: West Africa faced the COVID-19 pandemic in early March 2020 and, as of March 31, 2022, had more than 900,000 confirmed cases and more than 12,000 deaths. During this period, SARS-CoV-2 genomes evolved genetically, resulting in the emergence of distinct lineages. This review was conducted to provide the epidemiological profile of COVID-19, the mutational profile of SARS-CoV-2, and the dynamics of its lineages in the 16 west African countries by analyzing data from 33 studies and seven situation reports. For a more complete representation of the epidemiology and genetic diversity of SARS-CoV-2, we used reliable public data in addition to eligible studies. As of March 31, 2022, the 16 west African countries experienced four epidemic waves with variable intensities. Higher mortality was noted during the third wave with a case fatality rate (CFR) of 1.9%. After these four epidemic waves, Liberia recorded the highest CFR (4.0%), whereas Benin had the lowest CFR (0.6%). Through mutational analysis, a high genetic heterogeneity of the genomes was observed, with a predominance of mutations in the spike protein. From this high mutational rate, different lineages emerged. Our analysis of the evolutionary diversity allowed us to count 205 lineages circulating in west Africa. This study has provided a good representation of the mutational profile and the prevalence of SARS CoV-2 lineages beyond the knowledge of the global epidemiology of the 16 African countries.

Purpose: Cancer of the prostate (CaP) is the leading cancer among men in sub-Saharan Africa (SSA). A substantial proportion of these men with CaP are diagnosed at late (usually incurable) stages, yet little is known about the etiology of CaP in SSA. Methods: We established the Men of African Descent and Carcinoma of the Prostate Network, which includes seven SSA centers partnering with five US centers to study the genetics and epidemiology of CaP in SSA. We developed common data elements and instruments, regulatory infrastructure, and biosample collection, processing, and shipping protocols. We tested this infrastructure by collecting epidemiologic, medical record, and genomic data from a total of 311 patients with CaP and 218 matched controls recruited at the seven SSA centers. We extracted genomic DNA from whole blood, buffy coat, or buccal swabs from 265 participants and shipped it to the Center for Inherited Disease Research (Baltimore, MD) and the Centre for Proteomics and Genomics Research (Cape Town, South Africa), where genotypes were generated using the UK Biobank Axiom Array. Results: We used common instruments for data collection and entered data into the shared database. Double-entered data from pilot participants showed a 95% to 98% concordance rate, suggesting that data can be collected, entered, and stored with a high degree of accuracy. Genotypes were obtained from 95% of tested DNA samples (100% from blood-derived DNA samples) with high concordance across laboratories. Conclusion: We provide approaches that can produce high-quality epidemiologic and genomic data in multicenter studies of cancer in SSA.

The coronavirus disease 2019 (COVID-19) is a contagious disease caused by a new coronavirus called SARS-CoV-2. The first case was discovered in Wuhan, China, in December 2019, raising concerns about the emergence of a new coronavirus that poses a significant public health risk. The objective of this study, based on data collected and sequenced at the Institut de Recherche en Santé, de Surveillance Epidémiologique et de Formations (IRESSEF), is to characterize the pandemic evolution, establish a relationship between the different strains in each wave, and finally determine the phylodynamic evolution of the pandemic, utilizing microreact simulations. The study shows that SARS-CoV-2 strains have evolved over time and the variability of the virus is characterized by sequencing during each wave, as is its contagiousness (the speed at which it spreads). The pandemic has spread at a rate of 44.34 cases/week during the first wave. Twelve weeks later it has risen to 185.33 cases/week during the second wave. Twenty-three weeks into the pandemic, the numbers have reached 681.77 cases/week during the third wave. During the fourth wave, the rate of infection was found to decrease slightly at 646 cases/week between early December 2021 and mid-January 2022. Data collected during this study also provided us with a geographical distribution of COVID-19, indicating that the epidemic started in Dakar before spreading inland.

We used whole genome sequencing to identify and analyze mutations in SARS-CoV-2 in urban settings during the deadliest wave of the COVID-19 epidemic—from March to April 2021—in Senegal. Nasopharyngeal samples testing positive for SARS-CoV-2 were sequenced on the Illumina NovaSeq 6000 sequencing system using the COVIDSeq protocol. A total of 291 genotypable consensus genome sequences were obtained. Phylogenetic analyses grouped the genomes into 16 distinct PANGOLIN lineages. The major lineage was B.1.1.420, despite circulation of the Alpha variant of concern (VOC). A total of 1125 different SNPs, identified relative to the Wuhan reference genome, were detected. These included 13 SNPs in non-coding regions. An average density of 37.2 SNPs per 1000 nucleotides was found, with the highest density occurring in ORF10. This analysis allowed, for the first time, the detection of a Senegalese SARS-CoV-2 strain belonging to the P.1.14 (GR/20J, Gamma V3) sublineage of the Brazilian P.1 lineage (or Gamma VOC). Overall, our results highlight substantial SARS-CoV-2 diversification in Senegal during the study period.

