High seroprevalence of yellow fever, dengue, and chikungunya viruses in the Greater Darfur region of Sudan: Implications for national health policy and surveillance

High seroprevalence of yellow fever, dengue, and chikungunya viruses in the Greater Darfur region of Sudan: Implications for national health policy and surveillance | 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 Short Report High seroprevalence of yellow fever, dengue, and chikungunya viruses in the Greater Darfur region of Sudan: Implications for national health policy and surveillance Nouh Saad Mohamed, Emmanuel Edwar Siddig, Abdualmoniem Omer Musa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4908948/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2024 Read the published version in BMC Research Notes → Version 1 posted 12 You are reading this latest preprint version Abstract Objectives Arboviruses pose a significant global health challenge. This study investigated the seroprevalence of major human arboviral infections, including yellow fever (YFV), dengue (DENV), Crimean-Congo hemorrhagic fever (CCHF), Rift Valley fever (RVFV), West Nile virus (WNV), and chikungunya (CHIKV), in the Darfur region from September to December 2018. ELISA-IgM was used to detect antibodies. RT‒PCR was used to confirm YFV infection in positive IgM samples. Results A total of 152 blood samples were collected, with 123 (80.9%) from males and 29 (19.1%) from females. The participants were grouped by age: 50 (32.9%) were under 20 years, 96 (63.2%) were aged 20–45 years, and 6 (3.9%) were over 45 years. The seroprevalence rates for YFV, DENV, and CHIKV were 68 (44.7%), 23 (15.1%), and 5 (3.3%), respectively. There were 11 confirmed YFV cases (7.2%) using RT-PCR. Among these, 3/11 were positive for DENV-IgM, and 1/11 was positive for CHIKV-IgM. Among the 68 YFV-positive individuals, 15 (22.1%) had been exposed to DENV, and 2 (2.9%) had been exposed to CHIKV. Coexposure to DENV and CHIKV was detected in 3 (1.9%) patients, while 2 (1.3%) patients had triple exposure to YFV, CHIKV, or DENV. No exposure to CCHF, RVFV, or WNV was detected. yellow fever dengue fever chikungunya virus arboviruses Darfur Sudan Figures Figure 1 Introduction The global public health risk of arthropod-borne viral diseases (arboviruses) is rapidly increasing, with these viruses expanding their geographical distribution at an alarming rate [ 1 ]. This exponential rise in arboviral infections is driven by several risk factors, including globalization and unplanned urbanization [ 2 ]; increased international travel and trade [ 3 , 4 ]; and environmental, ecological, and human practices that create favourable conditions for the breeding of competent arbovirus vectors and their increased contact with humans and animals [ 5 , 6 ]. Furthermore, climate change and armed conflicts are additional risk factors contributing to the rapid spread and prevalence of arboviral diseases [ 7 – 9 ] In Sudan, several major arboviruses are endemic, including yellow fever virus (YFV), dengue fever virus (DENV), Rift Valley fever virus (RVFV), Crimean-Congo hemorrhagic fever virus (CCHFV), West Nile virus (WNV), Chikungunya virus (CHIKV), and Zika virus (ZIKV) [ 7 ]. DENV is widespread across the country, whereas CHIKV and ZIKV are prevalent in East China, whereas YFV and WNV are prevalent in West China, and RVFV and CCHFV are prevalent in Central China [ 7 ]. Recently, DENV has emerged in Darfur along with CCHF and WNV [ 4 , 10 ]. Additionally, human arboviruses are becoming increasingly common in Sudan [ 11 ]. The recent increase in human population movements, including refugees, internally displaced persons (IDPs), war returnees, and humanitarian responders in the Darfur region, has brought more than peace to the war-torn to the communities in western Sudan [ 12 , 13 ]. Previously, the Darfur area was characterized by a humanitarian crisis of the post-conflict environment, with a health system that had yet to recover from the war that began in 2003 [ 14 , 15 ]. Most of the 13.4 million people in Darfur are IDPs, refugees, or war returnees living in humanitarian crisis settings [ 4 ]. These significant changes