Delineation of the population genetic structure of Culicoides imicola in East and South Africa

BACKGROUND: Culicoides imicola Kieffer, 1913 is the main vector of bluetongue virus (BTV) and African horse sickness virus (AHSV) in Sub-Saharan Africa. Understanding the population genetic structure of this midge and the nature of barriers to gene flow will lead to a deeper understanding of bluetongue epidemiology and more effective vector control in this region. METHODS: A panel of 12 DNA microsatellite markers isolated de novo and mitochondrial DNA were utilized in a study of C. imicola populations from Africa and an outlier population from the Balearic Islands. The DNA microsatellite markers and mitochondrial DNA were also used to examine a population of closely related C. bolitinos Meiswinkel midges. RESULTS: The microsatellite data suggest gene flow between Kenya and south-west Indian Ocean Islands exist while a restricted gene flow between Kenya and South Africa C. imicola populations occurs. Genetic distance correlated with geographic distance by Mantel test. The mitochondrial DNA analysis results imply that the C. imicola populations from Kenya and south-west Indian Ocean Islands (Madagascar and Mauritius) shared haplotypes while C. imicola population from South Africa possessed private haplotypes and the highest nucleotide diversity among the African populations. The Bayesian skyline plot suggested a population growth. CONCLUSIONS: The gene flow demonstrated by this study indicates a potential risk of introduction of new BTV serotypes by wind-borne infected Culicoides into the Islands. Genetic similarity between Mauritius and South Africa may be due to translocation as a result of human-induced activities; this could impact negatively on the livestock industry. The microsatellite markers isolated in this study may be utilised to study C. bolitinos, an important vector of BTV and AHSV in Africa and identify sources of future incursions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13071-015-1277-4) contains supplementary material, which is available to authorized users

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

Few opportunities, much desperation: The Dichotomy of non-agricultural activities and inequality in Western Kenya [Dataset]

Using data from Western Kenya, we confirm the existence of a dichotomous non-agricultural sector. The poverty and inequality implications of the differently motivated diversification strategies only partly correspond to expected patterns. While high-return activities are indeed confined to richer households, low-return activities constitute an important income source for households across the entire income distribution. Finally, we examine the wider implications of our findings for rural livelihoods. We find that only engagement in high-return non-agricultural activities is significantly associated with increased agricultural productivity. It seems that such high-return activities play a key role in triggering cumulative effects of relative livelihood success