Two years of genomic surveillance capacity development in Guinea: an operational roadmap for local implementation in low-income countries and tracking of SARS-CoV-2 circulation dynamics

Two years of genomic surveillance capacity development in Guinea: an operational roadmap for local implementation in low-income countries and tracking of SARS-CoV-2 circulation dynamics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Two years of genomic surveillance capacity development in Guinea: an operational roadmap for local implementation in low-income countries and tracking of SARS-CoV-2 circulation dynamics Magassouba Magassouba, Emanuele Gustani-Buss, Kékoura Ifono, Emily Victoria Nelson, and 42 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8530368/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Introduction The COVID-19 pandemic significantly accelerated the development of genomic surveillance capabilities worldwide, though equitable access remains a challenge. On 12 March 2020, Guinea, a low-income country in West Africa, reported its first COVID-19 case; however, no local genomic infrastructure was available at the time. A year later, a long-term mentorship program was initiated to establish a SARS-CoV-2 nanopore sequencing unit at the Centre de Recherche en Virologie, Laboratoire des Fièvres Hémorragiques Virales de Guinée (CRV-LFHVG) in Conakry, Guinea. Here, we describe the establishment of this capacity and its role in uncovering SARS-CoV-2 circulation dynamics in the region. Methods We established a local hub for sequencing training where SARS-CoV-2-positive samples, collected as part of routine diagnostic activities from July 2020 to July 2022, were retrospectively and prospectively sequenced using MinION nanopore. Consensus genomes were generated for variant typing and GISAID-submission. Retrospective phylodynamic analysis was performed. Results By July 2022, the laboratory had generated 238 SARS-CoV-2 consensus sequences with a median genomic recovery of 98.1 % [range: 90.5-99.4], representing 0.64% of the 37,464 confirmed cases reported in the country as of 29 July 2022. These sequences encompassed four waves of infection, with the Delta (21A, 21I and 21J) and Omicron (21K and 21L) variants of concern (VOCs) accounting for 84% of all identified lineages. Notably, phylogeographic reconstructions unveiled introductions of Delta/B.1.617.2 and Delta/AY.37, as well as of Omicron/BA.1.1 and Omicron/BA.1.15.1, from the neighboring Western, Eastern and Middle African regions at dates close to their estimated emergence. Sequencing output was >0.5% of the total positive samples and resulted in the timely delivery of two preliminary variant identification reports to national health authorities, followed by six official reports. Conclusion This work underscores key findings during a global health crisis and offers operational guidance to support future genomic surveillance initiatives in low- and middle-income countries. Sustained financial investment, dedicated time, specialized expertise, efficient logistics, and local ownership are essential for long-term implementation of such capacities. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Health sciences/Health care Biological sciences/Microbiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Since the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019, and, as of December 2025 more than 778 million cases of coronavirus disease 2019 (COVID-19) have been diagnosed worldwide causing more than seven million deaths and substantial economic losses 1 , 2 . In late 2020, SARS-CoV-2 variants emerged carrying a large number of mutations and or deletions, raising concerns about their impact on global public health. As a result, these variants were classified into categories designated as Variants of Concern (VOCs), Variants of Interest (VOIs) and Variants under Monitoring (VUMs) 3 – 5 . The spread of multiple variants around the world highlighted the critical importance of real-time access to genomic surveillance data for tracking variant emergence and guiding public health efforts globally, including the adjustment of molecular tools, treatments, and vaccines 6 . In particular, it allowed for the characterization of mutations potentially driving changes in transmissibility, disease severity, and immune escape 7 . During the pandemic, Alpha (20I; B.1.1.7), Beta (20H; B.1.351), Gamma (20J; P.1), Delta (21A, 21I and 21J; B.1.617.2 and AY.37), and Omicron (21K or BA.1 and 21L or BA.2; B.1.1.529) variants were classified as VOCs 8 , 9 . A few VOCs were first reported on the African continent including Beta and Omicron in South Africa 10 , 11 , as well as the VOIs Eta (21D; B.1.525) in Nigeria 12 . The timely detections of Eta and Omicron variants in South Africa prior to their global spread are two of the many examples highlighting the beneficial impact of locally run sequencing capacities in supporting pandemic control efforts 10 , 11 . The availability of locally operated diagnostic and genomic surveillance facilities during the COVID-19 pandemic exacerbated global disparities in diagnostic capacity, with investment funds primarily bolstering infrastructure development in high-income countries 13 – 16 . Specifically, Brito and colleagues showed that 78% of high-income countries sequenced > 0,5% of their COVID-19 cases compared to only 42% of low- and middle-income countries (LMICs) meeting that threshold in the pandemic’s first two years 13 . In line with this, the World Health Organization (WHO) advocated for a ten year strategy to enhance global genomic surveillance, aiming to promptly identify pathogens as part of epidemic and pandemic preparedness 17 . The African continent saw the establishment of multiple local sequencing facilities, improving variant monitoring and enabling the characterization of pandemic waves 16 , 18 – 21 . This led to the identification of four successive waves in West Africa between March 2020 and March 2022, with the first wave peaking in July 2020, the second in January-February 2021, the third in August 2021 and the fourth in January 2022, each of them driven by distinct variant circulation dynamics 19 . Guinea is a low-income West African country and since the first detection of SARS-CoV-2 in the country on 12 March 2020, Guinea has reported a total of 38582 cases and 468 deaths (case fatality rate (CFR) of 1,2%; as of November 2025) 1 . Retrospective sequencing has provided insights into the circulation of variants in Guinea during the two first waves of the pandemic (i.e., 2020 and early 2021) by analyzing 136 and 19 consensus genomes respectively 22 , 23 . Consistent with the broad West African lineage landscape, lineages 20A and 20B were predominant in Guinea in 2020, whereas the VOC Alpha (20I) and VOI Eta (21D) were the major lineages present during the second wave in early 2021 19,22,23 . Later, recombinant XBB lineage descending from BA.2 variants would be detected in Guinea in the second half of 2022 24 , shortly before the XBB became dominant in the eastern hemisphere 25 . To complement the regional sequencing efforts of Guinea and improve our understanding of in-country variant diversity and dynamics, we established a local nanopore genomic surveillance unit during the pandemic and analyzed the genomic epidemiology of locally generated SARS-CoV-2 genomes. The benefit of such a local setup at the Centre de Recherche en Virologie , Laboratoire des Fièvres Hémorragiques Virales de Guinée (CRV-LFHVG), Conakry, was previously highlighted during the Ebola virus disease resurgence and the Marburg virus detection in Guinea 26 , 27 . The feasibility of deploying and implementing nanopore technology has also been detailed in our previous work 28 – 30 . Here, we underscore the local, regional and global impact of locally operated sequencing platforms and bioinformatic tools in supporting genomic surveillance efforts set up during a public health emergency. This study highlights the challenges faced during in-country implementation and emphasizes the collaborative nature of the work. In addition to providing a crucial operational roadmap for future initiatives aimed at establishing local nanopore sequencing units in resource-limited settings, we use genomic epidemiology to deepen our understanding of SARS-CoV-2 transmission patterns in West-Africa. MATERIALS AND METHODS Ethical approval and Nagoya permit This descriptive research, using anonymized diagnostics surveillance data, has been approved by the National Ethics Committee of Guinea (CNERS) under the number 165/CNERS/24. This work is part of the Nagoya permit number 006/2023/PN. Definition of the study dataset Data were included in the study dataset if they were obtained from samples that had been sequenced at CRV-LFHVG and yielded a genomic recovery fitting the threshold for GISAID submission (50%). The samples had various origins: (i) they directly originated from CRV-LFHVG routine diagnostics activities as part of prospective or retrospective sequencing activities (total of 199 samples with GISAID-submitted sequences generated, Supplemental Table S1), or (ii) they originated from other laboratories in Guinea including Laboratoire des Fièvres Hémorragiques Virales de l’Hôpital Régional de N’Zérékoré (LFHV-HRNZE, N’Zérékoré) and the Institut National de Santé Publique (INSP) which shared their materials for the retrospective sequencing service offered by CRV-LFHVG with a total of 34 RNAs from LFHV-HRNZE and five from INSP. In total, 238 GISAID-submitted sequences have been generated by the CRV-LFHVG laboratory and used for phylogenetic analysis (Table 1). Samples, viral RNA extraction and SARS-CoV-2 diagnostics at CRV-LFHVG Nasopharyngeal swabs collected as part of the in-country surveillance system for COVID-19 were sent to CRV-LFHVG for real-time reverse transcription PCR (RT-PCR) processing as part of routine diagnostic activities. Various kits for the inactivation of samples, extraction of RNA, and RT-PCR were used during the pandemic depending on what was available from donations that were made, kit availability and supply from manufacturers. All kits were used according to manufacturers’ instructions. Extraction kits included (i) DaAn Gene nucleic acid extraction kit (DaAnGene Co Ltd., China), (ii) QIAamp Viral RNA Mini Kit (Qiagen, Germany), (iii) Quick-RNA Viral Kit (Zymo Research, United States) and (iv) AmpliSens® Ribo-prep (InterLabService, Russia). The RT-PCR assays included (i) Fosun COVID-19 RT-PCR detection Kit (Fosun Diagnostics, China), (ii) Novel Coronavirus (2019-nCoV) Nucleic Acid Diagnostic Kit (Sansure Biotech Inc, China), (iii) Detection Kit for 2019-nCoV (PCR-Fluorescence) (DaAnGene Co Ltd., China), (iv) RealStar® SARS-CoV-2 RT-PCR Kit 1.0 (altona Diagnostics, Germany), and (v) AmpliSens® SARS-Coronavirus (InterLabService, Russia). Two real-time thermal cyclers platforms were used including the Rotor Gene Q® (Qiagen, Germany) and BioRad CFX96™ (BioRad, Germany). Leftover RNAs after RT-PCR setup were kept at 4°C until results were validated according to manufacturers’ instruction algorithms. As part of the real time genomic monitoring of variants from September 2021 to July 2022, RNAs with a SARS-CoV-2 positive RT-PCR result were selected and transferred to the sequencing laboratory for further processing with sequencing. The retrospective sequencing activities were performed using stored leftover swab aliquots (-20°C) which were collected during the period of July 2020 to September 2021. The lack of material stored prior to July 2020 did not allow for an earlier retrospective testing. For the retrospective testing and sequencing work, RNAs were extracted with the available extraction kits and RT-PCR analysis was re-run with the available assays to verify the cycle threshold (Ct) value before proceeding with sequencing. Overall, all SARS-CoV-2 positive RNAs used for variant tracking covered the period from July 2020 to July 2022. Amplicon preparation, MinION library preparation and next generation nanopore sequencing at CRV-LFHVG Leftover RNA extracts used for next generation sequencing originated from either prospective activity (i.e. routine diagnostics) or retrospective testing. Samples with a cycle threshold (Ct) <30 were selected and processed for amplicon-based sequencing using Midnight RT PCR Expansion (EXP-MRT001, Oxford Nanopore Technologies (ONT), United Kingdom). Sequencing experimental setup included: (i) inclusion of the negative RNA extraction controls corresponding to each of the RNA batches to be tested, (ii) inclusion of one sequencing negative control per sequencing run, (iii) doubling the volumes of some steps to minimize pipetting errors, (iv) pooling of RNA batches with a range of maximum five Ct values (e.g., samples from Ct 20 to Ct 25 were pooled in one sequencing run), (v) the pooling of a maximum of 24 samples per flow cell. Briefly, RNA was directly used for cDNA synthesis using reagents of the Midnight RT PCR Expansion and the cDNA generated was used as template for the multiplex PCR in two reaction pools. The resulting amplicons from the two PCR pools were combined and processed as per protocol for barcoding and multiplexing using the Rapid Barcoding Kit (SQK-RBK110·96). For the preparation of less than 11 samples, each sample was prepared in replicates to achieve the library concentration required for sequencing. One unique barcode was used per sample. The pooled, barcoded amplicons were quantified using the Qubit Fluorometer (Thermo Fisher Scientific) aiming for a minimum recovery of 600 to 800 ng in a volume of 11 µl. Libraries were loaded onto the R9.4.1 Flow Cells (FLO-MIN106D, ONT) and run on the Mk1C device (ONT) using the MinKNOW version 21.02.2 with fast5 set to 1000 reads per file and live basecalling disabled. Sequencing runs were stopped after ~24hr and fast5 files were transferred on a laptop for basecalling and demultiplexing 26 . Bioinformatics, consensus generation, lineage identification, data curation and sharing during the pandemic at CRV-LFHVG Basecalling and demultiplexing were performed using Guppy version 5.0.16 to obtain fastq files. The following parameters were used: high accuracy basecalling, mid-strand barcodes on, and override minimum mid barcoding score on and set to 50. A modem was configured to ensure optimal internet connection for the analysis of fastq files (using approximately 5 Gb of data). The epi2me™ ARTIC SARS-CoV-2 pipeline (wf-artic workflow) with different versions available at each time of analysis was used for consensus generation from fastq files according to the developers’ instructions. Output consensus files were checked using CLC Main Workbench (Qiagen, Germany) and lineages further identified using the Nextclade desktop app (https://clades.nextstrain.org/) and Pangolin 8,31 . A total of 238 consensus sequences have been generated at CRV-LFHVG, curated following GISAID’s algorithm and submitted in different batches to GISAID throughout the pandemic 32 . The related metadata are in the Supplemental File S1. Phylogenetic analyses For the analysis performed here, the 238 genomes were assigned to lineages using Phylogenetic Assignment of Named Global Outbreak Lineages (pangolin; v4.3) 33 and to clades using Nextclade (v3.8.2) 31 . To provide a global context, high-quality complete genome sequences reported from Guinea and from the six global regions were downloaded from GISAID. For further analysis, global genomes related to the lineage diversity assigned in this study were down-sampled biweekly to cover the time interval explored in this study. A total of 3400 sequences collected between 31 December 2019 and 11 November 2022 were obtained as follows: Africa (n = 442), Asia (n = 556), Europe (n = 657), North America (n = 571), South America (n = 349) and Oceania (n = 212). The whole dataset was assigned using Nextclade 3.8.2 31 , and the alignment performed using Nextclade CLI (3.7.1) 34 . The trimming and checking of data integrity were done using Aliview 35 . For the former VOC Delta, the database creation and formatting followed the same criteria as those described above totaling 4557 genomes sampled between 7 November 2020 and 1 April 2022 comprising Asia (n = 180), Eastern Africa (1809), Europe (n = 209), Middle Africa (350), North America (n = 212), Northern Africa (403), Oceania (n = 6), South America (n = 5), Southern Africa (5), and Western Africa (818). A similar method was used for the VOC Omicron resulting in 2885 genomes sampled between 5 November 2021 and 23 August 2022, including Asia (n = 187), Central America (12), Eastern Africa (441), Europe (n = 343), Middle Africa (43), North America (n = 375), Northern Africa (59), Oceania (n = 115), South America (n = 172), Southern Africa (792), and Western Africa (237). Maximum likelihood phylogenetic trees for these datasets were constructed using IQTREE2 (v.2.3.4) and a General Time-Reversible (GTR) with empirical base frequencies and gamma-distributed rate heterogeneity across four categories, which was selected as the best fitting substitution model 36 . The branch support was inferred by ultrafast bootstrap approximation (UFBoot) and Shimodaira-Hasegawa-like approximate likelihood ratio test (SH-aLRT) with 1000 replicates. A root-to-tip regression was conducted in TempEst v1.5.3 to explore the relationship between genetic divergence and time, evaluating the degree and pattern of temporal signal in the dataset, and to exclude outliers or identify assembly issues 37 . Phylogeographic discrete diffusion analysis of selected cluster for the VOCs Delta (AY.37 and B.1.617.2) and Omicron (BA.1.15 and BA.1.1) A spatiotemporal reconstruction using BEAST v1.10.5 with BEAGLE v4.0.0 was performed using supported subtrees (considering UFBoot and SH-like replicates) clustering with the most frequent lineages identified here to optimize the high-burden computational analysis 38,39 . The substitution model specified was Hasegawa, Kishino, Yano (HKY) nucleotide with gamma-distributed rate variation among sites 40 , combined with a strict molecular clock model (fixed rate = 8 x 10 −4 substitutions per site per year), a non-parametric skygrid coalescent tree model, informed by root height prior distribution 41 . To model the location transition, the asymmetric discrete state diffusion in a Continuous-Time Markov Chain process using the Bayesian stochastic search variable selection (BSSVS) was used to determine routes of significant non-zero migration rates, estimating the posterior inclusion probability (State Posterior Probability or SPP), which provides the supported fluxes by Bayes factor test (Bayes factor >3) 42 . Markov jump counts between locations were extracted using the BEAST tool TreeMarkovJumpHistoryAnalyzer 43 . The Markov chain Monte Carlo analyses were run for 200 million steps sampling every 10000 steps. The effective sample size (ESS) for each parameter was inspected with TRACER v.1.7 and the runs were combined with LogCombiner 38,44 . For each selected cluster (subtree), a maximum clade credibility (MCC) tree was generated from the posterior samples using TreeAnnotator 39 . The resulting trees were visualized and annotated using the ggtree package 45 . RESULTS The context for capacity development As the reference laboratory in Guinea for measles and viral hemorrhagic fevers, including Ebola virus disease and yellow fever, CRV-LFHVG was mandated by the Guinean Ministry of Health to assist with the in-country testing for SARS-CoV-2. Between March 2020 and July 2022, the laboratory tested 38130 samples, of which 5420 were positive for SARS-CoV-2 (Figure 1, Supplemental Table S1). Overall, this accounted for approximately 5,3% of the countrywide number of tests performed during this period (Guinea surveillance data with 725.278 tests, 12 March 2020 to 29 July 2022) and led to the diagnosis of about 14,5% of all confirmed cases (Guinea surveillance data with 37464 confirmed cases, 12 March 2020 to 29 July 2022). Furthermore, the establishment of the genomic capacity accounted for 3,7% (n = 199) of all positive samples (n = 5420) being sequenced (Supplemental Table S1). During the peaks of the pandemic, the number of diagnostics tests reached up to 135 tests per day, which were manually performed by lab staff (i.e. no automated platforms). The deployment of in-country sequencing at CRV-LFHVG in 2021 during the Ebola virus disease resurgence ultimately stimulated the launch of the SARS-CoV-2 genomic surveillance capacity development program, following a request from the Agence de Sécurité Sanitaire (ANSS) of the Guinean Ministry of Health 26 . The BNITM, through its humanitarian aid cluster, the European Mobile Laboratory (EMLab), and with support from the WHO/GOARN and Guinean health authorities, planned for the implementation of a two-year training program aiming to establish an independently-operated nanopore sequencing unit for SARS-CoV-2 in Guinea. Logistical considerations in developing SARS-CoV-2 nanopore sequencing capabilities Between March 2021 and December 2022, a total of ten missions to Guinea were conducted, with 32 staff deployed (i.e., deployment mechanisms through either GOARN/WHO/EMLab or BNITM/EMLab), to support CRV-LFHVG with diagnostics strengthening and implementation of nanopore sequencing for genomic surveillance. This included scaled-up training encompassing both wet lab and bioinformatics components. Expert teams of three to five staff rotating every four to five weeks from March 2021 to June 2021 were deployed (i.e.17 experts). From August to December 2021, the frequency of training decreased, with three missions of two- to three-weeks each and a total of seven specialized staff deployed. In early, mid and end of 2022, three training missions (three- to four-weeks each) with eight experts took place. Having French speaking experts on teams was a key component of ensuring efficient communication and knowledge transfer. During this period, six local staff were trained and two became heads of the sequencing unit. The logistic backbone from Germany to Guinea was substantial, consisting of 20 shipments in 2021 and six in 2022, either via courier or as checked luggage of deployed teams. These supplied the laboratory with reagents, consumables and equipment to set up and sustain diagnostics and sequencing operations. Redesigning of the laboratory organization, rewiring of the infrastructure and installation of additional generators were necessary to prevent contamination risks and support access to uninterrupted power. Implementation of standard operating procedures (SOPs) encompassing laboratory, bioinformatics, data storage and back-up processes ensured smooth implementation of a quality assurance management system for delivery of quality data. The supplemental Figure S1 provides an example of a minimum laboratory setup and minimum necessary equipment. Between March and August 2021, two amplicon-based sequencing protocols were tested at CRV-LFHVG, namely the ARTIC nCoV-2019 sequencing protocol V3 (ARTIC network and Integrated DNA Technologies GmbH (IDT), Germany) and the NEBNext® ARTIC SARS-CoV-2 Companion Kit (New England Biolabs, United Kingdom). Considering minor optimisations between runs in an attempt to improve genomic recoveries, a total of nine sequencing runs were performed on 57 SARS-CoV-2-positive samples, all of which yielded low genomic recoveries [median of 46,9%, range: 6-93,1%, Inter-Quartile Range (IQR): 25,1-84,9%]. None of these sequences could be submitted to GISAID, as the threshold for submission (50%) was not met. Yet, preliminary findings could still be drawn with the identification of the VOCs Alpha and Delta, as well as the VOI Eta, which were immediately reported in April and August 2021 to the Ministry of Health of Guinea. From October 2021, the use of the ONT Midnight kit (see Materials and Methods) led to the successful retrieval of 238 consensus sequences with a median genomic recovery of 98,1% [range: 90,5-99,4%, IQR: 96,5-99,3%] (Figure 2B). Until April 2022, a total of six variant monitoring reports were shared with the Guinean health authorities before GISAID submission (Figure 1). Broad impact within and beyond Guinea By October 2021, the SARS-CoV-2 sequencing unit at CRV-LFHVG was fully operational, enabling other laboratories in Guinea to send samples for variant identification. This resulted in additional SARS-CoV-2 sequences from LFHV-HRNZE (n = 34) and INSP (n = 5). Furthermore, the unit facilitated transborder collaboration with the Irrua Specialist Teaching Hospital (ISTH) in Edo State, Nigeria, which sought to establish similar capabilities. Using CRV-LFHVG as a hub, six Nigerian scientists participated in two separate 2-week sequencing training sessions from late 2021 to the end of 2022, making the Guinean laboratory a regional training hub where technical knowledge was acquired by experienced trainers who would eventually teach others at their original laboratories (i.e. trainers of trainers). Lineage characterization, July 2020 to July 2022 A total of 238 sequences were generated at CRV-LFHVG and covered the four waves of the pandemic period from July 2020 to July 2022 (Figure 1 and 2B, Supplemental file S1). Most of the samples sequenced originated from the Conakry prefecture (75,6%, n = 180 samples labeled as Conakry, Ratoma, Kaloum, Dixin, Matoto), followed by N'Zérékoré (14,4%, n = 34), Forecariah (5%, n = 12) and a minor percentage from various regions throughout the country (5%, n = 12). The genomes were assigned to ten clades (Nextclade classification) or 23 lineages with four VOCs and VOI identified as per WHO label including Omicron, Delta, Alpha and Eta (Supplementary Table S2). During the first wave in 2020, the lineages B.1, B.1.1, B.1.1.1 and B.1.1.318 (clades 20A, 20B and 20D) were predominant, followed by a diversity shift in early 2021 with the appearance of the VOC Alpha B.1.1.7 (20I) and VOI Eta B.1.525 (21D) (Figure 2). From July 2021 to November 2021, the VOC Delta B.1.617.2, AY.34.1, AY.36, AY.37, AY.6 (21J and 21A) was the most identified variant in our dataset. From December 2021 and until July 2022, nearly all sequences were identified as VOI Omicron BA.1, BA.1.1, BA.1.1.1, BA.1.1.14, BA.1.14, BA.1.15.1, BA.1.16, BA.1.18, BA.2 and BA.2.10 (21K and 21L, Supplementary Table S2 and Figure 2A). The mutation annotation provided by Nextclade showed that the Spike (S) protein had the highest number of amino acid substitutions with 52,5% (n = 123) of all analyzed samples, harboring mutations such as S:K417N, S:N440K, S:G446S, S:E484A, S:Q493R, S:G496S, S:Q498R, S:N501Y and S:Y505H, while one mutation, S:L452R, was seen in 31,5% (n = 75) of the genomes. Global phylodynamic analysis To place the SARS-CoV-2 genetic diversity identified from our dataset in a global context, we performed a time-calibrated analysis starting with a maximum likelihood phylogenetic tree. A dataset of well-curated, complete and high-quality genomes (n = 3.400) representing the different