in social structure and living conditions have facilitated the emergence of several infectious diseases, particularly vector-borne viral and parasitic diseases, including dengue fever, CCHF, yellow fever, WNV, and malaria [ 10 , 16 , 17 ]. In recent years, several epidemics of arboviral diseases, including dengue fever, chikungunya, RVF, and yellow fever, have occurred in the Darfur region and neighboring areas [ 4 , 10 , 11 , 16 ]. The living conditions in refugee camps—characterized by a lack of stable water supply, high population density, increased exposure to infective vectors, and ineffective vector control—have favoured the introduction and establishment of competent vectors for arboviruses such as DENV, YFV, and WNV [ 16 , 18 , 19 ]. The limited resources and laboratory capacity in the region make the detection of arboviral infections particularly challenging, often leading to misdiagnosis of malaria on the basis of its clinical presentation [ 20 – 23 ]. In this report, we present the results of serological and molecular analyses of secondary data collected during an epidemic of febrile illness in the Darfur region. Methods Study design and study area This retrospective cross-sectional study analysed secondary data collected during an investigation of an epidemic of febrile illness in the Darfur region. The Darfur region, covering 493,180 km 2 of desert and semidesert, is divided into 5 states: East, West, South, North, and Central Darfur. The prolonged armed conflict in the Darfur region severely disrupted the socioeconomic structure of local communities, altered the environment, and transformed the area into a humanitarian crisis zone. This turmoil has forced the local population to abandon their destroyed homes and villages. As a result of these environmental and socioeconomic changes, many have become IDPs or refugees, fleeing to neighboring countries such as South Sudan, the Central African Republic, China, and Libya, in search of safety and sustenance for themselves and their livestock. Nearly all of the region’s 13.4 million inhabitants currently reside in the camps of densely populated refugees and IDPs [ 4 , 10 ]. Sample collection Blood samples were collected during a survey investigating a febrile illness epidemic that occurred in late 2018. Blood samples were collected from febrile patients at outpatient clinics within refugee and IDP camps. These patients, who tested negative for malaria, had experienced febrile illness within the previous 2–3 weeks. Sera were separated from the blood samples and stored at -20°C until they were shipped to the National Public Health Laboratory in Khartoum for further analysis. All the data were anonymised, and personal identifiers were removed to ensure confidentiality. Laboratory testing Serum samples were tested for antibodies against DENV, RVFV, CCHFV, YFV, and CHIKV via commercially available IgM capture ELISA kits following the manufacturer’s instructions (Panbio, Inverness Medical Innovations Australia Pty Ltd., Brisbane, Australia). Additionally, an RT‒PCR confirmatory test was performed on samples positive for YFV-IgM via the RealStar® yellow fever RT‒PCR Kit (Altona Diagnostics GmbH, Hamburg, Germany) to reduce the bias of false-positive results due to previous YFV vaccination campaigns in the region. Statistical analysis The data were analysed, and the frequencies of the variables were calculated via the Statistical Package for the Social Sciences (SPSS v20). Frequencies were calculated for variables, including age group, sex, and laboratory test results. Results Blood samples were collected from 152 febrile patients who tested negative for malaria parasites through microscopic examination. The samples were evenly distributed among 4 states—North, South, West, and Central Darfur—with only 6 samples (4%) obtained from East Darfur state. The majority of the patients, 123 (80.9%), were males. One-third of all patients were children younger than 20 years of age, 96 (63.2%) were between 20 and 45 years, and only 6 (3.9%) were older than 45 years (Table 1 ). Table 1 Shows the demographics and laboratory tests results of the study participants Characteristics