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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.

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.

Following a first study made to assess the impacts of sea level rise on the Senegalese coastline ( Dennis et al., 1995 ), this vulnerability and adaptation (V&A) study tried to determine the impacts of climate change on two representative coastal zones, the Cap Vert peninsula and the Saloum estuary as well as potential adaptation options. The Intergovernmental Panel on Climate Change (IPCC) Methodology ( Carter et al., 1994 ), completed by the United Nations Environment Programme (UNEP) Handbook ( Feenstra et al., 1998 ), were used together with different models (like the Bruun rule, the FEFLOW model), geographical information system and economic data to determine the physical and socio-economic impacts of different climate change scenarios on these coastal zones. Land losses are expected due to sea level rise that will enhance coastal erosion and increase inundation levels, the second phenomenon being responsible for most of these losses, especially in low lying areas like the Saloum estuary (27% of the total area lost with a 1 m inundation level). The combination of sea level rise and decreased precipitation will increase the salt water intrusion in a number of coastal aquifers, especially around Dakar and in the Saloum estuary. Population at risk of inundation in the Cap Vert peninsula could represent between 1 and 12% of the total population of this area. Economic values at risk were estimated by considering socio-economic scenarios and discount rates. For a 1 m inundation level by 2050, this could represent (for the two areas) 14.1% of the actual Gross Domestic Product. While housings represent more than 90% of this value in the Cap Vert peninsula, in the Saloum estuary, it is the agricultural production which represents the dominant form of value at risk (55%). Only two adaptation options, protection works (sea walls and groins) and afforestation of littoral dunes, were evaluated and costed. For a 1 m inundation level by 2050, this will represent 7.3% of the actual Gross Domestic Product. The main result of this study is the demonstration that protection costs will be lower than economic value at risk contrary to the results of the previous study. This is mainly attributed to the consideration of socio-economic scenarios and a better economic evaluation of the goods and services at risk of inundation.

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learned. As a component of the Platform, the SARS-CoV-2 Data Hubs enabled the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.