clades from six regions worldwide were retrieved from GISAID and added to the 238 sequences generated locally (Figure 3). The phylogeny shows well-supported subtrees containing the study genomes, suggesting multiple potential introductions of SARS-CoV-2 in Guinea and also fitting the global distribution of SARS-CoV-2 in the respective period of the pandemic. These subtrees consisted in large clusters of the Delta (third wave) and Omicron (fourth wave) VOCs. To gain insights into the patterns of introduction and progression within Africa of the two most frequently identified VOCs in this study (Delta and Omicron), we tailored a phylogenetic analysis to examine their diversity and distribution across continental and worldwide representative clades. We then focused on the five subregions of Africa (i.e., Northern, Southern, Eastern, Western and Middle). The VOC Delta was first reported in India in late 2020 and thus 4557 Delta sequences were assembled to study the local (Guinea) and global (Africa) context of the study genomes (Figure 4). The analysis supports two subtrees, one represented by the lineage AY.37 (n = 40, 16,8%) (Figure 4B) and one by B.1.617.2 (n = 25, 10,5%) (Figure 4C). To further explore the introduction and circulation of AY.37 and B.1.617.2, Bayesian discrete phylogeographic analysis was performed to model the spatial transitions between the regions evaluated here and for each subtree. The introduction of the Delta lineage AY.37 was estimated to have occurred from a Western Africa region (SPP = 0,99) seeding into Guinea on 25 May 2021 (95% highest posterior density (HPD): 23 May–23 July 2021) (Figure 4B). For lineage B.1.617.2, the ancestral location reconstruction also suggested a Western Africa origin (SPP = 0,39) on 4 April 2021 (95% HDP: 6 March–30 April 2021) (Figure 4C). A similar strategy was used for Omicron which was first detected in South Africa and Botswana in mid-November 2021. A total of 2885 sequences representing all BA.1 lineage and sub-lineages worldwide were assembled to produce a phylogenetic tree (Figure 5). Our analysis revealed multiple introductions for BA.1 and two supported subtrees for lineages BA.1.15.1 (n = 38, 16%) and BA.1.1 (n = 27, 11,3%) (Figure 5A). The phylogeographic inference for BA.1.15.1 dated the introduction into Guinea on 23 November 2021 (95% HDP: 14 November – 30 November 2021) with a likely source from Middle Africa (SPP = 1) (Figure 5B). For BA.1.1, our analysis suggested an introduction from Eastern Africa (SPP = 1) on 24 November 2021 (95% HDP: 10 November – 6 December 2021) (Figure 5C). DISCUSSION Since SARS-CoV-2 was first identified, genomic surveillance combined with evolutionary epidemiology has helped untangle the complexity of worldwide variant spread. However, disparities in genomic surveillance capabilities among LMICs relative to high-income countries have been broadly highlighted 13 . Here, we illustrate relevant achievements following the establishment of locally run sequencing capacity in Guinea (i.e. no export of samples abroad). Our study offers useful practical lessons learned during a long-term capacity development program, including a model of genomic surveillance for tracking the spatio-temporal dynamics of pathogen variants during an ongoing public health crisis. The successful establishment of genomic surveillance capacity led to the generation of 238 consensus sequences obtained locally by prospective and retrospective sequencing of diagnostic samples collected between July 2020 and July 2022. Overall, these sequences contributed to 27,8% of sequences collected in Guinea during this time frame and were deposited on GISAID (n = 856, March 2020-July 2022). At the laboratory level, this work accounted for 3,7% of SARS-CoV-2 positive samples being sequenced (Supplemental Table S1 ), and for 0,64% of all confirmed cases at national level (n = 37464; 29 July 2022). This outcome positions Guinea slightly above the sequencing intensity threshold of 0,5% suggested by Brito and colleagues as a potential marker for genomic sequencing ability although a turnaround time of < 21 days from sample collection to GISAID submission was also suggested as a proxy for genomic surveillance capability 13 . The median of 172,5 days [range: 37–563; IQR: 116–317, n = 238 sequences] observed here is likely biased by a combination of factors such as late implementation during the pandemic (October 2021), substantial yield of retrospective sequencing, time allocation for training, data curation and sharing to health authorities prior to GISAID submission. Such bottlenecks should definitely be considered in improving future preparedness activities for timely sharing. Previous expertise of the local laboratory workforce in human diagnostics and molecular biology likely facilitated the successful setup and provided access to good quality genetic material for sequencing. Notably, some aspects should be considered for sustained impact including troubleshooting training, access to English courses if applicable, and implementation of robust workflows with a quality assurance management system for laboratory process tracking, data validation and curation to deliver high quality data. Furthermore, delivery of broad bioinformatics training ranging from computer programming courses, to computer setup, data analysis, data storage and backup was key to local workforce development and sustainable knowledge transfer. These aspects were implemented by the large number of experts deployed to support diagnostics refreshers, sequencing trainings and knowledge transfer. The online availability of user-friendly tools, such as Nextstrain or Pangolin 8 , 31 , 32 , 34 , 46 , significantly facilitated SARS-CoV-2 sequencing training with data analysis, variant identification and phylogenetic investigations 47 , 48 . Yet, prior knowledge of bioinformatics still remains a prerequisite for staff and the scarcity of such experts remains a major obstacle to maintaining capabilities. Mobilization of funds to foster bioinformatics capacity in LMICs must become a priority 13 , 17 , 21 , 49 . Operational challenges in such settings are considerable, ranging from import/export procedures, security and political context, laboratory location and infrastructure to chosen sequencing technology, access to the power grid and running water, data access and dissemination rights, or availability to in-country or within-continent supply chains (temperature-controlled) for reagents. These aspects must be addressed to not only build robust genomic surveillance capabilities but also to strengthen resilient and self-sufficient public health systems in such settings 50 , 51 . The 26 shipments from Germany to Guinea in this study are just one example of such challenges and underline the urgent need for equitable market access, as well as country leadership and engagement in promoting such access 17 , 21 , 49 – 51 . Another fundamental aspect was the fruitful South-South cooperation with Nigeria, which expedited the establishment of a similar capacity at ISTH in April 2022. Guinea as a training hub was opportune for improving on-site know-how, best practices and staff expertise in operating such a laboratory in an environment reflecting best the daily reality in LMICs. The cooperation showcased the benefits of establishing a regional training hub where African scientists were trained for subsequently disseminating acquired knowledge in their original labs, thus making this capacity building model a sustainable one. The distribution of the 238 SARS-CoV-2 sequences is similar with previously reported dynamics during the early waves in Guinea and West-Africa 19 , 22 , 23 . Initially dominated by 20A and 20B clades, the Alpha VOCs and the Eta VOI subsequently dominated the epidemiological landscape during the second wave in Guinea. Our dataset further shows that the VOCs Delta and Omicron dominated the third and fourth waves in Guinea with a predominance of Delta/AY.37 and Omicron/BA.2, which had not yet been reported in Guinea at the time. Our results may be biased by sampling limited to certain districts, sample selection based on low Ct values and timeline of operational readiness affecting overall lineage frequencies and leading to the VOCs Delta and Omicron dominating the dataset. Yet, identification of virus lineages and understanding their diffusion across time allows for mutation screening to identify potential sites located primarily in the receptor binding domain (RBD) of the viral Spike protein which are linked with immune escape and reduced vaccine efficacy 7 . Thus, amino acid changes identified in our dataset within the RBD, N501Y, P681H, K417T, E484K, L452R, T478K have been reported to contribute to a differential binding affinity to the ACE2 receptor and alter pathogenicity, immunogenicity and immune escape 52 . In this study, our genomic surveillance setup initiative provided the opportunity to explore the introduction dynamics of the most frequent VOCs in Guinea, namely Delta/B.1.617.2 and Delta/AY.37, as well as Omicron/BA.1.1 and Omicron/BA.1.15. Both were identified as prominent lineages during the second and third waves, which coincidently corresponded to a period of rather low vaccination coverage (4%) in the country 53 . Our findings suggested that both Delta/B.1.617.2 and Delta/AY.37 were introduced into Guinea from Western Africa countries. Introduction timelines are aligned to their respective estimated global circulation. The Delta/B.1.617.2 emerged in October 2020 in India and several sub-lineages circulated prior to its global expansion by March 14 , 21 , 54 , 55 . Eastern and Middle Africa were inferred as the likely source of transmission events of respectively Omicron/BA.1.1 and /BA1.15.1 into Guinea only a few weeks after their estimated emergence in November 2021 in Southern Africa 10 , 21 , 56 . This is in line with the regional and global spreading of Omicron/BA.1’s reaching more than 30 countries by early December 2021 and further driving virus circulation globally, coinciding with the global relaxation of non-pharmaceutical interventions and no or low vaccination coverage 19 , 57 – 59 . Lockdown or other restrictive measures in Guinea have been previously described and support variant dynamics 22 , 53 , as well as global mobility in rapidly spreading variants 59 . Global and regional epidemic patterns may vary due to regional particularities, such as restrictions of mobility, waning of immunity, lineage competition and recombination, or high transmission rate, all of which facilitate the emergence of new variants and justify the need to have such genomic capacities in country 21 , 48 . Omicron thus rapidly displaced Delta on the global scale which was also facilitated by its enhanced immune evasion in the context of vaccination, convalescent sera and antibody therapy 60 . Our findings align with the reported enhanced transmissibility of Omicron, as compared to other variants circulating at the time, as our estimates indicate its introduction in Guinea within a few weeks after its first detection in the middle of November 10 , 60 . It is however important to interpret these results in the context of confounding factors pertaining to sampling design (e.g. lack of genomic surveillance data) which in this case represent the most notable study limitation. Our work shows that setting up a locally-run sequencing hub during a health emergency is feasible with robust financial support, staff dedication, substantial logistics and long-term partnership. Variant dynamics during the four pandemic waves could be drawn in addition to reliable inference of multiple early introductions of Delta and Omicron in Guinea. The established local sequencing capacity ultimately provided the Guinean Ministry of Health with timely alerts about variant circulation and also stimulated South-South collaboration where disparities in diagnostic and sequencing capacity are still large. Closing these gaps is essential for regional and global genomic surveillance, as many LMIC in both the Old and the New World are at higher risk of zoonotic outbreaks. Activities are ongoing to further enhance genomic surveillance preparedness in Guinea in the event of future epidemics and pandemics. We wish our work to stimulate similar future initiatives in building resilient laboratory systems and advocate for sustained investments. Abbreviations Abbreviation Terminology AI Artificial Intelligence ANSS Agence de Sécurité Sanitaire BNITM Bernhard-Nocht-Institute for Tropical Medicine BSSVS Bayesian Stochastic Search Variable Selection cDNA Complementary DNA CNERS Comité National d'Ethique pour la Recherche en Santé (National Ethics Committee of Guinea) COVID-19 Corona Virus Disease 2019 CRV-LFHVG “Centre de Recherche en Virologie, Laboratoire des Fièvres Hémorragiques Virales de Guinée” Ct Cycle threshold DZIF Deutsches Zentrum für Infektionsforschung (German Center for Infection Research) EMLab European Mobile Laboratory ESS Effective Sample Size Gb Gigabit GISAID Global Initiative on Sharing All Influenza Data GOARN Global Outbreak Alert and Response Network HKY Hasegawa, Kishino, Yano INSP Institut National de Santé Publique ISTH Irrua Specialist Teaching Hospital IQR Inter-Quartile Range LFHV-GKD Laboratoire des Fièvres Hémorragiques Virales de Gueckédou LFVH-HRNZE Laboratoire des Fièvres Hémorragiques Virales de l’Hôpital Régional de N’Zérékoré LMIC Low- and middle-income countries MCC Maximum Clade Credibility µl Micro Liter n Sample Size ng Nano Gram ONT Oxford Nanopore Technologies RBD Receptor Binding Domain PCR Polymerase Chain Reaction RNA Ribonucleic Acid RT-PCR Real time reverse transcription polymerase chain reaction S Spike SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2 SH-aLRT Shimodaira-Hasegawa-like Approximate Likelihood Ratio Test SOPs Standard Operating Procedures SPP State Posterior Probability UFBoot Ultrafast Bootstrap Approximation VOCs Variants of Concern VOIs Variants of Interest VUMs Variants under Monitoring WHO World Health Organization Declarations Ethics approval and consent to participate Not applicable. As the analyzed data were generated during routine clinical practice as part of surveillance, written informed consent (signature or fingerprint) was not obtained from individuals. The Ethics Committee “Comité National d'Ethique pour la Recherche en Santé (CNERS)” of Guinea approved (approval number: 165/CNERS/24) an exemption from obtaining individual consent in accordance with paragraph 32 of the Declaration of Helsinki, as only anonymized data was used for this research. Quote: “Declaration of Helsinki - Ethical Principles for Medical Research Involving Human Subjects – 32: For medical research using identifiable human material or data, such as research on material or data contained in biobanks or similar repositories, physicians must obtain informed consent for their collection, storage, and/or reuse. There may be exceptional situations where consent would be impossible or impractical to obtain for such research. In such situations, the research may only be conducted after review and approval by a research ethics committee." This work is part of the Nagoya permit number 006/2023/PN. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The work was supported by the German Federal Ministry of Health through support of the WHO Collaborating Centre for Arboviruses and Hemorrhagic Fever Viruses at the Bernhard-Nocht-Institute for Tropical Medicine (agreement ZMV I1-2517WHO005), the Global Health Protection Program (GHPP, agreements ZMV I1-2517GHP-704, ZMVI1-2519GHP704, and ZMI1-2521GHP921 until end of 2022, and from 2023 agreements ZMI5-2523GHP006 and ZMI5-2523GHP008), the COVID-19 surge fund (BMG ZMVI1-2520COR001), and the Research and Innovation Programme of the European Union under H2020 grant agreement n°871029-EVA-GLOBAL. The BNITM is a member of the German Center for Infection Research (DZIF, partner site Hamburg–Lübeck–Borstel–Riems, Hamburg, Germany) and all works performed in this study have been supported by DZIF. The European Mobile Laboratory (EMLab), coordinated by the BNITM, is a technical partner of the WHO Global Outbreak Alert and Response Network (GOARN) and part of the deployments of EMLab to Guinea were coordinated and supported by the GOARN Operational Support Team at WHO/HQ. P.L. acknowledges support from the Research Foundation -- Flanders (“Fonds voor Wetenschappelijk Onderzoek – Vlaanderen”, G066215N, G0D5117N and G0B9317N) and through the Arctic Network receiving funding from the Wellcome Trust through project 206298/Z/17/Z. L.E.K. acknowledges support from the Research Foundation -- Flanders (“Fonds voor Wetenschappelijk Onderzoek – Vlaanderen”, 12X9222N). EG-B acknowledges funding from EU Horizon 2020 grants MOOD (H2020–347 874850). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Authors’ contributions Conceived and designed the study: N.F.M., S.G., L.E.K., S.B., So.D. Performed laboratory diagnostics: K.I.A, E.V.N., J.C., G.A., A.R., J.H., M.H., S.R., S.L.M., E.K., M.C., B.S., N.I., S.M., H.S., B.E.P., J.M., Y.S., K.K., M.B.K., B.S. Developed and or performed laboratory trainings: K.I., E.V.N., G.A., J.H., M.H., S.R., S.M., H.S., J.G.D.B., B.E.P., L.N.C., E.M., L.D.C., J.M., B.S., L.E.K. Performed sequencing: K.I., E.V.N., J.C., G.A., J.H., M.H., S.R., E.E., S.L.M, E.K., M.C., B.S., N.I., J.G.D.B., L.N.C., E.M., L.D.C., B.S. Performed sequence validation: E.G.B., K.I., S.R., L.E.K., So.D. Bioinformatics setup and support: K.I., E.V.N., G.A., A.R., J.H., M.H., S.R., E.E., L.E.K. Formal data collection and analysis: N.F.M., E.G.B., K.I., E.V.N., J.C., G.A., S.R., Al.T., L.E.K., S.B., So.D. Formal phylogenetic analysis: E.G.B., K.I., G.A., S.R., P.L., So.D. Project implementation: N.F.M., K.I., E.V.N., J.C., G.A., E.E., C.E., S.O., F.R.K., Y.S., S.G., A.C., B.S., L.E.K., S.B., So.D. Logistics for project implementation: E.V.N., G.A., J.H., M.H., S.R., S.M., H.S., J.G.D.B., B.E.P., L.N.C., E.M., L.D.C., J.M., An.T., C.J., M.P., B.B.Z., K.K., M.B.K., G.L., M.F.J.M.K., G.A.K.Z., Se.D. Funding acquisition: N.F.M., S.G., So.D. Wrote the manuscript: N.F.M., E.G.B., K.I., E.V.N., Al.T., L.E.K., S.B. & So.D. Edited the manuscript: all authors. All authors read and approved the contents of the manuscript. Acknowledgements We would like to thank all those who contributed to SARS-CoV-2 diagnostics activities and in-country surveillance efforts including all EMLab experts who deployed, the Ministry of Health of Guinea and the “ Agence de Sécurité Sanitaire (ANSS)”. We are thankful to the WHO Country Office of Guinea which supported and facilitated EMLab deployment of experts under the WHO/GOARN umbrella. We gratefully acknowledge all data contributors, i.e., the authors and their originating laboratories responsible for obtaining the specimens, and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. The BNITM acknowledges punctual technical assistance from Adam Koletis. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used the ChatGPT / free version in order to edit some sentences. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Funding German Ministry of Health, European Union, German Center for Infection Research, Research Foundation – Flanders, and Wellcome Trust. References WHO COVID-19 dashboard. World Health Organization Data | WHO Health Emergencies Programme (2025). Wu, F. et al. 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Solid black triangles indicate official reports shared with the Ministry of Health after SARS-CoV-2 genome recovery increased to \u0026gt;90% allowing for variant identification and GISAID data deposition.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8530368/v1/219e4a61072c152300e720dd.png"},{"id":100900566,"identity":"cbf9ce1e-ff97-4f7b-a87a-afc79cd3c984","added_by":"auto","created_at":"2026-01-22 14:51:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":387650,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Lineage diversity in the study dataset, July 2020 to July 2022 and (B) sequencing output representing the number of high coverage (\u0026gt;90%) SARS-CoV-2 genomes generated per origin of samples.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8530368/v1/8294010066fb0b650eecc69b.png"},{"id":100900548,"identity":"ca656033-75c4-40e9-9463-0b9aeca7708e","added_by":"auto","created_at":"2026-01-22 14:51:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":998385,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum-likelihood phylogeny and lineages of the 238 sequences generated in this study together with 3400 genomes representative of six global regions. These sequences, along with those from Guinea and the study dataset are identified by the assigned colors in the tree annotation. Lineages identified in our dataset are color-coded on tree branches and identified in the legend.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8530368/v1/4c7fbfff871c9ee6183b354d.png"},{"id":100900549,"identity":"d57fa762-11f7-4cd7-b4b5-e6e14e7334b1","added_by":"auto","created_at":"2026-01-22 14:51:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":923137,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Maximum-likelihood phylogeny of the Delta variants for 4557 sequences representing global and regional diversity of variants sampled (colored circles) in Guinea in 2021 and used to identify subtrees clustering the lineages B.1.617.2 and AY.37. (B) Bayesian discrete phylogeographic reconstruction (asymmetric discrete state) of genomic clusters, summarized in Maximum clade credibility trees for AY.37 and (C) B.1.617.2. Branch colors represent regions of interest in the reconstruction.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8530368/v1/454296601f9aec13dea31b05.png"},{"id":100950491,"identity":"d6cb241a-2973-44f2-b3ae-13fd7ca12f8a","added_by":"auto","created_at":"2026-01-23 07:08:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1014510,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Maximum-likelihood phylogeny of Omicron variants for 2885 sequences representing global and regional diversity of variants sampled (colored circles) in Guinea in 2022 and used to identify subtrees clustering the lineages BA.1.15.1 and BA.1.1. (B) Bayesian discrete phylogeographic reconstruction (asymmetric discrete state) of genomic clusters, summarized in Maximum clade credibility tree for BA.1.15.1 and for (C) BA.1.1. Branch colors represent regions of interest in the reconstruction.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8530368/v1/76247e125082e188aca853de.png"},{"id":106343734,"identity":"eb3f8281-6384-4336-a348-df2447ccdf6a","added_by":"auto","created_at":"2026-04-07 16:08:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4564698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8530368/v1/e1f833ee-9fe2-470f-b22a-d348c776bff2.pdf"},{"id":100949798,"identity":"53e86cb9-00a8-4cd4-baed-0d69b613dabd","added_by":"auto","created_at":"2026-01-23 07:05:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5286935,"visible":true,"origin":"","legend":"","description":"","filename":"Covid19GINBMCSupp16.12.25.docx","url":"https://assets-eu.researchsquare.com/files/rs-8530368/v1/e6ca724bc9d49cc22eb00d78.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Two years of genomic surveillance capacity development in Guinea: an operational roadmap for local implementation in low-income countries and tracking of SARS-CoV-2 circulation dynamics","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSince the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019, and, as of December 2025 more than 778\u0026nbsp;million cases of coronavirus disease 2019 (COVID-19) have been diagnosed worldwide causing more than seven million deaths and substantial economic losses\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn late 2020, SARS-CoV-2 variants emerged carrying a large number of mutations and or deletions, raising concerns about their impact on global public health. As a result, these variants were classified into categories designated as Variants of Concern (VOCs), Variants of Interest (VOIs) and Variants under Monitoring (VUMs)\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The spread of multiple variants around the world highlighted the critical importance of real-time access to genomic surveillance data for tracking variant emergence and guiding public health efforts globally, including the adjustment of molecular tools, treatments, and vaccines\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In particular, it allowed for the characterization of mutations potentially driving changes in transmissibility, disease severity, and immune escape\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. During the pandemic, Alpha (20I; B.1.1.7), Beta (20H; B.1.351), Gamma (20J; P.1), Delta (21A, 21I and 21J; B.1.617.2 and AY.37), and Omicron (21K or BA.1 and 21L or BA.2; B.1.1.529) variants were classified as VOCs \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. A few VOCs were first reported on the African continent including Beta and Omicron in South Africa\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, as well as the VOIs Eta (21D; B.1.525) in Nigeria\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The timely detections of Eta and Omicron variants in South Africa prior to their global spread are two of the many examples highlighting the beneficial impact of locally run sequencing capacities in supporting pandemic control efforts \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe availability of locally operated diagnostic and genomic surveillance facilities during the COVID-19 pandemic exacerbated global disparities in diagnostic capacity, with investment funds primarily bolstering infrastructure development in high-income countries\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Specifically, Brito and colleagues showed that 78% of high-income countries sequenced\u0026thinsp;\u0026gt;\u0026thinsp;0,5% of their COVID-19 cases compared to only 42% of low- and middle-income countries (LMICs) meeting that threshold in the pandemic\u0026rsquo;s first two years\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In line with this, the World Health Organization (WHO) advocated for a ten year strategy to enhance global genomic surveillance, aiming to promptly identify pathogens as part of epidemic and pandemic preparedness\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The African continent saw the establishment of multiple local sequencing facilities, improving variant monitoring and enabling the characterization of pandemic waves\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This led to the identification of four successive waves in West Africa between March 2020 and March 2022, with the first wave peaking in July 2020, the second in January-February 2021, the third in August 2021 and the fourth in January 2022, each of them driven by distinct variant circulation