003cp\u003eTable 10: Total Count of Matches and Mismatches per Primer Set\u003c/p\u003e\n\u003ctable style=\"float: left;width: 5.0e+2pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHong Kong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eBerlin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eUS CDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eJapan NIID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eThailand\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNIH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharit\u0026eacute;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatch\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e22.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e63.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e95.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e65.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMisMatch\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e77.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e36.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e34.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe resulting sequences were uploaded to the NCBI database under\u0026nbsp;NCBI GenBank under the name of S01, S12, S18 with accessions OQ152808.1[15], OQ152122.1[16], and OP999655.1[17], respectively.\u0026nbsp;and to GISAID under references EPI_ISL_16093679[18], EPI_ISL_16066187[19]\u0026nbsp;and EPI_ISL_16066107[20]. All sequences are publicly accessible and viewable through these respective repositories.\u003c/p\u003e\n\u003cp\u003eOur testing on these samples revealed a discordant result: amplification was successful for the Spike gene target, but the Nucleocapsid gene target failed to amplify.\u003c/p\u003e\n\u003cp\u003eAlignment of the nucleocapsid sequences of three clinical specimens with the reference Wuhan strain of SARS-CoV-2 identified deletions and nucleotide substitutions at the exact locations where the nucleocapsid primers were meant to bind. These genetic changes prevented primer binding, thereby preventing successful amplification and accounting for the poor RT-PCR results for the Nucleocapsid gene in these samples.\u003c/p\u003e\n\u003cp\u003eFigure 5: Alignment of the nucleocapsid sequences from the three samples with the Wuhan variant of SARS-CoV-2(Named the variants S01-S012-S18)\u003c/p\u003e\n\u003cp\u003eThe three nucleocapsid sequences were assessed using the available primer sets, and the corresponding results are summarized in the table 10.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present research was conducted to examine the impact of genetic variations in SARS-CoV-2 on the validity of RT-PCR diagnostic assays[29], [30], [31]. For this purpose, we analyzed 25,658 genomic sequences retrieved from the GISAID database. The research evaluated the performance of 12 widely utilized primer and probe sets, which were each intended to bind to distinct areas of the SARS-CoV-2 genome, including the nucleocapsid (N) gene, envelope (E) gene, RNA-dependent RNA polymerase (RdRp) gene.\u003c/p\u003e\n\u003cp\u003eThe results of this research demonstrate that primer and probe binding site mutations can significantly compromise the sensitivity of RT-PCR assays and thereby enhance the risk of false-negative findings[9], [10], [32], [33]. Importantly, mutations within the binding sites decreased significantly the detection capacity of some primers, with those targeting the N gene being of particular vulnerability[33], [34], [35]. For instance, the primer set B\u0026quot;China CDC N\u0026quot; showed a high incidence of mismatches during the later part of the pandemic, something that correlated with the emergence of new virus variants[28], [36]. This result is of particular importance considering that the N gene has been described as having the highest mutation rate among SARS-CoV-2 structural proteins[37], [38], representing a serious challenge to diagnostic tests relying on the exclusive utilization of this genomic target.\u003c/p\u003e\n\u003cp\u003eOur findings extend previous investigations into SARS-CoV-2 genetic evolution\u0026apos;s impact on RT-PCR diagnostics. Khan et al. (2020) analyzed 17,026 sequences against 27 RT-PCR assays and found mismatches in seven assays, with China CDC-N primers showing 18.8% mismatch rates[9]. Kocsis et al. (2022) examined over 1.2 million samples, identifying compromised performance in specific primer sets against Omicron variants[33]. However, our study significantly advances this field by analyzing 25,658 sequences across a longer timeframe (2019-2023), revealing dramatic temporal deterioration (B,China CDC primers declining from 100% to 0.15% match rates), and uniquely validating computational predictions through clinical specimens with Sanger sequencing confirmation of primer binding failures.\u003c/p\u003e\n\u003cp\u003eThese observations highlight the imperative to employ multiple genetic targets in RT-PCR assays for reducing false-negative outcomes resulting from mutations within a single target area [22], [39].