First Report of Dengue Virus in Sierra Leone: Implications for Arbovirus Surveillance and Control

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O. Campbell, Ifeanyi F. Omah, Andy M. Diouf, Mignane Ndiaye, and 32 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7767082/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dengue is the leading mosquito-borne viral cause of human illness and death. More than four billion people globally live at risk of Dengue Virus (DENV) infection and most infections are asymptomatic or present with a non-specific febrile illness. In this report the first laboratory-confirmed dengue in Sierra Leone was identified one month after the launch of the national Syndromic Sentinel Surveillance Strategy (4S). A febrile adult tested RT-PCR–positive; we generated a near-complete genome assigned to DENV-2 genotype II, lineage F.1.1. Phylogenetically, the Sierra Leone genome formed a well-supported sister lineage with a 2024 USA genome, both nested within, but clearly diverged from, Indian sequences (2021–2022) and distinct from the Réunion clade. The degree of divergence is incompatible with a recent or direct import from South Asia lineages. It is more consistent with diversification in an undersampled Indian-Ocean/South Asia network or via unsampled intermediates outside Asia. No canonical NS4B resistance substitutions were detected. With a single Sierra Leone genome, the source and extent of local transmission remain unresolved. These findings underscore the benefits of integrating differential diagnostics and genomics into routine febrile-illness care and sustaining regional arboviral surveillance. Epigenetics & Genomics Molecular Biology Dengue virus DENV-2 Sierra Leone arbovirus surveillance phylogenetics genomic epidemiology Figures Figure 1 Figure 2 Figure 3 Background Dengue is the leading mosquito-borne viral cause of human illness and death. More than four billion people globally live at risk of Dengue Virus (DENV) infection and most infections are asymptomatic or present with a non-specific febrile illness (Madewell, 2020); WHO, 2024). Each year, ~ 500,000 cases progress to severe disease with appreciable mortality (Young, 2018). Four distinct serotypes (DENV-1–4) are in circulation globally and differ in epidemic potential and clinical severity (Sah et al., 2023) In Africa, confirmed outbreaks and co-circulation of multiple serotypes, including DENV-2, have been reported in Senegal, Mauritania, Cabo Verde, Burkina Faso and elsewhere, with entomological evidence of active transmission in urban settings (Fourié et al., 2021; Dieng et al., 2022), suggesting that DENV could be widespread on the continent against a backdrop of limited access to differential diagnostics and sparse pathogen-specific surveillance. Outside malaria, often, the only routinely available point-of-care test, most non-malarial fevers are empirically managed, leaving their aetiologies largely uncharacterized and creating substantial gaps in policy response (Amoako et al., 2018). However, where differential testing has been deployed, substantial underdetection emerges: in a cohort of 515 febrile patients in southern Nigeria who were malaria- and typhoid-negative, ~ 28% had evidence of prior DENV exposure, ~ 8% showed recent/ongoing infection, and ~ 3% met criteria for acute infection (B. A. Onoja et al., 2024). This diagnostic gap obscures the burden of arboviruses such as dengue and constrains timely outbreak detection. In West and Central Africa (WCA) more broadly, dengue epidemiology remains undersampled, even as rapid urbanisation, mobility, and climate change are reshaping Aedes ecology and likely accelerating transmission potential (Mordecai et al., 2020). Sierra Leone has no confirmed national record of dengue to date. Historical dengue reporting is sparse and fragmented, with no sustained nationwide arboviral surveillance and very limited routine testing beyond malaria(De Araújo Lobo et al., 2016; Dariano et al., 2017).Given established Aedes aegypti presence, densely populated urban city (Western Area Urban), porous regional travel networks, and ecological suitability that is likely increasing with climate variability (Jones et al., 2023; De Araújo Lobo et al., 2016), undetected endemic or recurrently imported DENV transmission is plausible. Some studies have reported the seroprevalence of DENV in Kenema and Bo (Dariano et al., 2017). However, in the absence of systematic, syndromic and laboratory-supported surveillance, the magnitude, serotype diversity, and transmission dynamics in Sierra Leone remain uncertain. To address this gap, Sierra Leone initiated implementation of the Syndromic Sentinel Surveillance Strategies (4S) in June 2025, adapted from the Integrated Surveillance and Laboratory Network (RISLNET) model in Senegal, where 4S has repeatedly enabled early detection of respiratory and arboviral outbreaks, including Rift Valley fever virus (RVF), Crimean-Congo hemorrhagic fever (CCHF), Zika, yellow fever, chikungunya, and dengue, since 2015 (Dieng et al., 2024);(Dieng et al., 2022). 4S links frontline clinical syndromes to targeted laboratory testing and genomic confirmation, creating an operational platform for rapid risk assessment. Here, we report the first month of 4S implementation in Sierra Leone, including the country’s first laboratory-confirmed dengue case detected through this system, and we place this finding within the regional context of evolving DENV transmission. Our results underscore the need and feasibility of integrating differential diagnostics and genomic surveillance into routine febrile-illness care to understand the true burden of dengue and guide public-health action in Sierra Leone. Clinical suspicion of Dengue Fever confirmed by RT-PCR A 64-year-old female herbalist presented on 5 July 2025 with a five-day history of headache and arthralgia. Examination showed tachypnoea (38 breaths per minute) and mild tachycardia (104 beats per minute); blood pressure was 141/76 mmHg (widened pulse pressure) and oxygen saturation 96% on room air. No haemorrhagic signs, hypoxia or circulatory instability were observed. Based on these clinical symptoms, the doctor at the hospital suspected acute haemorrhagic fever (Likely Dengue) as these signs were most consistent with early dengue infection without evidence of plasma leakage or shock at presentation. During hospitalisation, blood samples were obtained and sent to the Central Public Health Reference Laboratory (CPHRL) for confirmation. Using RT-PCR, we confirmed that the patient was positive for the Dengue virus. However, due to the concurrent Mpox outbreak in Sierra Leone, a thorough case investigation, including detailed epidemiological follow-up, was not possible. DENV-2 genotype II lineage F 1.1 is responsible for the febrile illness To identify the serotype of the DENV, we generated a near-complete dengue genome from the Sierra Leone case. The consensus sequence has a genome coverage of 99.7%. BLAST analysis identified the closest matches as DENV serotype 2 (DENV-2) sequences sampled in India in 2021–2022, with 99.23–99.45% nucleotide similarity. To place our novel sequence within the context of global DENV diversity, we determined its lineage using Nextclade. The sequence was assigned to genotype II, major lineage F and minor lineage 1.1 (2II_F.1.1)(Hill et al., 2024). Also known as the Cosmopolitan genotype, DENV-2 genotype II is one of the most widely distributed and diverse DENV genotypes (Hill et al., 2024) Next, we compiled a dataset of representative sequences for all DENV-2 from GenBank and GISAID (n = 5,475) to resolve the geographic