dynamics\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGuinea is a low-income West African country and since the first detection of SARS-CoV-2 in the country on 12 March 2020, Guinea has reported a total of 38582 cases and 468 deaths (case fatality rate (CFR) of 1,2%; as of November 2025) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Retrospective sequencing has provided insights into the circulation of variants in Guinea during the two first waves of the pandemic (i.e., 2020 and early 2021) by analyzing 136 and 19 consensus genomes respectively \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Consistent with the broad West African lineage landscape, lineages 20A and 20B were predominant in Guinea in 2020, whereas the VOC Alpha (20I) and VOI Eta (21D) were the major lineages present during the second wave in early 2021\u003csup\u003e19,22,23\u003c/sup\u003e. Later, recombinant XBB lineage descending from BA.2 variants would be detected in Guinea in the second half of 2022 \u003csup\u003e24\u003c/sup\u003e, shortly before the XBB became dominant in the eastern hemisphere \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To complement the regional sequencing efforts of Guinea and improve our understanding of in-country variant diversity and dynamics, we established a local nanopore genomic surveillance unit during the pandemic and analyzed the genomic epidemiology of locally generated SARS-CoV-2 genomes. The benefit of such a local setup at the \u003cem\u003eCentre de Recherche en Virologie\u003c/em\u003e, \u003cem\u003eLaboratoire des Fi\u0026egrave;vres H\u0026eacute;morragiques Virales de Guin\u0026eacute;e\u003c/em\u003e (CRV-LFHVG), Conakry, was previously highlighted during the Ebola virus disease resurgence and the Marburg virus detection in Guinea \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The feasibility of deploying and implementing nanopore technology has also been detailed in our previous work \u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Here, we underscore the local, regional and global impact of locally operated sequencing platforms and bioinformatic tools in supporting genomic surveillance efforts set up during a public health emergency. This study highlights the challenges faced during in-country implementation and emphasizes the collaborative nature of the work. In addition to providing a crucial operational roadmap for future initiatives aimed at establishing local nanopore sequencing units in resource-limited settings, we use genomic epidemiology to deepen our understanding of SARS-CoV-2 transmission patterns in West-Africa.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003ch2\u003eEthical approval and Nagoya permit\u003c/h2\u003e\n\u003cp\u003eThis descriptive research, using anonymized diagnostics surveillance data, has been approved by the National Ethics Committee of Guinea (CNERS) under the number 165/CNERS/24. This work is part of the Nagoya permit number 006/2023/PN.\u003c/p\u003e\n\u003ch2\u003eDefinition of the study dataset\u003c/h2\u003e\n\u003cp\u003eData were included in the study dataset if they were obtained from samples that had been sequenced at CRV-LFHVG and yielded a genomic recovery fitting the threshold for GISAID submission (50%). The samples had various origins: (i) they directly originated from CRV-LFHVG routine diagnostics activities as part of prospective or retrospective sequencing activities (total of 199 samples with GISAID-submitted sequences generated, Supplemental Table S1), or (ii) they originated from other laboratories in Guinea including \u003cem\u003eLaboratoire des Fi\u0026egrave;vres H\u0026eacute;morragiques Virales de l\u0026rsquo;H\u0026ocirc;pital R\u0026eacute;gional de N\u0026rsquo;Z\u0026eacute;r\u0026eacute;kor\u0026eacute; \u003c/em\u003e(LFHV-HRNZE, N\u0026rsquo;Z\u0026eacute;r\u0026eacute;kor\u0026eacute;) and the \u003cem\u003eInstitut National de Sant\u0026eacute; Publique\u003c/em\u003e (INSP) which shared their materials for the retrospective sequencing service offered by CRV-LFHVG with a total of 34 RNAs from LFHV-HRNZE and five from INSP. In total, 238 GISAID-submitted sequences have been generated by the CRV-LFHVG laboratory and used for phylogenetic analysis (Table 1). \u003c/p\u003e\n\u003ch2\u003eSamples, viral RNA extraction and SARS-CoV-2 diagnostics at CRV-LFHVG\u003c/h2\u003e\n\u003cp\u003eNasopharyngeal swabs collected as part of the in-country surveillance system for COVID-19 were sent to CRV-LFHVG for real-time reverse transcription PCR (RT-PCR) processing as part of routine diagnostic activities. Various kits for the inactivation of samples, extraction of RNA, and RT-PCR were used during the pandemic depending on what was available from donations that were made, kit availability and supply from manufacturers. All kits were used according to manufacturers\u0026rsquo; instructions. Extraction kits included (i) DaAn Gene nucleic acid extraction kit (DaAnGene Co Ltd., China), (ii) QIAamp Viral RNA Mini Kit (Qiagen, Germany), (iii) Quick-RNA Viral Kit (Zymo Research, United States) and (iv) AmpliSens\u0026reg; Ribo-prep (InterLabService, Russia). The RT-PCR assays included (i) Fosun COVID-19 RT-PCR detection Kit (Fosun Diagnostics, China), (ii) Novel Coronavirus (2019-nCoV) Nucleic Acid Diagnostic Kit (Sansure Biotech Inc, China), (iii) Detection Kit for 2019-nCoV (PCR-Fluorescence) (DaAnGene Co Ltd., China), (iv) RealStar\u0026reg; SARS-CoV-2 RT-PCR Kit 1.0 (altona Diagnostics, Germany), and (v) AmpliSens\u0026reg; SARS-Coronavirus (InterLabService, Russia). Two real-time thermal cyclers platforms were used including the Rotor Gene Q\u0026reg; (Qiagen, Germany) and BioRad CFX96\u0026trade; (BioRad, Germany). Leftover RNAs after RT-PCR setup were kept at 4\u0026deg;C until results were validated according to manufacturers\u0026rsquo; instruction algorithms. As part of the real time genomic monitoring of variants from September 2021 to July 2022, RNAs with a SARS-CoV-2 positive RT-PCR result were selected and transferred to the sequencing laboratory for further processing with sequencing. The retrospective sequencing activities were performed using stored leftover swab aliquots (-20\u0026deg;C) which were collected during the period of July 2020 to September 2021. The lack of material stored prior to July 2020 did not allow for an earlier retrospective testing. For the retrospective testing and sequencing work, RNAs were extracted with the available extraction kits and RT-PCR analysis was re-run with the available assays to verify the cycle threshold (Ct) value before proceeding with sequencing. Overall, all SARS-CoV-2 positive RNAs used for variant tracking covered the period from July 2020 to July 2022. \u003c/p\u003e\n\u003ch2\u003eAmplicon preparation, MinION library preparation and next generation nanopore sequencing at CRV-LFHVG\u003c/h2\u003e\n\u003cp\u003eLeftover RNA extracts used for next generation sequencing originated from either prospective activity (i.e. routine diagnostics) or retrospective testing. Samples with a cycle threshold (Ct) \u0026lt;30 were selected and processed for amplicon-based sequencing using Midnight RT PCR Expansion (EXP-MRT001, Oxford Nanopore Technologies (ONT), United Kingdom). Sequencing experimental setup included: (i) inclusion of the negative RNA extraction controls corresponding to each of the RNA batches to be tested, (ii) inclusion of one sequencing negative control per sequencing run, (iii) doubling the volumes of some steps to minimize pipetting errors, (iv) pooling of RNA batches with a range of maximum five Ct values (e.g., samples from Ct 20 to Ct 25 were pooled in one sequencing run), (v) the pooling of a maximum of 24 samples per flow cell. Briefly, RNA was directly used for cDNA synthesis using reagents of the Midnight RT PCR Expansion and the cDNA generated was used as template for the multiplex PCR in two reaction pools. The resulting amplicons from the two PCR pools were combined and processed as per protocol for barcoding and multiplexing using the Rapid Barcoding Kit (SQK-RBK110\u0026middot;96). For the preparation of less than 11 samples, each sample was prepared in replicates to achieve the library concentration required for sequencing. One unique barcode was used per sample. The pooled, barcoded amplicons were quantified using the Qubit Fluorometer (Thermo Fisher Scientific) aiming for a minimum recovery of 600 to 800 ng in a volume of 11 \u0026micro;l. Libraries were loaded onto the R9.4.1 Flow Cells (FLO-MIN106D, ONT) and run on the Mk1C device (ONT) using the MinKNOW version 21.02.2 with fast5 set to 1000 reads per file and live basecalling disabled. Sequencing runs were stopped after ~24hr and fast5 files were transferred on a laptop for basecalling and demultiplexing \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eBioinformatics, consensus generation, lineage identification, data curation and sharing during the pandemic at CRV-LFHVG\u003c/h2\u003e\n\u003cp\u003eBasecalling and demultiplexing were performed using Guppy version 5.0.16 to obtain fastq files. The following parameters were used: high accuracy basecalling, mid-strand barcodes on, and override minimum mid barcoding score on and set to 50. A modem was configured to ensure optimal internet connection for the analysis of fastq files (using approximately 5 Gb of data). The epi2me\u0026trade; ARTIC SARS-CoV-2 pipeline (wf-artic workflow) with different versions available at each time of analysis was used for consensus generation from fastq files according to the developers\u0026rsquo; instructions. Output consensus files were checked using CLC Main Workbench (Qiagen, Germany) and lineages further identified using the Nextclade desktop app (https://clades.nextstrain.org/) and Pangolin\u003csup\u003e8,31\u003c/sup\u003e. A total of 238 consensus sequences have been generated at CRV-LFHVG, curated following GISAID\u0026rsquo;s algorithm and submitted in different batches to GISAID throughout the pandemic\u003csup\u003e32\u003c/sup\u003e. The related metadata are in the Supplemental File S1.\u003c/p\u003e\n\u003ch2\u003ePhylogenetic analyses\u003c/h2\u003e\n\u003cp\u003eFor the analysis performed here, the 238 genomes were assigned to lineages using Phylogenetic Assignment of Named Global Outbreak Lineages (pangolin; v4.3)\u003csup\u003e33\u003c/sup\u003e and to clades using Nextclade (v3.8.2)\u003csup\u003e31\u003c/sup\u003e. To provide a global context, high-quality complete genome sequences reported from Guinea and from the six global regions were downloaded from GISAID. For further analysis, global genomes related to the lineage diversity assigned in this study were down-sampled biweekly to cover the time interval explored in this study. A total of 3400 sequences collected between 31 December 2019 and 11 November 2022 were obtained as follows: Africa (n = 442), Asia (n = 556), Europe (n = 657), North America (n = 571), South America (n = 349) and Oceania (n = 212). The whole dataset was assigned using Nextclade 3.8.2 \u003csup\u003e31\u003c/sup\u003e, and the alignment performed using Nextclade CLI (3.7.1) \u003csup\u003e34\u003c/sup\u003e. The trimming and checking of data integrity were done using Aliview \u003csup\u003e35\u003c/sup\u003e. For the former VOC Delta, the database creation and formatting followed the same criteria as those described above totaling 4557 genomes sampled between 7 November 2020 and 1 April 2022 comprising Asia (n = 180), Eastern Africa (1809), Europe (n = 209), Middle Africa (350), North America (n = 212), Northern Africa (403), Oceania (n = 6), South America (n = 5), Southern Africa (5), and Western Africa (818). A similar method was used for the VOC Omicron resulting in 2885 genomes sampled between 5 November 2021 and 23 August 2022, including Asia (n = 187), Central America (12), Eastern Africa (441), Europe (n = 343), Middle Africa (43), North America (n = 375), Northern Africa (59), Oceania (n = 115), South America (n = 172), Southern Africa (792), and Western Africa (237).