\u003c/p\u003e\n\u003cp\u003eChronological and Geographical analyses revealed that the prevalence of mismatches showed variability across regions and time periods[40], [41], most likely reflective of the advent and dissemination of different SARS-CoV-2 variants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, a greater frequency of primer mismatches was detected in genomic sequences gathered during the later phases of the pandemic, as VOCs Alpha, Delta, and Omicron arose with each characterized by a constellation of mutations[42], [43]. This observation provides a temporal link between greater prevalence of mismatches and the emergence of VOCs, which are defined by their composite mutational profiles.\u003c/p\u003e\n\u003cp\u003eFigure 6: Variant-specific primer failure rates showing differential impact on diagnostic performance\u003c/p\u003e\n\u003cp\u003eThe results of our laboratory experimental study have also corroborated the effect of mutations on the precision of diagnostics.[37], [44] [45]The in vitro experiments demonstrating that genetic changes in the SARS-CoV-2 genome can reduce the validity of diagnostic results. The confirmation highlights the vulnerability of molecular tests to virus evolution, which has significant implications for their application in clinical practice[46]. This discrepancy is indicative of a targeted defect in the detection assay, which could be attributed to genetic mutations that affect the interaction of particular primers with their targets.\u003c/p\u003e\n\u003cp\u003eFollowing Sanger sequencing of the nucleocapsid region, mutations and deletions within the primer binding sites were identified, revealing the reason for the failure of amplification upon amplification with nucleocapsid-specific primers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis remark points to the practical repercussions of primer mismatches and emphasizes the fundamental requirement for constant monitoring of primer and probe sequences for their compatibility with existing viral genomes. The results demonstrate a concrete consequence of primer mismatches: the possibility of false-negative outcomes in clinical diagnostics, which can result in infections being missed. As such, this highlights the need for ongoing scrutiny and, where required, the redesign of primer and probe sequences to keep pace with the changing genetic diversity of SARS-CoV-2 populations.\u003c/p\u003e\n\u003cp\u003eThe findings highlight the need of selecting primer sets for diagnostic assays that have low mismatch rates, particularly in view of the fact that mutations in viruses are perpetual. In order to achieve efficient detection across variations and geographical locations, it is recommended that primer sets which are target conserved sections of the SARS-CoV-2 genome\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003esuch as those used in the China CDC (A), Hong Kong (C), and Charit\u0026eacute; (R) assays, to ensure robust detection across diverse variants and geographical regions.\u003c/p\u003e\n\u003cp\u003eClinical Implications and Future Directions: The clinical implications of our findings extend beyond extend beyond diagnostic accuracy to public health surveillance and epidemic preparedness. False-negative results due to primer mismatches can lead to missed cases, inadequate contact tracing, and compromised pandemic response[47]. The 77.15% failure rate observed for China CDC-N(B) primers during Omicron waves demonstrates the need for diagnostic flexibility and continuous primer surveillance. Laboratories should implement quality control measures following established guidelines[13][14] and establish rapid primer update protocols. Future diagnostic strategies should prioritize universal primers targeting highly conserved genomic regions, combined with real-time bioinformatics monitoring of circulating variants against primer databases [8], [31]. This integrated approach represents a critical advancement for maintaining diagnostic accuracy during viral evolution.\u003c/p\u003e\n\u003cp\u003eOur analysis offers important insights into how SARS-CoV-2 genetic diversity can compromise RT-PCR test efficiency and underscores the imperative need for both multiplex targeting strategies and continuous optimization of diagnostic reagents to match the evolutionary dynamics of the virus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial thanks the GISAID Initiative and all its data contributors for sharing SARS-CoV-2 genome sequences. We also acknowledge the technical support provided by Genesaze Company in sequencing analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe three SARS‑CoV‑2 nucleocapsid gene sequences generated in this study have been deposited in GenBank under accession numbers OQ152808.1, OQ152122.1 and OP999655.1(Supplementary Data, Table ST2). The SARS‑CoV‑2 genomic sequences and metadata analysed in this study were obtained from the GISAID database (https://www.gisaid.org) under controlled‑access agreements and must be requested directly from GISAID. The multiple‑sequence alignment file and the associated