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

CD4 T-cell enumeration in a field setting : evaluation of CyFlow counter using the CD4 easy count kit-dry and pima CD4 systems

BACKGROUND: Flow Cytometry (FCM) is still considered to be the method of choice for accurate CD4 enumeration. However, the use of FCM in developing countries is problematic due to their cost and complexity. Lower-cost technologies have been introduced. We evaluated CyFlow Counter together with its lyophilized reagents, and Pima CD4 in high-temperature area, using FACSCount as reference. MATERIALS AND METHODS: Whole blood samples were consecutively collected by venipuncture from 111 HIV+ patients and 17 HIV-negative donors. CD4 T-cell enumeration was performed on CyFlow Counter, Pima CD4 and FACSCount. RESULTS: CyFlow Counter and Pima CD4 systems showed good correlation with FACSCount (slope of 0.82 and 0.90, and concordance ρ(c) of 0.94 and 0.98, respectively). CyFlow Counter showed absolute or relative biases (LOA) of −63 cells/mm(3) (−245 to 120) or −9.8% (−38.1 to 18.4) respectively, and Pima CD4 showed biases (LOA) of −30 cells/mm(3) (−160 to 101) or −3.5% (−41.0 to 33.9%). CyFlow Counter and Pima CD4 showed respectively 106.7% and 105.9% of similarity with FACSCount. According to WHO-2010 ART initiation threshold of 350 cells/mm(3), CyFlow Counter and Pima CD4 showed respectively sensibility of 100% and 97%, and specificity of 91% and 93%. CyFlow Counter and Pima CD4 were strongly correlated (slope of 1.09 and ρ(c) of 0.95). These alternative systems showed good agreement with bias of 33 cells/mm(3) (−132 to 203) or 6.3% (−31.2 to 43.8), and similarity of 104.3%. CONCLUSION: CyFlow Counter using CD4 easy count kit-dry and Pima CD4 systems can accurately provide CD4 T-cell counts with acceptable agreement to those of FACSCount

Laboratory and field evaluation of the partec CyFlow MiniPOC for absolute and relative CD4 T-cell enumeration

BACKGROUND:A new CD4 point-of-care instrument, the CyFlow miniPOC, which provides absolute and percentage CD4 T-cells, used for screening and monitoring of HIV-infected patients in resource-limited settings, was introduced recently. We assessed the performance of this novel instrument in a reference laboratory and in a field setting in Senegal. METHODOLOGY:A total of 321 blood samples were obtained from 297 adults and 24 children, all HIV-patients attending university hospitals in Dakar, or health centers in Ziguinchor. Samples were analyzed in parallel on CyFlow miniPOC, FACSCount CD4 and FACSCalibur to assess CyFlow miniPOC precision and accuracy. RESULTS:At the reference lab, CyFlow miniPOC, compared to FACSCalibur, showed an absolute mean bias of -12.6 cells/mm3 and a corresponding relative mean bias of -2.3% for absolute CD4 counts. For CD4 percentages, the absolute mean bias was -0.1%. Compared to FACSCount CD4, the absolute and relative mean biases were -31.2 cells/mm3 and -4.7%, respectively, for CD4 counts, whereas the absolute mean bias for CD4 percentages was 1.3%. The CyFlow miniPOC was able to classify HIV-patients eligible for ART with a sensitivity of ≥ 95% at the different ART-initiation thresholds (200, 350 and 500 CD4 cells/mm3). In the field lab, the room temperature ranged from 30 to 35°C during the working hours. At those temperatures, the CyFlow miniPOC, compared to FACSCount CD4, had an absolute and relative mean bias of 7.6 cells/mm3 and 2.8%, respectively, for absolute CD4 counts, and an absolute mean bias of 0.4% for CD4 percentages. The CyFlow miniPOC showed sensitivity equal or greater than 94%. CONCLUSION:The CyFlow miniPOC showed high agreement with FACSCalibur and FACSCount CD4. The CyFlow miniPOC provides both reliable absolute CD4 counts and CD4 percentages even under the field conditions, and is suitable for monitoring HIV-infected patients in resource-limited settings

Comparison of CD4 T-cell counts between Pima CD4 and CyFlow Counter.

<p>95% CI means 95% of Confidence Interval; Low means CD4 lower than 200 cells/mm3; Medium means CD4 between 200 and 500 cells/mm3; High means CD4 greater than 500 cells/mm3; HIV+  =  HIV-infected patients, LOA  =  limits of agreement, SD  =  standard deviation.</p

Inter-assay precision of FACSCount, CyFlow Counter and Pima CD4.

<p>N means Number of replicates; CV means Coefficient of Variation; SD means absolute standard deviation (presented for low CD4 only); Low means CD4 lower than 200 cells/mm3; Medium means CD4 between 300 and 500 cells/mm3; and High means CD4 greater than 500 cells/mm3.</p

Comparison of CD4 T-cell counts between CyFlow Counter and FACSCount.

<p>95% CI means 95% of Confidence Interval; Low means CD4 lower than 200 cells/mm3; Medium means CD4 between 200 and 500 cells/mm3; High means CD4 greater than 500 cells/mm3; HIV+  =  HIV-infected patients, LOA  =  limits of agreement, SD  =  standard deviation.</p