State Total Central Darfur East Darfur North Darfur South Darfur West Darfur Sex Female 6 (20%) 6 (100%) 4 (11.4%) 7 (13.5%) 6 (20.7%) 29 (19.1%) Male 24 (80%) 0 (0%) 31 (88.6%) 45 (86.5%) 23 (79.3%) 123 (80.9%) Age Group > 20 years 11 (36.7%) 4 (66.7%) 7 (20%) 15 (28.8%) 13 (44.8%) 50 (32.9%) 20–45 years 19 (63.3%) 2 (33.3%) 26 (74.3%) 35 (67.3%) 14 (48.3%) 96 (63.2%) < 45 years 0 (0%) 0 (0%) 2 (5.7%) 2 (3.8%) 2 (6.9%) 6 (3.9%) YFV-IgM Positive 14 (46.7%) 4 (66.7%) 10 (28.6%) 21 (40.4%) 19 (65.5%) 68 (44.7%) Negative 16 (53.3%) 2 (33.3%) 25 (71.4%) 31 (59.6%) 10 (34.5%) 84 (55.3%) YFV-RT-PCR Positive 2 (6.7%) 1 (16.7%) 2 (5.7%) 1 (1.9%) 5 (17.2%) 11 (7.2%) Negative 28 (93.3%) 5 (83.3%) 33 (94.3%) 51 (98.1%) 24 (82.8%) 141 (92.8%) CICKV-IgM Positive 1 (3.3%) 0 (0%) 0 (0%) 4 (7.7%) 0 (0%) 5 (3.3%) Negative 29 (96.7%) 6 (100%) 35 (100%) 48 (92.3%) 29 (100%) 147 (96.7%) DENV-IgM Positive 4 (13.3%) 0 (0%) 4 (11.4%) 8 (15.4%) 7 (24.1%) 23 (15.1%) Negative 26 (86.7%) 6 (100%) 31 (88.6%) 44 (84.6%) 22 (75.9%) 129 (84.9%) Total 30 (20%) 6 (4%) 35 (23%) 52 (34%) 29 (19%) 152 (100%) Serological and molecular analyses of the serum samples revealed widespread exposure to YFV across all Darfur states, with 68 (44.7%) of the samples indicating previous exposure to YFV, primarily from South and West Darfur states: 21 (40.4%) and 19 (65.5%), respectively. The RT‒PCR assay confirmed 11 recent YFV infections (Table 1 ). Additionally, five participants had previously been exposed to CHIKV, with cases originating from southern and central Darfur states (Table 1 ). The seroprevalence of DENV antibodies was 15.1%, with 23 positive individuals detected across four of the five states, except East Darfur (Table 1 ). No prior exposure to RVFV or CCHFV was detected. Interestingly, co-exposure to two or more of these viruses was notably prevalent. Among the patients with recent YFV infections, three patients were also positive for DENV-IgM, and one was positive for CHIKV-IgM. Among the 68 individuals with YFV antibodies, 15 individuals had also been exposed to DENV, and two had been exposed to CHIKV. Additionally, co-exposure to both DENV and CHIKV was detected in three participants. Notably, two cases of triple exposure to YFV, CHIKV, and DENV were identified through IgM testing (Table 2 ). Table 2 Co-exposure to Yellow fever, Dengue, and Chikungunya viruses. Virus infections/exposure YFV-RT-PCR YFV-IgM CHIKV-IgM DENV-IgM Positive all IgM Negative all IgM YFV-RT-PCR 11 6 1 3 - - YFV-IgM 6 68 2 15 - - CHIKV-IgM 1 2 5 3 - - DENV-IgM 3 15 3 23 - - Positive all IgM - - - - 2 - Negative all IgM - - - - - 0 Geospatial analysis of seroprevalence by state revealed that populations in Central Darfur and South Darfur were exposed to YFV, DENV, and CHIKV, whereas populations in West Darfur and North Darfur were exposed to YFV and DENV. In contrast, only exposure to YFV was detected in East Darfur state (Fig. 1 ). Discussion The findings of this study are alarming, as they present the first report of the cocirculation of YFV, CHIKV, and DENV in the Greater Darfur region. The high seroprevalence of YFV antibodies was anticipated because of the previous vaccination campaign following the 2012 YFV epidemic [ 16 ]. However, the detection of a few YFV cases via RT‒PCR suggests that the virus is still actively circulating in the area. This persistence of YFV could be attributed to the significant cross-border movement between vaccinated and nonvaccine-susceptible areas due to conflict and political instability, which may have reduced vaccination coverage among residents [ 24 , 15 ]. Alternatively, YFV might be maintained within populations of nonhuman primates [ 25 ]. DENV emerged in the area in 2015, starting in North Darfur, and soon after, an epidemic spread across Central, North, South, and West Darfur states, during which the co-transmission of WNV and CCHFV has also been documented [ 4 , 10 ]. Additionally, a massive outbreak of CHIKV fever occurred in eastern Sudan between May 2018 and March 2019, with over 47,000 cases reported [ 7 ]. The region's heightened population dynamics, cross-border movement, trade, and influx of refugees contribute to its