metadata table (mdata.tsv, including accession IDs, collection dates, countries and variant annotations), are available in the Zenodo repository (https://doi.org/10.5281/zenodo.18447903). The Python scripts for primer\u0026ndash;genome alignment and mismatch analyses are available at https://github.com/Petotem/SARS_CoV_2_Mutations.git. Additional de‑identified clinical RT‑PCR and Sanger sequencing results are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.A. conceived and designed the study, supervised the project, provided overall guidance on methodology, and critically revised the manuscript. K.R. developed the Python scripts and performed the bioinformatics data analysis. M.M. contributed to the design of the bioinformatics workflow and assisted in the analysis and interpretation of bioinformatics data. M.K. assisted with the experimental procedures and contributed to data analysis and interpretation. F.S. contributed to the experimental work and sample processing. P.M. performed the RT-PCR experiments, analyzed and interpreted the results, drafted the manuscript, and coordinated the overall study. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbavisani, M. et al. Mutations in SARS-CoV-2 structural proteins: a global analysis. \u003cem\u003eVirol. 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Control\u003c/em\u003e. \u003cb\u003e49\u003c/b\u003e (8), 1052\u0026ndash;1053. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajic.2021.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ajic.2021.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 10 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8703149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8703149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the COVID-19 pandemic continues to challenge global health systems, the reliability of diagnostic tests remains a critical concern. The most accurate way to identify SARS-CoV-2 infection is nucleic acid amplification tests (NAATs), especially real-time PCR (RT-PCR) assays. However, changes in SARS-CoV-2 primer and probe binding sites might compromise the accuracy of these diagnostic tests and increase false-negative rates. Real-time PCR serves as the gold standard for SARS-CoV-2 detection but shows 2\u0026ndash;29% false-negative rates.\u003c/p\u003e \u003cp\u003eThe present study analyzed\u0026thinsp;~\u0026thinsp;26,000 SARS-CoV-2 genomic sequences from the Global Initiative on Sharing All Influenza Data (GISAID) database to shed light on genetic variants that affected the performance of ongoing setup RT-PCR primer and probe set. This study assesses 12 primer sets for detecting SARS-CoV-2 variants from late 2019 to early 2023 across four frameworks: chronological, geographical, variant-wise, and diagnostic metrics. We validated computational predictions using clinical specimens and Sanger sequencing. Our findings indicate a correlation between amplification failures and single-point mutations or other genetic alterations in the primer and probe binding sites, leading to false-negative results in RT-PCR testing.\u003c/p\u003e \u003cp\u003eOur findings provide crucial data for RT-PCR assay design and enhancement. Specifically, our analysis provided quantitative mismatch rates (0.15\u0026ndash;77.15%), identified critical binding site mutations causing RT-PCR failures, and established temporal performance patterns tracking variant-driven primer degradation. These results enable evidence-based primer selection and highlight the need for continuous surveillance in viral pandemics. These findings recommend implementing multiplex RT-PCR assays and continuous primer surveillance for reliable COVID-19 diagnosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Investigation of SARS-CoV-2 Variants at Primer Binding Sites in Diagnostic Platforms and the Effect on Laboratory Diagnostic Samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 17:09:49","doi":"10.21203/rs.3.rs-8703149/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-16T07:58:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T04:44:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T12:49:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-01T15:23:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-01T11:39:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18c9cda6-b21c-4cf0-bf2e-cb8a4048341e","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66429090,"name":"Biological sciences/Biological techniques"},{"id":66429092,"name":"Health sciences/Biomarkers"},{"id":66429094,"name":"Health sciences/Diseases"},{"id":66429095,"name":"Biological sciences/Genetics"},{"id":66429096,"name":"Health sciences/Medical research"},{"id":66429097,"name":"Biological sciences/Microbiology"},{"id":66429098,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-04-23T17:09:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 17:09:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8703149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8703149","identity":"rs-8703149","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}