origin of our sequence. Using this dataset, we inferred a maximum-likelihood phylogeny (Fig. 1). Consistent with previous results, our new sequence was nested within DENV-2, clustering with sequences from DENV-2 genotype II F 1.1 (2II_F.1.1) (Fig. 1). Major Lineage F is globally distributed, with minor lineage 1.1 predominantly circulating across Asia and the Caribbean(Hill et al., 2024). Our SLE sequence clustered together with a 2024 USA genome with a bootstrap support of 99, and they are both nested in but diverged from sequences sampled in India. Our SLE sequence is separated from the closest USA sequences with 64 substitutions and Indian relatives (2021–2022) by a total of 127 substitutions, with 45 substitutions along the branch from the common ancestor (Fig. 1). This degree of divergence is inconsistent with a recent import from South Asia; instead, it suggests a prolonged period of diversification from the shared ancestor likely circulating in India or somewhere in South Asia or an unsampled location outside of the South Asia network. Our SLE sequence formed a sister lineage to sequences sampled from Réunion Island (2023–2024) (Bootstrap support = 100). Our SLE sequence is separated from the closest sequence from Réunion by 66 substitutions. There have been recurrent DENV epidemics in Réunion since 2017, resulting from repeated introductions from the Indian Ocean region (Frumence et al., 2024;Vincent et al., 2020). Together with the degree of divergence, points to independent exports from an unsampled 2II_F.1.1 reservoir in the Indian-Ocean/South Asia network, rather than direct transmission from India or the USA or Réunion to Sierra Leone. Tree of representative DENV-2 viruses coloured by minor lineages, the Sierra Leone genome 064_S23|SLE|2025 is highlighted in blue. The solid box expands the relevant DENV-2 lineage 2II_F.1.1 clade. Within this clade, two Indian genomes from 2021–2022 are basal to the Sierra Leone genome, followed by a sister clade of a tight Réunion Island cluster sampled in 2023–2024. Tip labels show accession | location | host | date. Branch lengths are substitutions per site (100 bootstrap support). The topology places the Sierra Leone virus within DENV-2 2II_F.1.1, nearest to Indian Ocean/South Asia lineages and adjacent to the Réunion outbreak cluster. 2II_F.1.1 reported is part of the South Asia lineages We performed Bayesian phylogenetic reconstructions to understand the timing of divergence between our SLE sequence relative to the 2II_F.1.1 Diversity in India and Réunion. We performed Bayesian phylogenetic reconstruction under a log-normal uncorrelated relaxed clock and a constant demographic model. The SLE genome’s median divergence time from its closest sampled South-Asia relative was December 2021, with wide uncertainty [95% HPD: November 2020 to October 2022] (Fig. 2). Given the phylogenetic placement of our SLE sequence nested within India diversity (Fig. 1), this suggests that our sequence likely descended from diversity circulating in India or the wider Asian lineages or an unsampled location outside of the South Asia network more than three years before this case report. This is inconsistent with a recent import from South Asia. However, we cannot infer the source of the introduction with any confidence owing to sparse sampling in the African region and beyond (Fig. 2B). We cannot infer that it was a direct introduction from South Asia, with the lineages diverging locally in India in the reservoir population before a more recent introduction. We also cannot exclude the possibility that the introduction from South Asia was facilitated via an unsampled location. As only one SLE genome was available, we could not resolve the extent of cryptic transmission locally. Additional sampling from Sierra Leone and neighbouring regions will be critical to distinguish between unsampled persistence and repeated introductions. No drug resistance-associated mutations No drug resistance-associated mutations Substitutions at specific NS4B residues (notably V91A, L94F, T108I, and, context-dependently, T216N/P) have been shown to reduce susceptibility to NS4B-targeting dengue antivirals (e.g., JNJ-A07, JNJ-1802) by restoring the NS3–NS4B interaction disrupted by these compounds (Goethals et al., 2023); (Bouzidi et al., 2024) We screened our Sierra Leone genome against this resistance panel using output from the Nextclade lineage assignment tool (Aksamentov et al., 2021a). The SLE genome has the NS4B:A19T, V48I, F112L, and T244A substitutions, but none of the canonical resistance substitutions (V91A, L94F, T108I, T216N/P) were present (Supplementary table). To contextualise these findings, we downloaded all publicly available 2II_F.1.1 high-quality genomes from GISAID and NCBI (n = 5,110). We found that our sequence exhibits a background NS4B profile typical of 2II_F.1.1. We found that the mutations observed at the NS4B region of our Sierra Leonean sequence are common, clade-typical polymorphisms. A19T occurred in 3,180 of the 5,110 sequences (62.2%), V48I in 3,117/5,110 (61.0%), F112L in 3,183/5,110 (62.3%), and T244A in 3,145/5,110 (61.6%). We found that canonical resistance signatures are exceptionally infrequent across this lineage: V91A occurs in 5 of the 5,110 sequences (0.10%), T108I in 3/5,110 (0.06%), and L94F or T216N/P was not detected. No genomes carried any combination of resistance substitutions (e.g., V91A + T108I or larger constellations). Discussion This report documents the first laboratory-confirmed dengue case in Sierra Leone. Detection of DENV-2 one month after implementing the Syndromic Sentinel Surveillance Strategy (4S) underscores the value of differential diagnostics for non-malarial febrile illness in a setting where symptom overlap has historically driven underreporting of the aetiology of non-malarial febrile illnesses and delayed response ( Onoja et al., 2024 ; De Araújo Lobo et al., 2016 ). Strengthening continuous genomic surveillance, cross-border data sharing, and targeted public-health interventions will be critical to mitigate onward transmission. In parallel, building laboratory capacity and integrating dengue and other priority arbovirus screening into routine febrile illness management, alongside clear public-health messaging, will help to improve prevention, recognition, co-infection management, and patient outcomes ( Mitsakakis et al., 2018 ; Robert et al., 2025 ). Also, understanding the epidemiology and transmission dynamics of dengue in Sierra Leone through sustained sampling will provide the evidence needed to guide control strategies and preparedness for future arboviral threats (Onoja et al., 2024 ; De Araújo Lobo et al., 2016 ). The co-circulation of multiple serotypes in neighbouring countries heightens the risk of severe secondary infections. Burkina Faso experienced Africa’s largest recorded dengue outbreak, with 70,433 confirmed cases and 709 deaths driven by concurrent DENV-1–3 circulation ( Manigart et al., 2024 ). Similar multi-serotype transmission has been reported in Senegal ( Dieng et al., 2024 ),Reunion ( Frumence et al., 2024 ; Vincent et al., 2020) , and Cabo Verde (44,410 cases, including 17,942 confirmed, and eight deaths). Given the relationship of our Sierra Leone genome within DENV-2 lineage 2II_F.1.1 from the related USA, Réunion Island and India genomes underscores the role of international travel in the spread of infectious diseases, particularly within broader DENV transmission networks ( Vincent et al., 2020b ). These patterns underscore the need for early detection, enhanced diagnostic capacity, and targeted clinical training ( Fourié et al., 