\u003c/p\u003e\n\u003cp\u003eMaximum likelihood phylogenetic trees for these datasets were constructed using IQTREE2 (v.2.3.4) and a General Time-Reversible (GTR) with empirical base frequencies and gamma-distributed rate heterogeneity across four categories, which was selected as the best fitting substitution model \u003csup\u003e36\u003c/sup\u003e. The branch support was inferred by ultrafast bootstrap approximation (UFBoot) and Shimodaira-Hasegawa-like approximate likelihood ratio test (SH-aLRT) with 1000 replicates. A root-to-tip regression was conducted in TempEst v1.5.3 to explore the relationship between genetic divergence and time, evaluating the degree and pattern of temporal signal in the dataset, and to exclude outliers or identify assembly issues \u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003ePhylogeographic discrete diffusion analysis of selected cluster for the VOCs Delta (AY.37 and B.1.617.2) and Omicron (BA.1.15 and BA.1.1)\u003c/h2\u003e\n\u003cp\u003eA spatiotemporal reconstruction using BEAST v1.10.5 with BEAGLE v4.0.0 was performed using supported subtrees (considering UFBoot and SH-like replicates) clustering with the most frequent lineages identified here to optimize the high-burden computational analysis \u003csup\u003e38,39\u003c/sup\u003e. The substitution model specified was Hasegawa, Kishino, Yano (HKY) nucleotide with gamma-distributed rate variation among sites \u003csup\u003e40\u003c/sup\u003e, combined with a strict molecular clock model (fixed rate = 8 x 10\u003csup\u003e\u0026minus;4\u003c/sup\u003e substitutions per site per year), a non-parametric skygrid coalescent tree model, informed by root height prior distribution \u003csup\u003e41\u003c/sup\u003e. To model the location transition, the asymmetric discrete state diffusion in a Continuous-Time Markov Chain process using the Bayesian stochastic search variable selection (BSSVS) was used to determine routes of significant non-zero migration rates, estimating the posterior inclusion probability (State Posterior Probability or SPP), which provides the supported fluxes by Bayes factor test (Bayes factor \u0026gt;3) \u003csup\u003e42\u003c/sup\u003e. Markov jump counts between locations were extracted using the BEAST tool TreeMarkovJumpHistoryAnalyzer \u003csup\u003e43\u003c/sup\u003e. The Markov chain Monte Carlo analyses were run for 200 million steps sampling every 10000 steps. The effective sample size (ESS) for each parameter was inspected with TRACER v.1.7 and the runs were combined with LogCombiner \u003csup\u003e38,44\u003c/sup\u003e. For each selected cluster (subtree), a maximum clade credibility (MCC) tree was generated from the posterior samples using TreeAnnotator \u003csup\u003e39\u003c/sup\u003e. The resulting trees were visualized and annotated using the \u003cem\u003eggtree\u003c/em\u003e package \u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch2\u003eThe context for capacity development\u003c/h2\u003e\n\u003cp\u003eAs the reference laboratory in Guinea for measles and viral hemorrhagic fevers, including Ebola virus disease and yellow fever, CRV-LFHVG was mandated by the Guinean Ministry of Health to assist with the in-country testing for SARS-CoV-2. Between March 2020 and July 2022, the laboratory tested 38130 samples, of which 5420 were positive for SARS-CoV-2 (Figure 1, Supplemental Table S1). Overall, this accounted for approximately 5,3% of the countrywide number of tests performed during this period (Guinea surveillance data with 725.278 tests, 12 March 2020 to 29 July 2022) and led to the diagnosis of about 14,5% of all confirmed cases (Guinea surveillance data with 37464 confirmed cases, 12 March 2020 to 29 July 2022). Furthermore, the establishment of the genomic capacity accounted for 3,7% (n = 199) of all positive samples (n = 5420) being sequenced (Supplemental Table S1). During the peaks of the pandemic, the number of diagnostics tests reached up to 135 tests per day, which were manually performed by lab staff (i.e. no automated platforms). The deployment of in-country sequencing at CRV-LFHVG in 2021 during the Ebola virus disease resurgence ultimately stimulated the launch of the SARS-CoV-2 genomic surveillance capacity development program, following a request from the \u003cem\u003eAgence de S\u0026eacute;curit\u0026eacute; Sanitaire\u003c/em\u003e (ANSS) of the Guinean Ministry of Health \u003csup\u003e26\u003c/sup\u003e. The BNITM, through its humanitarian aid cluster, the European Mobile Laboratory (EMLab), and with support from the WHO/GOARN and Guinean health authorities, planned for the implementation of a two-year training program aiming to establish an independently-operated nanopore sequencing unit for SARS-CoV-2 in Guinea. \u003c/p\u003e\n\u003ch2\u003eLogistical considerations in developing SARS-CoV-2 nanopore sequencing capabilities\u003c/h2\u003e\n\u003cp\u003eBetween March 2021 and December 2022, a total of ten missions to Guinea were conducted, with 32 staff deployed (i.e., deployment mechanisms through either GOARN/WHO/EMLab or BNITM/EMLab), to support CRV-LFHVG with diagnostics strengthening and implementation of nanopore sequencing for genomic surveillance. This included scaled-up training encompassing both wet lab and bioinformatics components. Expert teams of three to five staff rotating every four to five weeks from March 2021 to June 2021 were deployed (i.e.17 experts). From August to December 2021, the frequency of training decreased, with three missions of two- to three-weeks each and a total of seven specialized staff deployed. In early, mid and end of 2022, three training missions (three- to four-weeks each) with eight experts took place. Having French speaking experts on teams was a key component of ensuring efficient communication and knowledge transfer. During this period, six local staff were trained and two became heads of the sequencing unit. The logistic backbone from Germany to Guinea was substantial, consisting of 20 shipments in 2021 and six in 2022, either via courier or as checked luggage of deployed teams. These supplied the laboratory with reagents, consumables and equipment to set up and sustain diagnostics and sequencing operations. Redesigning of the laboratory organization, rewiring of the infrastructure and installation of additional generators were necessary to prevent contamination risks and support access to uninterrupted power. Implementation of standard operating procedures (SOPs) encompassing laboratory, bioinformatics, data storage and back-up processes ensured smooth implementation of a quality assurance management system for delivery of quality data. The supplemental Figure S1 provides an example of a minimum laboratory setup and minimum necessary equipment. \u003c/p\u003e\n\u003cp\u003eBetween March and August 2021, two amplicon-based sequencing protocols were tested at CRV-LFHVG, namely the ARTIC nCoV-2019 sequencing protocol V3 (ARTIC network and Integrated DNA Technologies GmbH (IDT), Germany) and the NEBNext\u0026reg; ARTIC SARS-CoV-2 Companion Kit (New England Biolabs, United Kingdom). Considering minor optimisations between runs in an attempt to improve genomic recoveries, a total of nine sequencing runs were performed on 57 SARS-CoV-2-positive samples, all of which yielded low genomic recoveries [median of 46,9%, range: 6-93,1%, Inter-Quartile Range (IQR): 25,1-84,9%]. None of these sequences could be submitted to GISAID, as the threshold for submission (50%) was not met. Yet, preliminary findings could still be drawn with the identification of the VOCs Alpha and Delta, as well as the VOI Eta, which were immediately reported in April and August 2021 to the Ministry of Health of Guinea. From October 2021, the use of the ONT Midnight kit (see Materials and Methods) led to the successful retrieval of 238 consensus sequences with a median genomic recovery of 98,1% [range: 90,5-99,4%, IQR: 96,5-99,3%] (Figure 2B). Until April 2022, a total of six variant monitoring reports were shared with the Guinean health authorities before GISAID submission (Figure 1). \u003c/p\u003e\n\u003ch2\u003eBroad impact within and beyond Guinea\u003c/h2\u003e\n\u003cp\u003eBy October 2021, the SARS-CoV-2 sequencing unit at CRV-LFHVG was fully operational, enabling other laboratories in Guinea to send samples for variant identification. This resulted in additional SARS-CoV-2 sequences from LFHV-HRNZE (n = 34) and INSP (n = 5). Furthermore, the unit facilitated transborder collaboration with the Irrua Specialist Teaching Hospital (ISTH) in Edo State, Nigeria, which sought to establish similar capabilities. Using CRV-LFHVG as a hub, six Nigerian scientists participated in two separate 2-week sequencing training sessions from late 2021 to the end of 2022, making the Guinean laboratory a regional training hub where technical knowledge was acquired by experienced trainers who would eventually teach others at their original laboratories (i.e. trainers of trainers).\u003c/p\u003e\n\u003ch2\u003eLineage characterization, July 2020 to July 2022\u003c/h2\u003e\n\u003cp\u003eA total of 238 sequences were generated at CRV-LFHVG and covered the four waves of the pandemic period from July 2020 to July 2022 (Figure 1 and 2B, Supplemental file S1). Most of the samples sequenced originated from the Conakry prefecture (75,6%, n = 180 samples labeled as Conakry, Ratoma, Kaloum, Dixin, Matoto), followed by N\u0026apos;Z\u0026eacute;r\u0026eacute;kor\u0026eacute; (14,4%, n = 34), Forecariah (5%, n = 12) and a minor percentage from various regions throughout the country (5%, n = 12). The genomes were assigned to ten clades (Nextclade classification) or 23 lineages with four VOCs and VOI identified as per WHO label including Omicron, Delta, Alpha and Eta (Supplementary Table S2). During the first wave in 2020, the lineages B.1, B.1.1, B.1.1.1 and B.1.1.318 (clades 20A, 20B and 20D) were predominant, followed by a diversity shift in early 2021 with the appearance of the VOC Alpha B.1.1.7 (20I) and VOI Eta B.1.525 (21D) (Figure 2). From July 2021 to November 2021, the VOC Delta B.1.617.2, AY.34.1, AY.36, AY.37, AY.6 (21J and 21A) was the most identified variant in our dataset. From December 2021 and until July 2022, nearly all sequences were identified as VOI Omicron BA.1, BA.1.1, BA.1.1.1, BA.1.1.14, BA.1.14, BA.1.15.1, BA.1.16, BA.1.18, BA.2 and BA.2.10 (21K and 21L, Supplementary Table S2 and Figure 2A). The mutation annotation provided by Nextclade showed that the Spike (S) protein had the highest number of amino acid substitutions with 52,5% (n = 123) of all analyzed samples, harboring mutations such as S:K417N, S:N440K, S:G446S, S:E484A, S:Q493R, S:G496S, S:Q498R, S:N501Y and S:Y505H, while one mutation, S:L452R, was seen in 31,5% (n = 75) of the genomes. \u003c/p\u003e\n\u003ch2\u003eGlobal phylodynamic analysis\u003c/h2\u003e\n\u003cp\u003eTo place the SARS-CoV-2 genetic diversity identified from our dataset in a global context, we performed a time-calibrated analysis starting with a maximum likelihood phylogenetic tree. A dataset of well-curated, complete and high-quality genomes (n = 3.400) representing the different clades from six regions worldwide were retrieved from GISAID and added to the 238 sequences generated locally (Figure 3). The phylogeny shows well-supported subtrees containing the study genomes, suggesting multiple potential introductions of SARS-CoV-2 in Guinea and also fitting the global distribution of SARS-CoV-2 in the respective period of the pandemic. These subtrees consisted in large clusters of the Delta (third wave) and Omicron (fourth wave) VOCs.\u003c/p\u003e\n\u003cp\u003eTo gain insights into the patterns of introduction and progression within Africa of the two most frequently identified VOCs in this study (Delta and Omicron), we tailored a phylogenetic analysis to examine their diversity and distribution across continental and worldwide representative clades. We then focused on the five subregions of Africa (i.e., Northern, Southern, Eastern, Western and Middle). The VOC Delta was first reported in India in late 2020 and thus 4557 Delta sequences were assembled to study the local (Guinea) and global (Africa) context of the study genomes (Figure 4). The analysis supports two subtrees, one represented by the lineage AY.37 (n = 40, 16,8%) (Figure 4B) and one by B.1.617.2 (n = 25, 10,5%) (Figure 4C). To further explore the introduction and circulation of AY.37 and B.1.617.2, Bayesian discrete phylogeographic analysis was performed to model the spatial transitions between the regions evaluated here and for each subtree. The introduction of the Delta lineage AY.37 was estimated to have occurred from a Western Africa region (SPP = 0,99) seeding into Guinea on 25 May 2021 (95% highest posterior density (HPD): 23 May\u0026ndash;23 July 2021) (Figure 4B). For lineage B.1.617.2, the ancestral location reconstruction also suggested a Western Africa origin (SPP = 0,39) on 4 April 2021 (95% HDP: 6 March\u0026ndash;30 April 2021) (Figure 4C). \u003c/p\u003e\n\u003cp\u003eA similar strategy was used for Omicron which was first detected in South Africa and Botswana in mid-November 2021. A total of 2885 sequences representing all BA.1 lineage and sub-lineages worldwide were assembled to produce a phylogenetic tree (Figure 5). Our analysis revealed multiple introductions for BA.1 and two supported subtrees for lineages BA.1.15.1 (n = 38, 16%) and BA.1.1 (n = 27, 11,3%) (Figure 5A). The phylogeographic inference for BA.1.15.1 dated the introduction into Guinea on 23 November 2021 (95% HDP: 14 November \u0026ndash; 30 November 2021) with a likely source from Middle Africa (SPP = 1) (Figure 5B). For BA.1.1, our analysis suggested an introduction from Eastern Africa (SPP = 1) on 24 November 2021 (95% HDP: 10 November \u0026ndash; 6 December 2021) (Figure 5C).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSince SARS-CoV-2 was first identified, genomic surveillance combined with evolutionary epidemiology has helped untangle the complexity of worldwide variant spread. However, disparities in genomic surveillance capabilities among LMICs relative to high-income countries have been broadly highlighted \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Here, we illustrate \u003cb\u003erelevant achievements\u003c/b\u003e following the establishment of locally run sequencing capacity in Guinea (i.e. no export of samples abroad). Our study offers useful practical lessons learned during a long-term capacity development program, including a model of genomic surveillance for tracking the spatio-temporal dynamics of pathogen variants during an ongoing public health crisis.