Rapid molecular assays for the detection of yellow fever virus in low-resource settings.

BACKGROUND: Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by Aedes mosquitoes. The causative agent, the yellow fever virus (YFV), is found in tropical and subtropical areas of South America and Africa. Although a vaccine is available since the 1930s, YF still causes thousands of deaths and several outbreaks have recently occurred in Africa. Therefore, rapid and reliable diagnostic methods easy to perform in low-resources settings could have a major impact on early detection of outbreaks and implementation of appropriate response strategies such as vaccination and/or vector control.

METHODOLOGY: The aim of this study was to develop a YFV nucleic acid detection method applicable in outbreak investigations and surveillance studies in low-resource and field settings. The method should be simple, robust, rapid and reliable. Therefore, we adopted an isothermal approach and developed a recombinase polymerase amplification (RPA) assay which can be performed with a small portable instrument and easy-to-use lyophilized reagents. The assay was developed in three different formats (real-time with or without microfluidic semi-automated system and lateral-flow assay) to evaluate their application for different purposes. Analytical specificity and sensitivity were evaluated with a wide panel of viruses and serial dilutions of YFV RNA. Mosquito pools and spiked human plasma samples were also tested for assay validation. Finally, real-time RPA in portable format was tested under field conditions in Senegal.

CONCLUSION/SIGNIFICANCE: The assay was able to detect 20 different YFV strains and demonstrated no cross-reactions with closely related viruses. The RPA assay proved to be a robust, portable method with a low detection limit (<21 genome equivalent copies per reaction) and rapid processing time (<20 min). Results from real-time RPA field testing were comparable to results obtained in the laboratory, thus confirming our method is suitable for YFV detection in low-resource settings.