A multi-country phase 2 study to evaluate the suitcase lab for rapid detection of SARS-CoV-2 in seven Sub-Saharan African countries: Lessons from the field.

BACKGROUND: The COVID-19 pandemic led to severe health systems collapse, as well as logistics and supply delivery shortages across sectors. Delivery of PCR related healthcare supplies continue to be hindered. There is the need for a rapid and accessible SARS-CoV-2 molecular detection method in low resource settings. OBJECTIVES: To validate a novel isothermal amplification method for rapid detection of SARS-CoV-2 across seven sub-Sharan African countries. STUDY DESIGN: In this multi-country phase 2 diagnostic study, 3,231 clinical samples in seven African sites were tested with two reverse transcription Recombinase-Aided Amplification (RT-RAA) assays (based on SARS-CoV-2 Nucleocapsid (N) gene and RNA-dependent RNA polymerase (RdRP) gene). The test was performed in a mobile suitcase laboratory within 15 min. All results were compared to a real-time RT-PCR assay. Extraction kits based on silica gel or magnetic beads were applied. RESULTS: Four sites demonstrated good to excellent agreement, while three sites showed fair to moderate results. The RdRP gene assay exhibited an overall PPV of 0.92 and a NPV of 0.88. The N gene assay exhibited an overall PPV of 0.93 and a NPV 0.88. The sensitivity of both RT-RAA assays varied depending on the sample Ct values. When comparing sensitivity between sites, values differed considerably. For high viral load samples, the RT-RAA assay sensitivity ranges were between 60.5 and 100% (RdRP assay) and 25 and 98.6 (N assay). CONCLUSION: Overall, the RdRP based RT-RAA test showed the best assay accuracy. This study highlights the challenges of implementing rapid molecular assays in field conditions. Factors that are important for successful deployment across countries include the implementation of standardized operation procedures, in-person continuous training for staff, and enhanced quality control measures.

Discovery and Genomic Characterization of a Novel Henipavirus, Angavokely Virus, from Fruit Bats in Madagascar.

The genus Henipavirus (family Paramyxoviridae) currently comprises seven viruses, four of which have demonstrated prior evidence of zoonotic capacity. These include the biosafety level 4 agents Hendra (HeV) and Nipah (NiV) viruses, which circulate naturally in pteropodid fruit bats. Here, we describe and characterize Angavokely virus (AngV), a divergent henipavirus identified in urine samples from wild, Madagascar fruit bats. We report the nearly complete 16,740-nucleotide genome of AngV, which encodes the six major henipavirus structural proteins (nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, and L polymerase). Within the phosphoprotein (P) gene, we identify an alternative start codon encoding the AngV C protein and a putative mRNA editing site where the insertion of one or two guanine residues encodes, respectively, additional V and W proteins. In other paramyxovirus systems, C, V, and W are accessory proteins involved in antagonism of host immune responses during infection. Phylogenetic analysis suggests that AngV is ancestral to all four previously described bat henipaviruses-HeV, NiV, Cedar virus (CedV), and Ghanaian bat virus (GhV)-but evolved more recently than rodent- and shrew-derived henipaviruses, Mojiang (MojV), Gamak (GAKV), and Daeryong (DARV) viruses. Predictive structure-based alignments suggest that AngV is unlikely to bind ephrin receptors, which mediate cell entry for all other known bat henipaviruses. Identification of the AngV receptor is needed to clarify the virus's potential host range. The presence of V and W proteins in the AngV genome suggest that the virus could be pathogenic following zoonotic spillover. IMPORTANCE Henipaviruses include highly pathogenic emerging zoonotic viruses, derived from bat, rodent, and shrew reservoirs. Bat-borne Hendra (HeV) and Nipah (NiV) are the most well-known henipaviruses, for which no effective antivirals or vaccines for humans have been described. Here, we report the discovery and characterization of a novel henipavirus, Angavokely virus (AngV), isolated from wild fruit bats in Madagascar. Genomic characterization of AngV reveals all major features associated with pathogenicity in other henipaviruses, suggesting that AngV could be pathogenic following spillover to human hosts. Our work suggests that AngV is an ancestral bat henipavirus that likely uses viral entry pathways distinct from those previously described for HeV and NiV. In Madagascar, bats are consumed as a source of human food, presenting opportunities for cross-species transmission. Characterization of novel henipaviruses and documentation of their pathogenic and zoonotic potential are essential to predicting and preventing the emergence of future zoonoses that cause pandemics.

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 (Ct) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-Ct 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 Ct value, suggesting that Ct 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 Ct 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 Ct distributions across three regions in Madagascar. We find that Ct-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 Ct 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.