\u003c/p\u003e \u003cp\u003e The successful establishment of genomic surveillance capacity led to the generation of 238 consensus sequences obtained locally by prospective and retrospective sequencing of diagnostic samples collected between July 2020 and July 2022. Overall, these sequences contributed to 27,8% of sequences collected in Guinea during this time frame and were deposited on GISAID (n\u0026thinsp;=\u0026thinsp;856, March 2020-July 2022). At the laboratory level, this work accounted for 3,7% of SARS-CoV-2 positive samples being sequenced (Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and for 0,64% of all confirmed cases at national level (n\u0026thinsp;=\u0026thinsp;37464; 29 July 2022). This outcome positions Guinea slightly above the sequencing intensity threshold of 0,5% suggested by Brito and colleagues as a potential marker for genomic sequencing ability although a turnaround time of \u0026lt;\u0026thinsp;21 days from sample collection to GISAID submission was also suggested as a proxy for genomic surveillance capability \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The median of 172,5 days [range: 37\u0026ndash;563; IQR: 116\u0026ndash;317, n\u0026thinsp;=\u0026thinsp;238 sequences] observed here is likely biased by a combination of factors such as late implementation during the pandemic (October 2021), substantial yield of retrospective sequencing, time allocation for training, data curation and sharing to health authorities prior to GISAID submission. Such bottlenecks should definitely be considered in improving future preparedness activities for timely sharing.\u003c/p\u003e \u003cp\u003ePrevious expertise of the local laboratory workforce in human diagnostics and molecular biology likely facilitated the successful setup and provided access to good quality genetic material for sequencing. Notably, some aspects should be considered for sustained impact including troubleshooting training, access to English courses if applicable, and implementation of robust workflows with a quality assurance management system for laboratory process tracking, data validation and curation to deliver high quality data. Furthermore, delivery of broad bioinformatics training ranging from computer programming courses, to computer setup, data analysis, data storage and backup was key to local workforce development and sustainable knowledge transfer. These aspects were implemented by the large number of experts deployed to support diagnostics refreshers, sequencing trainings and knowledge transfer. The online availability of user-friendly tools, such as Nextstrain or Pangolin \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, significantly facilitated SARS-CoV-2 sequencing training with data analysis, variant identification and phylogenetic investigations \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Yet, prior knowledge of bioinformatics still remains a prerequisite for staff and the scarcity of such experts remains a major obstacle to maintaining capabilities. Mobilization of funds to foster bioinformatics capacity in LMICs must become a priority \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Operational challenges in such settings are considerable, ranging from import/export procedures, security and political context, laboratory location and infrastructure to chosen sequencing technology, access to the power grid and running water, data access and dissemination rights, or availability to in-country or within-continent supply chains (temperature-controlled) for reagents. These aspects must be addressed to not only build robust genomic surveillance capabilities but also to strengthen resilient and self-sufficient public health systems in such settings \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The 26 shipments from Germany to Guinea in this study are just one example of such challenges and underline the urgent need for equitable market access, as well as country leadership and engagement in promoting such access \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother fundamental aspect was the fruitful South-South cooperation with Nigeria, which expedited the establishment of a similar capacity at ISTH in April 2022. Guinea as a training hub was opportune for improving on-site know-how, best practices and staff expertise in operating such a laboratory in an environment reflecting best the daily reality in LMICs. The cooperation showcased the benefits of establishing a regional training hub where African scientists were trained for subsequently disseminating acquired knowledge in their original labs, thus making this capacity building model a sustainable one.\u003c/p\u003e \u003cp\u003eThe distribution of the 238 SARS-CoV-2 sequences is similar with previously reported dynamics during the early waves in Guinea and West-Africa \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Initially dominated by 20A and 20B clades, the Alpha VOCs and the Eta VOI subsequently dominated the epidemiological landscape during the second wave in Guinea. Our dataset further shows that the VOCs Delta and Omicron dominated the third and fourth waves in Guinea with a predominance of Delta/AY.37 and Omicron/BA.2, which had not yet been reported in Guinea at the time. Our results may be biased by sampling limited to certain districts, sample selection based on low Ct values and timeline of operational readiness affecting overall lineage frequencies and leading to the VOCs Delta and Omicron dominating the dataset. Yet, identification of virus lineages and understanding their diffusion across time allows for mutation screening to identify potential sites located primarily in the receptor binding domain (RBD) of the viral Spike protein which are linked with immune escape and reduced vaccine efficacy \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Thus, amino acid changes identified in our dataset within the RBD, N501Y, P681H, K417T, E484K, L452R, T478K have been reported to contribute to a differential binding affinity to the ACE2 receptor and alter pathogenicity, immunogenicity and immune escape \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, our genomic surveillance setup initiative provided the opportunity to explore the introduction dynamics of the most frequent VOCs in Guinea, namely Delta/B.1.617.2 and Delta/AY.37, as well as Omicron/BA.1.1 and Omicron/BA.1.15. Both were identified as prominent lineages during the second and third waves, which coincidently corresponded to a period of rather low vaccination coverage (4%) in the country \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Our findings suggested that both Delta/B.1.617.2 and Delta/AY.37 were introduced into Guinea from Western Africa countries. Introduction timelines are aligned to their respective estimated global circulation. The Delta/B.1.617.2 emerged in October 2020 in India and several sub-lineages circulated prior to its global expansion by March \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Eastern and Middle Africa were inferred as the likely source of transmission events of respectively Omicron/BA.1.1 and /BA1.15.1 into Guinea only a few weeks after their estimated emergence in November 2021 in Southern Africa \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. This is in line with the regional and global spreading of Omicron/BA.1\u0026rsquo;s reaching more than 30 countries by early December 2021 and further driving virus circulation globally, coinciding with the global relaxation of non-pharmaceutical interventions and no or low vaccination coverage \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Lockdown or other restrictive measures in Guinea have been previously described and support variant dynamics \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, as well as global mobility in rapidly spreading variants \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Global and regional epidemic patterns may vary due to regional particularities, such as restrictions of mobility, waning of immunity, lineage competition and recombination, or high transmission rate, all of which facilitate the emergence of new variants and justify the need to have such genomic capacities in country \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Omicron thus rapidly displaced Delta on the global scale which was also facilitated by its enhanced immune evasion in the context of vaccination, convalescent sera and antibody therapy \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Our findings align with the reported enhanced transmissibility of Omicron, as compared to other variants circulating at the time, as our estimates indicate its introduction in Guinea within a few weeks after its first detection in the middle of November \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. It is however important to interpret these results in the context of confounding factors pertaining to sampling design (e.g. lack of genomic surveillance data) which in this case represent the most notable study limitation.\u003c/p\u003e \u003cp\u003eOur work shows that setting up a locally-run sequencing hub during a health emergency is feasible with robust financial support, staff dedication, substantial logistics and long-term partnership. Variant dynamics during the four pandemic waves could be drawn in addition to reliable inference of multiple early introductions of Delta and Omicron in Guinea. The established local sequencing capacity ultimately provided the Guinean Ministry of Health with timely alerts about variant circulation and also stimulated South-South collaboration where disparities in diagnostic and sequencing capacity are still large. Closing these gaps is essential for regional and global genomic surveillance, as many LMIC in both the Old and the New World are at higher risk of zoonotic outbreaks. Activities are ongoing to further enhance genomic surveillance preparedness in Guinea in the event of future epidemics and pandemics. We wish our work to stimulate similar future initiatives in building resilient laboratory systems and advocate for sustained investments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerminology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eANSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eAgence de S\u0026eacute;curit\u0026eacute; Sanitaire\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eBNITM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eBernhard-Nocht-Institute for Tropical Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eBSSVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eBayesian Stochastic Search Variable Selection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003ecDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eComplementary DNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCNERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eComit\u0026eacute; National d\u0026apos;Ethique pour la Recherche en Sant\u0026eacute; (National Ethics Committee of Guinea)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eCorona Virus Disease 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCRV-LFHVG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026ldquo;Centre de Recherche en Virologie, Laboratoire des Fi\u0026egrave;vres H\u0026eacute;morragiques Virales de Guin\u0026eacute;e\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eCycle threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eDZIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eDeutsches Zentrum f\u0026uuml;r Infektionsforschung (German Center for Infection Research)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eEMLab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eEuropean Mobile Laboratory\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eESS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eEffective Sample Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eGb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eGigabit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eGISAID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eGlobal Initiative on Sharing All Influenza Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eGOARN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eGlobal Outbreak Alert and Response Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eHKY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eHasegawa, Kishino, Yano\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eINSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eInstitut National de Sant\u0026eacute; Publique\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eISTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eIrrua Specialist Teaching Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eInter-Quartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eLFHV-GKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eLaboratoire des Fi\u0026egrave;vres H\u0026eacute;morragiques Virales de Gueck\u0026eacute;dou\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eLFVH-HRNZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eLaboratoire des Fi\u0026egrave;vres H\u0026eacute;morragiques Virales de l\u0026rsquo;H\u0026ocirc;pital R\u0026eacute;gional de N\u0026rsquo;Z\u0026eacute;r\u0026eacute;kor\u0026eacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eLMIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eLow- and middle-income countries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eMCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eMaximum Clade Credibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026micro;l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eMicro Liter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eSample Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eng\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eNano