Serological evidence of Flavivirus circulation in human populations in Northern Kenya : an assessment of disease risk 2016-2017

BACKGROUND: Yellow fever, Dengue, West Nile and Zika viruses are re-emerging mosquito-borne Flaviviruses of public health concern. However, the extent of human exposure to these viruses and associated disease burden in Kenya and Africa at large remains unknown. We assessed the seroprevalence of Yellow fever and other Flaviviruses in human populations in West Pokot and Turkana Counties of Kenya. These areas border Uganda, South Sudan and Ethiopia where recent outbreaks of Yellow fever and Dengue have been reported, with possibility of spillover to Kenya. METHODOLOGY: Human serum samples collected through a cross-sectional survey in West Pokot and Turkana Counties were screened for neutralizing antibodies to Yellow fever, Dengue-2, West Nile and Zika virus using the Plaque Reduction Neutralization Test (PRNT). Seroprevalence was compared by county, site and important human demographic characteristics. Adjusted odds ratios (aOR) were estimated using Firth logistic regression model. RESULTS: Of 877 samples tested, 127 neutralized with at least one of the four flaviviruses (14.5, 95% CI 12.3–17.0%), with a higher proportion in Turkana (21.1%, n = 87/413) than in West Pokot (8.6%, n = 40/464). Zika virus seroprevalence was significantly higher in West Pokot (7.11%) than in Turkana County (0.24%; χ 2 P < 0.0001). A significantly higher Yellow fever virus seroprevalence was also observed in Turkana (10.7%) compared to West Pokot (1.29%; χ 2 P < 0.0001). A high prevalence of West Nile virus was detected in Turkana County only (10.2%) while Dengue was only detected in one sample, from West Pokot. The odds of infection with West Nile virus was significantly higher in males than in females (aOR = 2.55, 95% CI 1.22–5.34). Similarly, the risk of Zika virus infection in West Pokot was twice higher in males than females (aOR = 2.01, 95% CI 0.91–4.41). CONCLUSION: Evidence of neutralizing antibodies to West Nile and Zika viruses indicates that they have been circulating undetected in human populations in these areas. While the observed Yellow Fever prevalence in Turkana and West Pokot Counties may imply virus activity, we speculate that this could also be as a result of vaccination following the Yellow Fever outbreak in the Omo river valley, South Sudan and Uganda across the borderNational Institutes of Health (NIH), UK Aid from the UK Government, Swedish International Development Cooperation Agency (Sida), Swiss Agency for Development and Cooperation (SDC) and the Kenyan Government.http://www.virologyj.compm2020Medical Virolog

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Genomic surveillance of Rift Valley fever virus: From sequencing to lineage assignment

Genetic evolution of Rift Valley fever virus (RVFV) in Africa has been shaped mainly by environmental changes such as abnormal rainfall patterns and climate change that has occurred over the last few decades. These gradual environmental changes are believed to have effected gene migration from macro (geographical) to micro (reassortment) levels. Presently, 15 lineages of RVFV have been identified to be circulating within the Sub-Saharan Africa. International trade in livestock and movement of mosquitoes are thought to be responsible for the outbreaks occurring outside endemic or enzootic regions. Virus spillover events contribute to outbreaks as was demonstrated by the largest epidemic of 1977 in Egypt. Genomic surveillance of the virus evolution is crucial in developing intervention strategies. Therefore, we have developed a computational tool for rapidly classifying and assigning lineages of the RVFV isolates. The computational method is presented both as a command line tool and a web application hosted at https://www.genomedetective.com/app/typingtool/rvfv/. Validation of the tool has been performed on a large dataset using glycoprotein gene (Gn) and whole genome sequences of the Large (L), Medium (M) and Small (S) segments of the RVFV retrieved from the National Center for Biotechnology Information (NCBI) GenBank database. Using the Gn nucleotide sequences, the RVFV typing tool was able to correctly classify all 234 RVFV sequences at species level with 100% specificity, sensitivity and accuracy. All the sequences in lineages A (n = 10), B (n = 1), C (n = 88), D (n = 1), E (n = 3), F (n = 2), G (n = 2), H (n = 105), I (n = 2), J (n = 1), K (n = 4), L (n = 8), M (n = 1), N (n = 5) and O (n = 1) were also correctly classified at phylogenetic level. Lineage assignment using whole RVFV genome sequences (L, M and S-segments) did not achieve 100% specificity, sensitivity and accuracy for all the sequences analyzed. We further tested our tool using genomic data that we generated by sequencing 5 samples collected following a recent RVF outbreak in Kenya. All the 5 samples were assigned lineage C by both the partial (Gn) and whole genome sequence classifiers. The tool is useful in tracing the origin of outbreaks and supporting surveillance efforts