status as a hotspot for multiple vector-borne diseases, leading to annual outbreaks [ 26 – 29 ]. The low seroprevalence of CHIKV antibodies detected in this study suggests either that exposure to CHIKV might have occurred in East Sudan (with CHIKV-positive individuals being returnees) or that there is independent and unrecognized local transmission of CHIKV in the area [ 30 , 31 ]. The latter hypothesis is more likely, given the detection of major arboviral disease vector in the region; Aedes albopictus [ 18 ]. The emergence and re-emergence of arboviral diseases pose a significant public health challenge, particularly in resource-limited settings with relatively weak health systems, where clinicians often rely heavily on clinical diagnosis [ 4 , 10 ]. This reliance has resulted in numerous arboviral infections being misdiagnosed and treated as malaria [ 4 , 10 , 32 ]. The lack of publicly available up-to-date information on circulating diseases further limits the quality and capacity of healthcare services, as clinical diagnosis is heavily influenced by a clinician’s awareness of endemic diseases, as well as the health and travel history of patients [ 7 , 33 ]. Also, coinfections with other parasitic infections, such as malaria, which are also characterized by fever, further complicates diagnosis. Without adequate laboratory diagnostic capacity to detect concurrent viral infections, especially when signs and symptoms become more severe and complicated, accurate diagnosis becomes even more challenging [ 21 , 22 , 34 ]. Additionally, the absence of a robust arboviral disease surveillance system significantly increases the risk of future epidemics [ 7 ]. To effectively combat arboviral diseases, it is crucial to establish a nationwide surveillance system that integrates both human and vector surveillance, with a strong focus on vector control. This system should include regular entomological surveys to monitor vector populations, assess their susceptibility to control measures, and detect the presence of arboviruses [ 35 , 36 ]. Strengthening diagnostic capacity across national and regional laboratories is essential for accurate and rapid diagnosis, particularly in high-risk areas like refugee and IDP camps [ 37 , 38 ]. Implementing standardized data collection and reporting systems will enable timely detection and response to outbreaks [ 39 ]. Building local capacity through ongoing training for healthcare workers, entomologists, and public health professionals is necessary, alongside community engagement to raise awareness about prevention and early detection [ 40 ]. Continuous monitoring and evaluation are key to ensuring the system's sustainability and effectiveness in controlling arboviral diseases. In areas endemic for multiple infectious diseases, the use of molecular and serological diagnostic tests is particularly imperative, as symptoms can be similar and microscopic tests cannot detect viral infections [ 41 ]. Therefore, the country would benefit greatly from rebuilding its health system, with a focus on primary healthcare, by improving diagnostic capacity, surveillance, and reporting systems [ 12 , 33 , 39 ]. Moreover, adopting a One Health approach would be instrumental in the early detection of and response to outbreaks of arboviral diseases [ 36 , 40 ]. The need for this approach is underscored by the increasing frequency of epidemics and epizootics of zoonotic arboviral diseases such as RVF, especially given the recent spatiotemporal changes in disease transmission [ 8 , 9 ]. Arboviral epidemics are rapidly growing and expanding their geographical reach globally. In the absence of effective and sensitive surveillance systems for the early detection of arboviral disease outbreaks, these outbreaks will escalate into global threats. We, therefore, emphasize the urgent need for a nationwide surveillance system for arboviral diseases in Sudan. While the current local capacity is limited due to resource constraints, international support for building local health capacity and preparedness for the early detection and containment of arboviral epidemics is worth the global investment to avoid the emergence of