2021 ); ( Dieng et al., 2022 ) The absence of canonical NS4B substitutions known to confer resistance to NS4B-targeting inhibitors is encouraging. While these may not currently represent resistance-associated adaptations, they should be monitored closely, as functional shifts in NS4B can alter antiviral sensitivity ( Bouzidi et al., 2024 ).This points to a broader lesson: molecular surveillance must not only catalogue known resistance mutations but also remain alert to lineage-defining changes that could alter viral behaviour over time. Together, these findings extend beyond cataloguing a single genome. They illustrate three converging dynamics: (i) dengue’s increasing reach into West Africa through global pathways, (ii) the critical importance of diagnostic strengthening to avoid underestimation of its true burden, and (iii) the role of genomic surveillance in identifying both introductions and potential molecular adaptations. A comprehensive public health strategy for Sierra Leone, therefore, must combine classical surveillance with genomics, healthcare provider education, and regional collaboration. This integrated approach will be essential to detect introductions early, mitigate outbreaks, and contribute to a more accurate understanding of dengue’s evolving role in Africa’s infectious disease landscape. Methods Sentinel sites During the Implementation phase, two pilot sites were chosen for the establishment of the sentinel surveillance: Christ the King Hospital (CKH) in Waterloo and Kenema Government Hospital in Kenema (KGH)(Fig. 4 ). The CKH, being located in the Western Rural Area district, has a large population catchment, while in Kenema, previously published studies highlighted the circulation of arboviruses ( Schoepp et al., 2014 ). During the practical phase, staff from the selected sites were trained on syndromic surveillance, including case definition, filling the case identification form, specimen sampling from suspected cases, storage, packaging, and transport, and data entry on a digital health platform. Patients were considered suspect for arbovirus infection if they presented with fever and at least two minor signs such as headaches, muscle pain, joint pain, retro-orbital pain, skin rash, etc. Central Public Health Reference Laboratory Building on its extensive experience in disease surveillance and control efforts, CPHRL was chosen being the national reference laboratory to test for suspected cases. RT-PCR reagents for the detection of seven arboviruses of public health concern in West Africa (Crimean Congo Hemorrhagic fever, chikungunya, dengue, Rift Valley fever, West Nile, yellow fever, and Zika) were provided. Practical sessions were carried out with staff who previously had considerable experience in molecular biology. This included the pre-analytical phase, which covers sample reception, handling, and storage, the analytical phase, and the post-analytical phase, with results reported on the digital health platform. Sample Collection, Nucleic Acid Extraction, and Next-Generation Sequencing Blood sample was collected at Christ the King Hospital (CKH) during the case investigation and transported at 2–8°C to the CPHRL for molecular detection and subsequent sequencing. Serum was separated from the collected blood, and total viral nucleic acid was extracted using the MagMAX Viral/Pathogen Nucleic Acid Isolation Kit on the KingFisher Automated Extraction System (both from Thermo Fisher Scientific, USA), following the manufacturer’s protocols. RT-PCR assay was carried out using Lightmix Polymerase 1-step RT-PCR mix (TIB MOLBIOL, Germany). The reaction mix consisted of 5µL of viral RNA, Lightmix buffer, and 10 µM of previously published dengue-specific primers that detect any of the four serotypes (Wagner et al., 2004 ). Samples were first confirmed by real-time reverse transcription PCR (RT-PCR), and the sample had a cycle threshold (Ct) value < 30. Library preparation was performed using the Illumina RNA Prep with Enrichment (L) Kit, incorporating the Viral Surveillance Panel (VSP) 2.0, which targets epidemic-prone pathogens, including Dengue virus. Following hybridization, libraries were captured using streptavidin-coated magnetic beads, then amplified, purified, and quantified. Quality assessment was conducted using the Qubit fluorometer (Thermo Fisher Scientific) and the Agilent TapeStation system (Agilent Technologies, Santa Clara, CA, USA). Sequencing was performed on the Illumina MiniSeq platform, generating 2 × 150 bp paired-end reads. Bioinformatics analysis Genome assembly We used a reference-guided assembly pipeline to analyze the sequencing data generated using the Illumina Viral Surveillance Panel v2 kit (described above). Raw reads were quality-filtered with Trimmomatic v0.39 ( Bolger et al., 2014 ) (parameters: ILLUMINACLIP:2:30:10 SLIDINGWINDOW:4:20 MINLEN:50) to remove adapters and low-quality bases. To deplete host material, filtered reads were aligned to the human genome (hg38) with Bowtie2 v2.5.4 ( Langmead & Salzberg, 2012 ) ; unmapped reads were retained for viral analysis. These de-hosted reads were then mapped with Minimap2 v2.28) ( Li, 2021 ) to representative references for the four dengue serotypes: DENV-1 (NC_001477.1), DENV-2(NC_001474.2), DENV-3 (NC_001475.2), and DENV-4 (NC_002640.1). Assemblies achieved a high breadth of coverage (up to ~ 97% for DENV-2 in our dataset). Viral species/serotype assignment was corroborated by BLASTn against the NCBI nucleotide database ( Sayers et al., 2025 ) Variant calling and consensus generation Variants were called with iVar v1.4.3 ( Grubaugh et al., 2019 ). We required per-site depth ≥ 50×; positions below this threshold were excluded from variant reporting and masked during consensus generation. Consensus genomes were produced with iVar under the same depth filter. Lineages for the resulting consensus sequences were assigned using Nextclade (Aksamentov et al., 2021b) . Phylogenetic analysis We combined our one high-quality genome (breadth of coverage > 90%) with publicly available dengue sequences from GenBank and GISAID (n = 5,575; deliberately enriched for DENV-2) to contextualise likely geographic origins and circulation history. Sequences were aligned with MAFFT v7.52 ( Katoh & Standley, 2013 ) and a maximum-likelihood tree was inferred with IQ-TREE v2.2.5 ( Minh et al., 2020 ) using ModelFinder Plus ( Kalyaanamoorthy et al., 2017 ) for model selection and ultrafast bootstrap support ( Hoang et al., 2018 ) (UFBoot; 1,000 replicates). For visualization in the Results, we subsampled 336 genomes to retain temporal and geographic breadth while reducing redundancy (priority to nearest phylogenetic neighbours of the Sierra Leone genome and diverse regional representatives). We reconstructed the time-resolved phylogeny using BEASTx v10.5 ( Baele et al., 2025 ; Suchard et al., 2018 ) . We used the GTR substitution model with gamma distribution rate variation among all sites. We employed the uncorrelated relax clock lognormal using a constant coalescent tree prior. We combined two independent MCMC chains of 100 million states ran with the BEAGLE computational library ( Ayres et al., 2019 ). Parameters and trees were sampled every 10,000 steps, with 10% of steps discarded as burn-in. Convergence and mixing of the MCMC chains were assessed with Tracer v.1.7.2 ( Rambaut et al., 2018 ), and all estimated parameters were determined to have effective sample sizes of greater than 200. Declarations Ethics Declaration This work was conducted as part of routine sentinel surveillance for arboviral diseases under the mandate of the National Public Health Agency (NPHA), Ministry