Gram\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eONT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eOxford Nanopore Technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eRBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eReceptor Binding Domain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003ePCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eRibonucleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eRT-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eReal time reverse transcription polymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eSpike\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eSevere Acute Respiratory Syndrome Coronavirus 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSH-aLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eShimodaira-Hasegawa-like Approximate Likelihood Ratio Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSOPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eStandard Operating Procedures\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eState Posterior Probability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eUFBoot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eUltrafast Bootstrap Approximation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eVOCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eVariants of Concern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eVOIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eVariants of Interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eVUMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eVariants under Monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the analyzed data were generated during routine clinical practice as part of surveillance, written informed consent (signature or fingerprint) was not obtained from individuals. The Ethics Committee \u0026ldquo;Comit\u0026eacute; National d\u0026apos;Ethique pour la Recherche en Sant\u0026eacute; (CNERS)\u0026rdquo; of Guinea approved (approval number: 165/CNERS/24) an exemption from obtaining individual consent in accordance with paragraph 32 of the Declaration of Helsinki, as only anonymized data was used for this research. Quote: \u0026ldquo;Declaration of Helsinki - Ethical Principles for Medical Research Involving Human Subjects \u0026ndash; 32: For medical research using identifiable human material or data, such as research on material or data contained in biobanks or similar repositories, physicians must obtain informed consent for their collection, storage, and/or reuse. There may be exceptional situations where consent would be impossible or impractical to obtain for such research. In such situations, the research may only be conducted after review and approval by a research ethics committee.\u0026quot;\u003c/p\u003e\n\u003cp\u003eThis work is part of the Nagoya permit number 006/2023/PN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by the German Federal Ministry of Health through support of the WHO Collaborating Centre for Arboviruses and Hemorrhagic Fever Viruses at the Bernhard-Nocht-Institute for Tropical Medicine (agreement ZMV I1-2517WHO005), the Global Health Protection Program (GHPP, agreements ZMV I1-2517GHP-704, ZMVI1-2519GHP704, and ZMI1-2521GHP921 until end of 2022, and from 2023 agreements ZMI5-2523GHP006 and ZMI5-2523GHP008), the COVID-19 surge fund (BMG ZMVI1-2520COR001), and the Research and Innovation Programme of the European Union under H2020 grant agreement n\u0026deg;871029-EVA-GLOBAL. The BNITM is a member of the German Center for Infection Research (DZIF, partner site Hamburg\u0026ndash;L\u0026uuml;beck\u0026ndash;Borstel\u0026ndash;Riems, Hamburg, Germany) and all works performed in this study have been supported by DZIF. The European Mobile Laboratory (EMLab), coordinated by the BNITM, is a technical partner of the WHO Global Outbreak Alert and Response Network (GOARN) and part of the deployments of EMLab to Guinea were coordinated and supported by the GOARN Operational Support Team at WHO/HQ. P.L. acknowledges support from the Research Foundation -- Flanders (\u0026ldquo;Fonds voor Wetenschappelijk Onderzoek \u0026ndash; Vlaanderen\u0026rdquo;, G066215N, G0D5117N and G0B9317N) and through the Arctic Network receiving funding from the Wellcome Trust through project 206298/Z/17/Z. L.E.K. acknowledges support from the Research Foundation -- Flanders (\u0026ldquo;Fonds voor Wetenschappelijk Onderzoek \u0026ndash; Vlaanderen\u0026rdquo;, 12X9222N). EG-B acknowledges funding from EU Horizon 2020 grants MOOD (H2020\u0026ndash;347 874850). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the study: N.F.M., S.G., L.E.K., S.B., So.D.\u003c/p\u003e\n\u003cp\u003ePerformed laboratory diagnostics: K.I.A, E.V.N., J.C., G.A., A.R., J.H., M.H., S.R., S.L.M., E.K., M.C., B.S., N.I., S.M., H.S., B.E.P., J.M., Y.S., K.K., M.B.K., B.S.\u003c/p\u003e\n\u003cp\u003eDeveloped and or performed laboratory trainings: K.I., E.V.N., G.A., J.H., M.H., S.R., S.M., H.S., J.G.D.B., B.E.P., L.N.C., E.M., L.D.C., J.M., B.S., L.E.K.\u003c/p\u003e\n\u003cp\u003ePerformed sequencing: K.I., E.V.N., J.C., G.A., J.H., M.H., S.R., E.E., S.L.M, E.K., M.C., B.S., N.I., J.G.D.B., L.N.C., E.M., L.D.C., B.S.\u003c/p\u003e\n\u003cp\u003ePerformed sequence validation: E.G.B., K.I., S.R., L.E.K., So.D.\u003c/p\u003e\n\u003cp\u003eBioinformatics setup and support: K.I., E.V.N., G.A., A.R., J.H., M.H., S.R., E.E., L.E.K.\u003c/p\u003e\n\u003cp\u003eFormal data collection and analysis: N.F.M., E.G.B., K.I., E.V.N., J.C., G.A., S.R., Al.T., L.E.K., S.B., So.D.\u003c/p\u003e\n\u003cp\u003eFormal phylogenetic analysis: E.G.B., K.I., G.A., S.R., P.L., So.D.\u003c/p\u003e\n\u003cp\u003eProject implementation: N.F.M., K.I., E.V.N., J.C., G.A., E.E., C.E., S.O., F.R.K., Y.S., S.G., A.C., B.S., L.E.K., S.B., So.D.\u003c/p\u003e\n\u003cp\u003eLogistics for project implementation: E.V.N., G.A., J.H., M.H., S.R., S.M., H.S., J.G.D.B., B.E.P., L.N.C., E.M., L.D.C., J.M., An.T., C.J., M.P., B.B.Z., K.K., M.B.K., G.L., M.F.J.M.K., G.A.K.Z., Se.D.\u003c/p\u003e\n\u003cp\u003eFunding acquisition: N.F.M., S.G., So.D.\u003c/p\u003e\n\u003cp\u003eWrote the manuscript: N.F.M., E.G.B., K.I., E.V.N., Al.T., L.E.K., S.B. \u0026amp; So.D.\u003c/p\u003e\n\u003cp\u003eEdited the manuscript: all authors.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the contents of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all those who contributed to SARS-CoV-2 diagnostics activities and in-country surveillance efforts including all EMLab experts who deployed, the Ministry of Health of Guinea and the \u0026ldquo;\u003cem\u003eAgence de S\u0026eacute;curit\u0026eacute;\u0026nbsp;\u003c/em\u003eSanitaire (ANSS)\u0026rdquo;. We are thankful to the WHO Country Office of Guinea which supported and facilitated EMLab deployment of experts under the WHO/GOARN umbrella. We gratefully acknowledge all data contributors, i.e., the authors and their originating laboratories responsible for obtaining the specimens, and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. The BNITM acknowledges punctual technical assistance from Adam Koletis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used the ChatGPT / free version in order to edit some sentences. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGerman Ministry of Health, European Union, German Center for Infection Research, Research Foundation \u0026ndash; Flanders, and Wellcome Trust.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO COVID-19 dashboard. \u003cem\u003eWorld Health Organization Data | WHO Health Emergencies Programme\u003c/em\u003e (2025). \u003c/li\u003e\n\u003cli\u003eWu, F. \u003cem\u003eet al.\u003c/em\u003e A new coronavirus associated with human respiratory disease in China. \u003cem\u003eNature \u003c/em\u003e\u003cstrong\u003e579\u003c/strong\u003e, 265\u0026ndash;269 (2020). \u003c/li\u003e\n\u003cli\u003eKupferschmidt, K. 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M. \u003cem\u003eet al.\u003c/em\u003e Genetic diversity of SARS-CoV-2 infections in Ghana from 2020-2021. \u003cem\u003eNat Commun \u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 2494 (2022). \u003c/li\u003e\n\u003cli\u003eTegally, H. \u003cem\u003eet al.\u003c/em\u003e Dispersal patterns and influence of air travel during the global expansion of SARS-CoV-2 variants of concern. \u003cem\u003eCell \u003c/em\u003e\u003cstrong\u003e186\u003c/strong\u003e, 3277-3290 e16 (2023). \u003c/li\u003e\n\u003cli\u003eFan, Y. \u003cem\u003eet al.\u003c/em\u003e SARS-CoV-2 Omicron variant: recent progress and future perspectives. \u003cem\u003eSignal Transduct Target Ther \u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 141 (2022). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8530368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8530368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe COVID-19 pandemic significantly accelerated the development of genomic surveillance capabilities worldwide, though equitable access remains a challenge. On 12 March 2020, Guinea, a low-income country in West Africa, reported its first COVID-19 case; however, no local genomic infrastructure was available at the time. A year later, a long-term mentorship program was initiated to establish a SARS-CoV-2 nanopore sequencing unit at the \u003cem\u003eCentre de Recherche en Virologie, Laboratoire des Fièvres Hémorragiques Virales de Guinée\u003c/em\u003e (CRV-LFHVG) in Conakry, Guinea. Here, we describe the establishment of this capacity and its role in uncovering SARS-CoV-2 circulation dynamics in the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe established a local hub for sequencing training where SARS-CoV-2-positive samples, collected as part of routine diagnostic activities from July 2020 to July 2022, were retrospectively and prospectively sequenced using MinION nanopore. Consensus genomes were generated for variant typing and GISAID-submission. Retrospective phylodynamic analysis was performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy July 2022, the laboratory had generated 238 SARS-CoV-2 consensus sequences with a median genomic recovery of 98.1 % [range: 90.5-99.4], representing 0.64% of the 37,464 confirmed cases reported in the country as of 29 July 2022. These sequences encompassed four waves of infection, with the Delta (21A, 21I and 21J) and Omicron (21K and 21L) variants of concern (VOCs) accounting for 84% of all identified lineages. Notably, phylogeographic reconstructions unveiled introductions of Delta/B.1.617.2 and Delta/AY.37, as well as of Omicron/BA.1.1 and Omicron/BA.1.15.1, from the neighboring Western, Eastern and Middle African regions at dates close to their estimated emergence. Sequencing output was \u0026gt;0.5% of the total positive samples and resulted in the timely delivery of two preliminary variant identification reports to national health authorities, followed by six official reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work underscores key findings during a global health crisis and offers operational guidance to support future genomic surveillance initiatives in low- and middle-income countries. Sustained financial investment, dedicated time, specialized expertise, efficient logistics, and local ownership are essential for long-term implementation of such capacities.\u003c/p\u003e","manuscriptTitle":"Two years of genomic surveillance capacity development in Guinea: an operational roadmap for local implementation in low-income countries and tracking of SARS-CoV-2 circulation dynamics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 14:51:28","doi":"10.21203/rs.3.rs-8530368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-12T12:04:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T09:50:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T23:47:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T18:53:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261546399649065980543069815193170382072","date":"2026-01-23T18:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278260479999583604418711469267985889049","date":"2026-01-22T17:00:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167263137966748593664387892907012703089","date":"2026-01-20T06:32:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T05:46:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-09T12:22:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T05:47:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T05:45:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-06T10:48:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"425f41cb-218d-438a-80bf-4517cad24d46","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61415355,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61415356,"name":"Health sciences/Diseases"},{"id":61415357,"name":"Biological sciences/Genetics"},{"id":61415358,"name":"Health sciences/Health care"},{"id":61415359,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-04-07T16:04:06+00:00","versionOfRecord":{"articleIdentity":"rs-8530368","link":"https://doi.org/10.1038/s41598-026-46736-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-01 15:58:02","publishedOnDateReadable":"April 1st, 2026"},"versionCreatedAt":"2026-01-22 14:51:28","video":"","vorDoi":"10.1038/s41598-026-46736-y","vorDoiUrl":"https://doi.org/10.1038/s41598-026-46736-y","workflowStages":[]},"version":"v1","identity":"rs-8530368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8530368","identity":"rs-8530368","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}