Quality Control during the Dengue Virus Epidemic of 2024: A Multivariate Approach for Molecular Biology Diagnostics in a Multicenter Study

Abstract The 2024 dengue epidemic in Brazil—the largest arboviral emergency in the country’s history—exposed critical gaps in the reliability of molecular diagnostics across its national public health laboratory network. Quality control (QC) of RT-qPCR assays performed by geographically dispersed Central Public Health Laboratories (LACENs) is essential to ensure the accuracy of epidemiological surveillance and clinical management. We conducted a multicenter QC evaluation of 3,192 complete RT-qPCR runs (19,152 datapoints) for dengue virus serotypes 1–4 (DENV1–4), Zika virus (ZIKV), and Chikungunya virus (CHIKV) across 15 LACENs over one epidemic year. An automated R-based bioinformatic pipeline applied hierarchical clustering (AGNES and DIANA), principal component analysis (PCA), linear and quadratic discriminant analysis (LDA/QDA), Shewhart and XmR control charts, process capability analysis, ANOVA, Baker’s gamma permutation testing, and PVClust bootstrap clustering to positive-control cycle threshold (CT) value datasets. Median CT values for DENV4 positive controls ranged from 26.3 to 30.5 across laboratories, representing an approximately 16-fold difference in measured RNA quantity. PCA explained 54.1%–100% of total variance on PC1 across viral targets. Baker’s gamma permutation tests confirmed significant concordance between AGNES and DIANA hierarchies across all six viral targets. LDA achieved 37.7% and QDA 49.1% cross-validated accuracy in laboratory-of-origin classification. PVClust bootstrap clustering identified DENV2+DENV4 (approximately unbiased probability, AU = 90) as the most analytically coherent serotype pair. ANOVA confirmed significant operator effects on ZIKV CT values (F = 8.799, df = 23), with regression coefficients for specific operators reaching β = +4.01 cycles—equivalent to an approximately 16-fold inferred difference in RNA quantity. Extreme outlier CT values signaled data integrity failures requiring immediate corrective action. The integrated multivariate QC framework substantially outperformed univariate Westgard-rule monitoring. Operator-specific CT deviations of up to four cycles carry direct consequences for clinical classification of borderline specimens. The automated R-based pipeline is operationally feasible in low-resource public health networks and provides a replicable model for arboviral diagnostic QC governance during epidemic emergencies. Author Summary During the 2024 dengue epidemic—the largest Brazil has ever faced—our national laboratory network processed millions of molecular diagnostic tests across 15 states. We wanted to understand whether these results, generated by different laboratories using different instruments and staff, were truly comparable to each other. We found that they were not fully interchangeable: the same standardized viral sample produced results that differed by the equivalent of a 16-fold difference in measured virus quantity, depending on which laboratory analyzed it. The main culprits were the type of machine used and the specific batch of reagents—not the geographic region or the size of the laboratory. We also found that individual technicians introduced measurable and consistent differences, some as large as four amplification cycles, which can be enough to change whether a borderline patient sample is classified as positive or negative. Using a set of advanced statistical tools routinely applied in industrial quality control, we were able to detect these patterns automatically—patterns that conventional monitoring systems would have missed. Our approach is built entirely on free, open-source software and can be adopted by any public health laboratory network in the world facing similar challenges. We believe this framework can strengthen epidemic diagnostic governance not only in Brazil but across all low– and middle-income settings.