of Health, Sierra Leone. All activities were performed in accordance with national surveillance and case investigation guidelines. As this activity was undertaken within the framework of public health surveillance, it was exempted from institutional ethics review and the requirement for individual informed consent under national public health regulations. All data were anonymised prior to analysis to protect patient confidentiality. Funding Statement The WARIL project is funded by the Global Fund DENV full genome sequencing was funded by the Africa CDC through the Africa PGI. Ethics Declaration This work was conducted as part of routine sentinel surveillance for arboviral diseases under the mandate of the National Public Health Agency (NPHA), Ministry of Health, Sierra Leone. All activities were performed in accordance with national surveillance and case investigation guidelines. As this activity was undertaken within the framework of public health surveillance, it was exempted from institutional ethics review and the requirement for individual informed consent under national public health regulations. All data were anonymized prior to analysis to protect patient confidentiality. Data availability The Dengue virus serotype 2 (F.1.1) genome sequence from this case report has been deposited in the GISAID database (Accession ID: EPI_ISL_20179260). Acknowledgments We want to acknowledge the staff of Christ the King Hospital especially Beatrice Bangali and Alfred Kamara and Kenema Government Hospital team, especially Dr. Tejan Jalloh and Kelfala Konneh Conflict of interest No conflicts of interest. Disclaimer N/A Author contributions Conceptualization,A.C., ; methodology A.C, I.O.; A.D.;J.S.O.C;M.N.;E.P software A.C;I.O, validation A.C, formal analysis A.C, I.O.; A.D.;J.S.O.C;M.N.;E.P investigation, A.C.; C.M.; A.J.W; N.D.S; J.C.;V.F.;S.M.; Z.S.; R.L.; M.T.K; A.K.; F.C.;T.J.;J.C; A.T.K;J.K.; resources D.H.;Z.K.; M.A.V; F.S.; C.T;S.T;A.K data curation A.O.K.C ; writing—original draft preparation,A.C.,I.O.,J.S.O.C;A.D;M.N; writing—review and editing,A.C.;I.O.;A.D.J.S.O.C;E.P;M.N;C.T;S.T;A.K visualization, I.O; supervision A.C;D.H.;B.D;B.S;A.S;Z.K;M.A.V;J.S.S;F.S project administration, A.C.F.C;D.H, funding acquisition B.D.A.K.;C.T; S.T;Y.K;. All authors have read and agreed to the published version of the manuscript. References Aksamentov I, Roemer C, Hodcroft E, Neher R (2021a) Nextclade: Clade assignment, mutation calling and quality control for viral genomes. 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Emerg Infect Dis 10(10):1872–1873. https://doi.org/10.3201/eid1010.031037 Supplementary Table 1 Notable amino acid annotations Additional Declarations The authors declare no competing interests. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:05:46","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130823,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7767082/v1/df81caff97507f9232f97893.html"},{"id":93024102,"identity":"852fafd4-04fa-4170-8885-fbef1622bf18","added_by":"auto","created_at":"2025-10-08 09:13:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189590,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1 | Maximum-likelihood phylogeny placing the Sierra Leone dengue genome in the global context.\u003c/p\u003e\n\u003cp\u003eTree of representative DENV-2 viruses coloured by minor lineages, the Sierra Leone genome 064_S23|SLE|2025 is highlighted in blue. The solid box expands the relevant DENV-2 lineage 2II_F.1.1 clade. Within this clade, two Indian genomes from 2021–2022 are basal to the Sierra Leone genome, followed by a sister clade of a tight Réunion Island cluster sampled in 2023–2024. Tip labels show accession | location | host | date. Branch lengths are substitutions per site (100 bootstrap support). The topology places the Sierra Leone virus within DENV-2 2II_F.1.1, nearest to Indian Ocean/South Asia lineages and adjacent to the Réunion outbreak cluster.\u003c/p\u003e","description":"","filename":"finalfigure2UCLDEN2embeddeddensityfull.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7767082/v1/ab74dd6f79f7fd66f4e645bf.jpg"},{"id":93023042,"identity":"99feb4ca-df44-469a-8ee2-1e4502f23b10","added_by":"auto","created_at":"2025-10-08 09:05:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":165639,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1 | Maximum-likelihood phylogeny placing the Sierra Leone dengue genome in the global context.\u003c/p\u003e\n\u003cp\u003eTree of representative DENV-2 viruses coloured by minor lineages, the Sierra Leone genome 064_S23|SLE|2025 is highlighted in blue. The solid box expands the relevant DENV-2 lineage 2II_F.1.1 clade. Within this clade, two Indian genomes from 2021–2022 are basal to the Sierra Leone genome, followed by a sister clade of a tight Réunion Island cluster sampled in 2023–2024. Tip labels show accession | location | host | date. Branch lengths are substitutions per site (100 bootstrap support). The topology places the Sierra Leone virus within DENV-2 2II_F.1.1, nearest to Indian Ocean/South Asia lineages and adjacent to the Réunion outbreak cluster.\u003c/p\u003e","description":"","filename":"completeBigDengue2.lineagecoloured2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7767082/v1/5008406aa9e48406474cf5c6.jpg"},{"id":93025054,"identity":"eed1a381-19fc-4761-b12e-7b79080e22c3","added_by":"auto","created_at":"2025-10-08 09:21:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":322069,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3. Lineage background versus resistance-associated NS4B substitutions in Dengue 2II_F.1.1, highlighting the Sierra Leone genome. The bar chart summarises NS4B variation across 2II_F.1.1 genomes (n = 5,110). The dashed divider separates clade-typical background polymorphisms (A19T, V48I, F112L, T244A) from resistance-associated positions described for NS4B-targeting dengue antivirals that disrupt the NS3–NS4B interaction (V91A, L94F, T108I, T216N/P). Bar heights indicate the percentage of genomes; annotations show raw counts (top) and percentages (in parentheses). The Sierra Leone genome 064_S23|SLE|2025 (caption footnote) carries A19T, V48I, F112L, and T244A, but none of the resistance substitutions. Across the dataset, V91A and T108I are rare (5/5,110 and 3/5,110, respectively), and L94F or T216N/P were not observed; no co-occurrence of resistance substitutions was detected.\u003c/p\u003e","description":"","filename":"Resistantfigure.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7767082/v1/ad3ef8175bec6bdbe2066a13.jpg"},{"id":93026191,"identity":"af6d71b3-43fb-4453-badb-87008565b27c","added_by":"auto","created_at":"2025-10-08 09:29:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1428126,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7767082/v1/b535be61-8af3-4879-98da-5832ac04f36a.pdf"},{"id":93023039,"identity":"ff523d12-84ee-420f-bf06-90631790349c","added_by":"auto","created_at":"2025-10-08 09:05:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":116590,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7767082/v1/599fff43a34af9be2b0689d8.