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

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 beta; = +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.