Acute respiratory tract infections are one of the leading causes of morbidity and mortality worldwide. More data are needed on circulating respiratory microorganisms in different geographical areas and ecosystems. We analyzed nasopharyngeal swabs from 500 febrile patients living in the Niakhar area (Senegal), using FTD^TM multiplex qPCR and simplex qPCR to target a panel of 25 microorganisms. We detected at least one microorganism for 366/500 patients (73.2%), at least one virus for 193/500 (38.6%), and at least one bacterium for 324/500 (64.8%). The most frequently detected microorganisms were Streptococcus pneumoniae (36.8%), Haemophilus influenzae (35.8%), adenovirus (11.8%), influenza viruses (6.4%), rhinovirus (5.0%), SARS-CoV-2 (4.0%), and RSV (4.0%). The main microorganisms significantly associated with respiratory symptoms, with a p-value ≤ 0.05, were influenza virus (11.9% in patients with respiratory symptoms versus 2.9% in patients without), RSV (6.5% versus 2.6%), metapneumovirus (5.4% versus 1.3%), HPIVs (7.6% versus 1.0%), S. pneumoniae (51.9% versus 28.0%), and H. influenzae (54.6% versus 24.5%). Co-infections were significantly associated with respiratory symptoms (65.4% versus 32.9%). All the epidemiological data show a high level of circulation of respiratory pathogens among febrile patients, including those preventable by vaccination such as S. pneumoniae, raising the question of the serotypes currently circulating. Furthermore, the availability of affordable real-time etiological diagnostic tools would enable management to be adapted as effectively as possible.

Background: In Senegal, the incidence of SARS-CoV-2 evolved with four successive epidemic waves. The first wave started in March 2020 with low virus variability, whilst the second outbreak, which started in December 2020, was dominated by the Alpha variant. The third wave took place in June 2021, and the fourth at the end of November 2021. Our interest was to investigate the involvement of variants of concern during these four waves and to track the viral diversity of SARS-CoV-2. Methodology: During the four waves of the pandemic, 276,876 nasopharyngeal swabs were analyzed at the Institut de Recherche en Santé, de Surveillance Epidémiologique et de Formation (IRESSEF). Of these, 22,558 samples tested positive for SARS-CoV-2 by RT-PCR. Then, the virus genomes were sequenced in 817 positive samples using the ARTIC Network of Oxford Nanopore Technologies (ONT). In addition, 10% of the negative samples in RT-PCR new variants were also targeted for the detection of new and previously undescribed variants. Results: Our data have overall shown that the Senegalese strains are very similar to each other or closely related to other strains, such as Gambia, France etc. During the first wave, the most common clade found was 19A (67.5%) and a majority of the samples were of the B.1 (50%) lineage. We noted more diversity during the second wave where clade 20A (38.4%) was more frequent, followed by clade 20B (31.52%) and 20I (9.74%). At the level of lineages, we identified variants of concern as B.1.1.7 (9.74%) and B.1.617.2 (0.86%). In the third wave, we observed at the clade level with mainly 21A (32.63%) and 21J (16.84%). During the fourth wave at the end of November 2021, we mainly identified clade 21K Omicron variant 21K (B.1.1.529 and BA.1) (80.47%) and Delta variant (21A, 21J, and 21I) (AY.103, AY.122, AY.122.1, AY.26, AY.34, AY.36, AY.4, AY.48, AY.57, AY.61, and AY.87) (14.06%). Impact: SARS-CoV-2 diversity may affect the virus’s properties, such as how it spreads, disease severity, or the performance of vaccines, tools, or other public health and social measures. Therefore, such tracking of SARS-CoV-2 variants is not only of public interest, but also highlights the role some African institutes such as IRESSEF with surveillance capabilities through the real-time sequencing of SARS-CoV-2 genomes in the local context. Conclusion: In Senegal, the SARS-CoV-2 pandemic has disrupted the organization of the health system. IRESSEF contributed to put in place strategies to respond effectively to the expectations of medical authorities by providing them with data on the strains circulating in Senegal at each moment of the epidemic.