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFirst Report of Dengue Virus in Sierra Leone: Implications for Arbovirus Surveillance and Control\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eDengue is the leading mosquito-borne viral cause of human illness and death. More than four billion people globally live at risk of Dengue Virus (DENV) infection and most infections are asymptomatic or present with a non-specific febrile illness (Madewell, 2020); WHO, 2024). Each year, ~\u0026thinsp;500,000 cases progress to severe disease with appreciable mortality (Young, 2018). Four distinct serotypes (DENV-1\u0026ndash;4) are in circulation globally and differ in epidemic potential and clinical severity (Sah et al., 2023) In Africa, confirmed outbreaks and co-circulation of multiple serotypes, including DENV-2, have been reported in Senegal, Mauritania, Cabo Verde, Burkina Faso and elsewhere, with entomological evidence of active transmission in urban settings (Fouri\u0026eacute; et al., 2021; Dieng et al., 2022), suggesting that DENV could be widespread on the continent against a backdrop of limited access to differential diagnostics and sparse pathogen-specific surveillance.\u003c/p\u003e\n\u003cp\u003eOutside malaria, often, the only routinely available point-of-care test, most non-malarial fevers are empirically managed, leaving their aetiologies largely uncharacterized and creating substantial gaps in policy response (Amoako et al., 2018). However, where differential testing has been deployed, substantial underdetection emerges: in a cohort of 515 febrile patients in southern Nigeria who were malaria- and typhoid-negative, ~\u0026thinsp;28% had evidence of prior DENV exposure, ~\u0026thinsp;8% showed recent/ongoing infection, and ~\u0026thinsp;3% met criteria for acute infection (B. A. Onoja et al., 2024). This diagnostic gap obscures the burden of arboviruses such as dengue and constrains timely outbreak detection. In West and Central Africa (WCA) more broadly, dengue epidemiology remains undersampled, even as rapid urbanisation, mobility, and climate change are reshaping \u003cem\u003eAedes\u003c/em\u003e ecology and likely accelerating transmission potential (Mordecai et al., 2020).\u003c/p\u003e\n\u003cp\u003eSierra Leone has no confirmed national record of dengue to date. Historical dengue reporting is sparse and fragmented, with no sustained nationwide arboviral surveillance and very limited routine testing beyond malaria(De Ara\u0026uacute;jo Lobo et al., 2016; Dariano et al., 2017).Given established \u003cem\u003eAedes aegypti\u003c/em\u003e presence, densely populated urban city (Western Area Urban), porous regional travel networks, and ecological suitability that is likely increasing with climate variability (Jones et al., 2023; De Ara\u0026uacute;jo Lobo et al., 2016), undetected endemic or recurrently imported DENV transmission is plausible. Some studies have reported the seroprevalence of DENV in Kenema and Bo (Dariano et al., 2017). However, in the absence of systematic, syndromic and laboratory-supported surveillance, the magnitude, serotype diversity, and transmission dynamics in Sierra Leone remain uncertain.\u003c/p\u003e\n\u003cp\u003eTo address this gap, Sierra Leone initiated implementation of the Syndromic Sentinel Surveillance Strategies (4S) in June 2025, adapted from the Integrated Surveillance and Laboratory Network (RISLNET) model in Senegal, where 4S has repeatedly enabled early detection of respiratory and arboviral outbreaks, including Rift Valley fever virus (RVF), Crimean-Congo hemorrhagic fever (CCHF), Zika, yellow fever, chikungunya, and dengue, since 2015 (Dieng et al., 2024);(Dieng et al., 2022). 4S links frontline clinical syndromes to targeted laboratory testing and genomic confirmation, creating an operational platform for rapid risk assessment. Here, we report the first month of 4S implementation in Sierra Leone, including the country\u0026rsquo;s first laboratory-confirmed dengue case detected through this system, and we place this finding within the regional context of evolving DENV transmission. Our results underscore the need and feasibility of integrating differential diagnostics and genomic surveillance into routine febrile-illness care to understand the true burden of dengue and guide public-health action in Sierra Leone.\u003c/p\u003e\n\u003ch3\u003eClinical suspicion of Dengue Fever confirmed by RT-PCR\u003c/h3\u003e\n\u003cp\u003eA 64-year-old female herbalist presented on 5 July 2025 with a five-day history of headache and arthralgia. Examination showed tachypnoea (38 breaths per minute) and mild tachycardia (104 beats per minute); blood pressure was 141/76 mmHg (widened pulse pressure) and oxygen saturation 96% on room air. No haemorrhagic signs, hypoxia or circulatory instability were observed. Based on these clinical symptoms, the doctor at the hospital suspected acute haemorrhagic fever (Likely Dengue) as these signs were most consistent with early dengue infection without evidence of plasma leakage or shock at presentation. During hospitalisation, blood samples were obtained and sent to the Central Public Health Reference Laboratory (CPHRL) for confirmation. Using RT-PCR, we confirmed that the patient was positive for the Dengue virus. However, due to the concurrent Mpox outbreak in Sierra Leone, a thorough case investigation, including detailed epidemiological follow-up, was not possible.\u003c/p\u003e\n\u003cp\u003eDENV-2 genotype II lineage F 1.1 is responsible for the febrile illness\u003c/p\u003e\n\u003cp\u003eTo identify the serotype of the DENV, we generated a near-complete dengue genome from the Sierra Leone case. The consensus sequence has a genome coverage of 99.7%. BLAST analysis identified the closest matches as DENV serotype 2 (DENV-2) sequences sampled in India in 2021\u0026ndash;2022, with 99.23\u0026ndash;99.45% nucleotide similarity. To place our novel sequence within the context of global DENV diversity, we determined its lineage using Nextclade. The sequence was assigned to genotype II, major lineage F and minor lineage 1.1 (2II_F.1.1)(Hill et al., 2024). Also known as the Cosmopolitan genotype, DENV-2 genotype II is one of the most widely distributed and diverse DENV genotypes (Hill et al., 2024)\u003c/p\u003e\n\u003cp\u003eNext, we compiled a dataset of representative sequences for all DENV-2 from GenBank and GISAID (n\u0026thinsp;=\u0026thinsp;5,475) to resolve the geographic origin of our sequence. Using this dataset, we inferred a maximum-likelihood phylogeny (Fig.\u0026nbsp;1). Consistent with previous results, our new sequence was nested within DENV-2, clustering with sequences from DENV-2 genotype II F 1.1 (2II_F.1.1) (Fig.\u0026nbsp;1). Major Lineage F is globally distributed, with minor lineage 1.1 predominantly circulating across Asia and the Caribbean(Hill et al., 2024).\u003c/p\u003e\n\u003cp\u003eOur SLE sequence clustered together with a 2024 USA genome with a bootstrap support of 99, and they are both nested in but diverged from sequences sampled in India. Our SLE sequence is separated from the closest USA sequences with 64 substitutions and Indian relatives (2021\u0026ndash;2022) by a total of 127 substitutions, with 45 substitutions along the branch from the common ancestor (Fig.\u0026nbsp;1). This degree of divergence is inconsistent with a recent import from South Asia; instead, it suggests a prolonged period of diversification from the shared ancestor likely circulating in India or somewhere in South Asia or an unsampled location outside of the South Asia network. Our SLE sequence formed a sister lineage to sequences sampled from R\u0026eacute;union Island (2023\u0026ndash;2024) (Bootstrap support\u0026thinsp;=\u0026thinsp;100). Our SLE sequence is separated from the closest sequence from R\u0026eacute;union by 66 substitutions. There have been recurrent DENV epidemics in R\u0026eacute;union since 2017, resulting from repeated introductions from the Indian Ocean region (Frumence et al., 2024;Vincent et al., 2020). Together with the degree of divergence, points to independent exports from an unsampled 2II_F.1.1 reservoir in the Indian-Ocean/South Asia network, rather than direct transmission from India or the USA or R\u0026eacute;union to Sierra Leone.