Results from the second WHO external quality assessment for the molecular detection of respiratory syncytial virus, 2019-2020.

Background: External quality assessments (EQAs) for the molecular detection of human respiratory syncytial virus (RSV) are necessary to ensure the standardisation of reliable results. The Phase II, 2019-2020 World Health Organization (WHO) RSV EQA included 28 laboratories in 26 countries. The EQA panel evaluated performance in the molecular detection and subtyping of RSV-A and RSV-B. This manuscript describes the preparation, distribution, and analysis of the 2019-2020 WHO RSV EQA. Methods: Panel isolates underwent whole genome sequencing and in silico primer matching. The final panel included nine contemporary, one historical virus and two negative controls. The EQA panel was manufactured and distributed by the UK National External Quality Assessment Service (UK NEQAS). National laboratories used WHO reference assays developed by the United States Centers for Disease Control and Prevention, an RSV subtyping assay developed by the Victorian Infectious Diseases Reference Laboratory (Australia), or other in-house or commercial assays already in use at their laboratories. Results: An in silico analysis of isolates showed a good match to assay primer/probes. The panel was distributed to 28 laboratories. Isolates were correctly identified in 98% of samples for detection and 99.6% for subtyping. Conclusions: The WHO RSV EQA 2019-2020 showed that laboratories performed at high standards. Updating the composition of RSV molecular EQAs with contemporary strains to ensure representation of circulating strains, and ensuring primer matching with EQA panel viruses, is advantageous in assessing diagnostic competencies of laboratories. Ongoing EQAs are recommended because of continued evolution of mismatches between current circulating strains and existing primer sets.

Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar.

As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (C t ) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-C t value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on C t value, suggesting that C t value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population-level C t distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional C t distributions across three regions in Madagascar. We find that C t -derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of C t values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.