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003cp\u003eTree of representative DENV-2 viruses coloured by minor lineages, the Sierra Leone genome 064_S23|SLE|2025 is highlighted in blue. The solid box expands the relevant DENV-2 lineage 2II_F.1.1 clade. Within this clade, two Indian genomes from 2021\u0026ndash;2022 are basal to the Sierra Leone genome, followed by a sister clade of a tight R\u0026eacute;union Island cluster sampled in 2023\u0026ndash;2024. Tip labels show accession | location | host | date. Branch lengths are substitutions per site (100 bootstrap support). The topology places the Sierra Leone virus within DENV-2 2II_F.1.1, nearest to Indian Ocean/South Asia lineages and adjacent to the R\u0026eacute;union outbreak cluster.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2II_F.1.1 reported is part of the South Asia lineages\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe performed Bayesian phylogenetic reconstructions to understand the timing of divergence between our SLE sequence relative to the 2II_F.1.1 Diversity in India and R\u0026eacute;union. We performed Bayesian phylogenetic reconstruction under a log-normal uncorrelated relaxed clock and a constant demographic model. The SLE genome\u0026rsquo;s median divergence time from its closest sampled South-Asia relative was December 2021, with wide uncertainty [95% HPD: November 2020 to October 2022] (Fig.\u0026nbsp;2). Given the phylogenetic placement of our SLE sequence nested within India diversity (Fig.\u0026nbsp;1), this suggests that our sequence likely descended from diversity circulating in India or the wider Asian lineages or an unsampled location outside of the South Asia network more than three years before this case report. This is inconsistent with a recent import from South Asia. However, we cannot infer the source of the introduction with any confidence owing to sparse sampling in the African region and beyond (Fig.\u0026nbsp;2B). We cannot infer that it was a direct introduction from South Asia, with the lineages diverging locally in India in the reservoir population before a more recent introduction. We also cannot exclude the possibility that the introduction from South Asia was facilitated via an unsampled location. As only one SLE genome was available, we could not resolve the extent of cryptic transmission locally. Additional sampling from Sierra Leone and neighbouring regions will be critical to distinguish between unsampled persistence and repeated introductions.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNo drug resistance-associated mutations\u003c/h3\u003e\n\u003cdiv\u003eNo drug resistance-associated mutations\u003c/div\u003e\n\u003cp\u003eSubstitutions at specific NS4B residues (notably V91A, L94F, T108I, and, context-dependently, T216N/P) have been shown to reduce susceptibility to NS4B-targeting dengue antivirals (e.g., JNJ-A07, JNJ-1802) by restoring the NS3\u0026ndash;NS4B interaction disrupted by these compounds (Goethals et al., 2023); (Bouzidi et al., 2024)\u003c/p\u003e\n\u003cp\u003eWe screened our Sierra Leone genome against this resistance panel using output from the Nextclade lineage assignment tool (Aksamentov et al., 2021a). The SLE genome has the NS4B:A19T, V48I, F112L, and T244A substitutions, but none of the canonical resistance substitutions (V91A, L94F, T108I, T216N/P) were present (Supplementary table). To contextualise these findings, we downloaded all publicly available 2II_F.1.1 high-quality genomes from GISAID and NCBI (n\u0026thinsp;=\u0026thinsp;5,110). We found that our sequence exhibits a background NS4B profile typical of 2II_F.1.1. We found that the mutations observed at the NS4B region of our Sierra Leonean sequence are common, clade-typical polymorphisms. A19T occurred in 3,180 of the 5,110 sequences (62.2%), V48I in 3,117/5,110 (61.0%), F112L in 3,183/5,110 (62.3%), and T244A in 3,145/5,110 (61.6%). We found that canonical resistance signatures are exceptionally infrequent across this lineage: V91A occurs in 5 of the 5,110 sequences (0.10%), T108I in 3/5,110 (0.06%), and L94F or T216N/P was not detected. No genomes carried any combination of resistance substitutions (e.g., V91A\u0026thinsp;+\u0026thinsp;T108I or larger constellations).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis report documents the first laboratory-confirmed dengue case in Sierra Leone. Detection of DENV-2 one month after implementing the Syndromic Sentinel Surveillance Strategy (4S) underscores the value of differential diagnostics for non-malarial febrile illness in a setting where symptom overlap has historically driven underreporting of the aetiology of non-malarial febrile illnesses and delayed response \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eOnoja et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; De Ara\u0026uacute;jo Lobo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Strengthening continuous genomic surveillance, cross-border data sharing, and targeted public-health interventions will be critical to mitigate onward transmission. In parallel, building laboratory capacity and integrating dengue and other priority arbovirus screening into routine febrile illness management, alongside clear public-health messaging, will help to improve prevention, recognition, co-infection management, and patient outcomes \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eMitsakakis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Robert et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Also, understanding the epidemiology and transmission dynamics of dengue in Sierra Leone through sustained sampling will provide the evidence needed to guide control strategies and preparedness for future arboviral threats (Onoja et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; De Ara\u0026uacute;jo Lobo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe co-circulation of multiple serotypes in neighbouring countries heightens the risk of severe secondary infections. Burkina Faso experienced Africa\u0026rsquo;s largest recorded dengue outbreak, with 70,433 confirmed cases and 709 deaths driven by concurrent \u003cem\u003eDENV-1\u0026ndash;3\u003c/em\u003e circulation \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eManigart et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar multi-serotype transmission has been reported in Senegal \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eDieng et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e),Reunion \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eFrumence et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVincent et al., 2020)\u003c/span\u003e, and Cabo Verde (44,410 cases, including 17,942 confirmed, and eight deaths). Given the relationship of our Sierra Leone genome within DENV-2 lineage 2II_F.1.1 from the related USA, R\u0026eacute;union Island and India genomes underscores the role of international travel in the spread of infectious diseases, particularly within broader DENV transmission networks \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eVincent et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). These patterns underscore the need for early detection, enhanced diagnostic capacity, and targeted clinical training \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eFouri\u0026eacute; et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eDieng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe absence of canonical NS4B substitutions known to confer resistance to NS4B-targeting inhibitors is encouraging. While these may not currently represent resistance-associated adaptations, they should be monitored closely, as functional shifts in NS4B can alter antiviral sensitivity \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eBouzidi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).This points to a broader lesson: molecular surveillance must not only catalogue known resistance mutations but also remain alert to lineage-defining changes that could alter viral behaviour over time.\u003c/p\u003e\u003cp\u003eTogether, these findings extend beyond cataloguing a single genome. They illustrate three converging dynamics: (i) dengue\u0026rsquo;s increasing reach into West Africa through global pathways, (ii) the critical importance of diagnostic strengthening to avoid underestimation of its true burden, and (iii) the role of genomic surveillance in identifying both introductions and potential molecular adaptations. A comprehensive public health strategy for Sierra Leone, therefore, must combine classical surveillance with genomics, healthcare provider education, and regional collaboration. This integrated approach will be essential to detect introductions early, mitigate outbreaks, and contribute to a more accurate understanding of dengue\u0026rsquo;s evolving role in Africa\u0026rsquo;s infectious disease landscape.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eSentinel sites\u003c/h2\u003e\u003cp\u003eDuring the Implementation phase, two pilot sites were chosen for the establishment of the sentinel surveillance: Christ the King Hospital (CKH) in Waterloo and Kenema Government Hospital in Kenema (KGH)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The CKH, being located in the Western Rural Area district, has a large population catchment, while in Kenema, previously published studies highlighted the circulation of arboviruses \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eSchoepp et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). During the practical phase, staff from the selected sites were trained on syndromic surveillance, including case definition, filling the case identification form, specimen sampling from suspected cases, storage, packaging, and transport, and data entry on a digital health platform. Patients were considered suspect for arbovirus infection if they presented with fever and at least two minor signs such as headaches, muscle pain, joint pain, retro-orbital pain, skin rash, etc.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCentral Public Health Reference Laboratory\u003c/h2\u003e\u003cp\u003eBuilding on its extensive experience in disease surveillance and control efforts, CPHRL was chosen being the national reference laboratory to test for suspected cases. RT-PCR reagents for the detection of seven arboviruses of public health concern in West Africa (Crimean Congo Hemorrhagic fever, chikungunya, dengue, Rift Valley fever, West Nile, yellow fever, and Zika) were provided. Practical sessions were carried out with staff who previously had considerable experience in molecular biology. This included the pre-analytical phase, which covers sample reception, handling, and storage, the analytical phase, and the post-analytical phase, with results reported on the digital health platform.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample Collection, Nucleic Acid Extraction, and Next-Generation Sequencing\u003c/h3\u003e\n\u003cp\u003eBlood sample was collected at Christ the King Hospital (CKH) during the case investigation and transported at 2\u0026ndash;8\u0026deg;C to the CPHRL for molecular detection and subsequent sequencing. Serum was separated from the collected blood, and total viral nucleic acid was extracted using the MagMAX Viral/Pathogen Nucleic Acid Isolation Kit on the KingFisher Automated Extraction System (both from Thermo Fisher Scientific, USA), following the manufacturer\u0026rsquo;s protocols.\u003c/p\u003e\u003cp\u003eRT-PCR assay was carried out using Lightmix Polymerase 1-step RT-PCR mix (TIB MOLBIOL, Germany). The reaction mix consisted of 5\u0026micro;L of viral RNA, Lightmix buffer, and 10 \u0026micro;M of previously published dengue-specific primers that detect any of the four serotypes (Wagner et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Samples were first confirmed by real-time reverse transcription PCR (RT-PCR), and the sample had a cycle threshold (Ct) value\u0026thinsp;\u0026lt;\u0026thinsp;30.\u003c/p\u003e\u003cp\u003eLibrary preparation was performed using the Illumina RNA Prep with Enrichment (L) Kit, incorporating the Viral Surveillance Panel (VSP) 2.0, which targets epidemic-prone pathogens, including Dengue virus. Following hybridization, libraries were captured using streptavidin-coated magnetic beads, then amplified, purified, and quantified. Quality assessment was conducted using the Qubit fluorometer (Thermo Fisher Scientific) and the Agilent TapeStation system (Agilent Technologies, Santa Clara, CA, USA). Sequencing was performed on the Illumina MiniSeq platform, generating 2 \u0026times; 150 bp paired-end reads.\u003c/p\u003e\n\u003ch3\u003eBioinformatics analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGenome assembly\u003c/h2\u003e\u003cp\u003eWe used a reference-guided assembly pipeline to analyze the sequencing data generated using the Illumina Viral Surveillance Panel v2 kit (described above). Raw reads were quality-filtered with Trimmomatic v0.39 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eBolger et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) (parameters: ILLUMINACLIP:2:30:10 SLIDINGWINDOW:4:20 MINLEN:50) to remove adapters and low-quality bases. To deplete host material, filtered reads were aligned to the human genome (hg38) with Bowtie2 v2.5.4 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eLangmead \u0026amp; Salzberg, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e; unmapped reads were retained for viral analysis. These de-hosted reads were then mapped with Minimap2 v2.28) \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eLi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e to representative references for the four dengue serotypes: DENV-1 (NC_001477.1), DENV-2(NC_001474.2), DENV-3 (NC_001475.2), and DENV-4 (NC_002640.1). Assemblies achieved a high breadth of coverage (up to ~\u0026thinsp;97% for DENV-2 in our dataset). Viral species/serotype assignment was corroborated by BLASTn against the NCBI nucleotide database \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eSayers et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eVariant calling and consensus generation\u003c/h2\u003e\u003cp\u003eVariants were called with iVar v1.4.3 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eGrubaugh et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We required per-site depth\u0026thinsp;\u0026ge;\u0026thinsp;50\u0026times;; positions below this threshold were excluded from variant reporting and masked during consensus generation. Consensus genomes were produced with iVar under the same depth filter. Lineages for the resulting consensus sequences were assigned using Nextclade \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(Aksamentov et al., 2021b)\u003c/span\u003e.\u003c/p\u003e\u003cp\u003ePhylogenetic analysis\u003c/p\u003e\u003cp\u003eWe combined our one high-quality genome (breadth of coverage\u0026thinsp;\u0026gt;\u0026thinsp;90%) with publicly available dengue sequences from GenBank and GISAID (n\u0026thinsp;=\u0026thinsp;5,575; deliberately enriched for DENV-2) to contextualise likely geographic origins and circulation history. Sequences were aligned with MAFFT v7.52 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eKatoh \u0026amp; Standley, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and a maximum-likelihood tree was inferred with IQ-TREE v2.2.5 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eMinh et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eusing ModelFinder Plus \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eKalyaanamoorthy et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for model selection and ultrafast bootstrap support \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eHoang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (UFBoot; 1,000 replicates). For visualization in the Results, we subsampled 336 genomes to retain temporal and geographic breadth while reducing redundancy (priority to nearest phylogenetic neighbours of the Sierra Leone genome and diverse regional representatives).\u003c/p\u003e\u003cp\u003eWe reconstructed the time-resolved phylogeny using BEASTx v10.5 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eBaele et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Suchard et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e. We used the GTR substitution model with gamma distribution rate variation among all sites. We employed the uncorrelated relax clock lognormal using a constant coalescent tree prior. We combined two independent MCMC chains of 100\u0026nbsp;million states ran with the BEAGLE computational library \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eAyres et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Parameters and trees were sampled every 10,000 steps, with 10% of steps discarded as burn-in. Convergence and mixing of the MCMC chains were assessed with Tracer v.1.7.2 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003eRambaut et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and all estimated parameters were determined to have effective sample sizes of greater than 200.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Declaration This work was conducted as part of routine sentinel surveillance for arboviral diseases under the mandate of the National Public Health Agency (NPHA), Ministry of Health, Sierra Leone. All activities were performed in accordance with national surveillance and case investigation guidelines. As this activity was undertaken within the framework of public health surveillance, it was exempted from institutional ethics review and the requirement for individual informed consent under national public health regulations. All data were anonymised prior to analysis to protect patient confidentiality.\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThe WARIL project is funded by the Global Fund\u003c/p\u003e\u003cp\u003eDENV full genome sequencing was funded by the Africa CDC through the Africa PGI.\u003c/p\u003e\u003cp\u003eEthics Declaration\u003c/p\u003e\u003cp\u003eThis work was conducted as part of routine sentinel surveillance for arboviral diseases under the mandate of the National Public Health Agency (NPHA), Ministry of Health, Sierra Leone. All activities were performed in accordance with national surveillance and case investigation guidelines. As this activity was undertaken within the framework of public health surveillance, it was exempted from institutional ethics review and the requirement for individual informed consent under national public health regulations. All data were anonymized prior to analysis to protect patient confidentiality.\u003c/p\u003e\u003cp\u003eData availability\u003c/p\u003e\u003cp\u003eThe Dengue virus serotype 2 (F.1.1) genome sequence from this case report has been deposited in the GISAID database (Accession ID: EPI_ISL_20179260).\u003c/p\u003e\u003cp\u003eAcknowledgments\u003c/p\u003e\u003cp\u003eWe want to acknowledge the staff of Christ the King Hospital especially Beatrice Bangali and Alfred Kamara and Kenema Government Hospital team, especially Dr. Tejan Jalloh and Kelfala Konneh\u003c/p\u003e\u003cp\u003eConflict of interest\u003c/p\u003e\u003cp\u003eNo conflicts of interest.\u003c/p\u003e\u003cp\u003eDisclaimer\u003c/p\u003e\u003cp\u003eN/A\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eConceptualization,A.C., ; methodology A.C, I.O.; A.D.;J.S.O.C;M.N.;E.P software A.C;I.O, validation A.C, formal analysis A.C, I.O.; A.D.;J.S.O.C;M.N.;E.P investigation, A.C.; C.M.; A.J.W; N.D.S; J.C.;V.F.;S.M.; Z.S.; R.L.; M.T.K; A.K.; F.C.;T.J.;J.C; A.T.K;J.K.; resources D.H.;Z.K.; M.A.V; F.S.; C.T;S.T;A.K data curation A.O.K.C ; writing\u0026mdash;original draft preparation,A.C.,I.O.,J.S.O.C;A.D;M.N; writing\u0026mdash;review and editing,A.C.;I.O.;A.D.J.S.O.C;E.P;M.N;C.T;S.T;A.K visualization, I.O; supervision A.C;D.H.;B.D;B.S;A.S;Z.K;M.A.V;J.S.S;F.S project administration, A.C.F.C;D.H, funding acquisition B.D.A.K.;C.T; S.T;Y.K;. 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Emerg Infect Dis 10(10):1872\u0026ndash;1873. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3201/eid1010.031037\u003c/span\u003e\u003cspan address=\"10.3201/eid1010.031037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSupplementary Table 1 Notable amino acid annotations\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Global fund","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dengue virus, DENV-2, Sierra Leone, arbovirus surveillance, phylogenetics, genomic epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-7767082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7767082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDengue is the leading mosquito-borne viral cause of human illness and death. More than four billion people globally live at risk of Dengue Virus (DENV) infection and most infections are asymptomatic or present with a non-specific febrile illness. In this report the first laboratory-confirmed dengue in Sierra Leone was identified one month after the launch of the national Syndromic Sentinel Surveillance Strategy (4S). A febrile adult tested RT-PCR\u0026ndash;positive; we generated a near-complete genome assigned to DENV-2 genotype II, lineage F.1.1. Phylogenetically, the Sierra Leone genome formed a well-supported sister lineage with a 2024 USA genome, both nested within, but clearly diverged from, Indian sequences (2021\u0026ndash;2022) and distinct from the R\u0026eacute;union clade. The degree of divergence is incompatible with a recent or direct import from South Asia lineages. It is more consistent with diversification in an undersampled Indian-Ocean/South Asia network or via unsampled intermediates outside Asia. No canonical NS4B resistance substitutions were detected. With a single Sierra Leone genome, the source and extent of local transmission remain unresolved. These findings underscore the benefits of integrating differential diagnostics and genomics into routine febrile-illness care and sustaining regional arboviral surveillance.\u003c/p\u003e","manuscriptTitle":"First Report of Dengue Virus in Sierra Leone: Implications for Arbovirus Surveillance and Control","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:05:40","doi":"10.21203/rs.3.rs-7767082/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a4245d0-93f8-4c91-9e45-cba3bb7b4aea","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55678362,"name":"Epigenetics \u0026 Genomics"},{"id":55678363,"name":"Molecular Biology"}],"tags":[],"updatedAt":"2025-10-08T09:05:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:05:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7767082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7767082","identity":"rs-7767082","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}