larger multi-country pandemics. We also urge local and international health partners to support efforts in increasing local diagnostic capacity, surveillance, reporting, prevention and control of arboviruses. Special attention should also be given to the health of displaced persons living in overcrowded refugee and IDP camps. Limitations A major limitation of this study is the lack of participants travel history, entomological and disease vector-related data, which should be considered in future research. Understanding vector composition, their role in disease transmission, and their susceptibility to current vector control measures is essential for effective intervention planning. Abbreviations Arboviruses arthropod-borne viral diseases YFV yellow fever virus DENV Dengue fever virus RVFV Rift Valley fever virus RVFV Rift Valley fever virus CCHF Crimean–Congo hemorrhagic fever CCHFV Crimean–Congo hemorrhagic fever virus WNV West Nile virus CHIKV Chikungunya virus ZIKV Zika virus Declarations Ethical approval and consent to participate Ethical approval was obtained from the National Health Research Ethics Committee, Federal Ministry of Health, Khartoum, Sudan. The requirement for informed consent was waived, as this study exclusively utilized retrospective secondary data from epidemiological reports. Consent for publication Not applicable. Availability of data and materials All the data generated or analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests . Funding Not applicable. Authors' contributions Conceptualization, design, and investigation: AA, AE; Data acquisition: AA, AE; Formal analysis and interpretation: NSM, AE, and AA; Editing and writing: AA, NSM, EES, AOM, and AE; Original draft preparation: NSM and AA. All the authors have read and approved the final manuscript. Acknowledgements We would like to thank the local communities for their cooperation and our colleagues at the State Ministries of Health for their help in sample collection and shipment. Additionally, we thank our colleagues at the National Public Health Laboratory for their help in the analysis. References Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2024 Read the published version in BMC Research Notes → Version 1 posted Editorial decision: Revision requested 30 Sep, 2024 Reviews received at journal 30 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviewers agreed at journal 12 Sep, 2024 Reviewers agreed at journal 10 Sep, 2024 Reviews received at journal 08 Sep, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviewers invited by journal 16 Aug, 2024 Editor invited by journal 14 Aug, 2024 Editor assigned by journal 14 Aug, 2024 Submission checks completed at journal 14 Aug, 2024 First submitted to journal 13 Aug, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4908948","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":352154406,"identity":"231382a9-2b6f-4201-bb6a-d5dcc55d6325","order_by":0,"name":"Nouh Saad Mohamed","email":"","orcid":"","institution":"Sirius Training and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Nouh","middleName":"Saad","lastName":"Mohamed","suffix":""},{"id":352154407,"identity":"6dc22e7c-268d-4fde-a0bc-fa36c7b9ac0a","order_by":1,"name":"Emmanuel Edwar Siddig","email":"","orcid":"","institution":"University of Khartoum","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"Edwar","lastName":"Siddig","suffix":""},{"id":352154408,"identity":"dbbe5efc-bcaa-47aa-a674-47921192a715","order_by":2,"name":"Abdualmoniem Omer Musa","email":"","orcid":"","institution":"University of Kassala","correspondingAuthor":false,"prefix":"","firstName":"Abdualmoniem","middleName":"Omer","lastName":"Musa","suffix":""},{"id":352154409,"identity":"f0a60fc1-41cf-4964-b9da-929cb2d6c124","order_by":3,"name":"Adel Elduma","email":"","orcid":"","institution":"National Public Health Laboratory, Federal Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Adel","middleName":"","lastName":"Elduma","suffix":""},{"id":352154410,"identity":"6f1c9984-450b-4f07-b923-efd5e740c5e0","order_by":4,"name":"Ayman Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYJADNiBmlgOxDjwgRYsxWEsCKVoSG0BMfFr4Z2Qnfi5gsIvmZ2B+9uhGhXX6/LDDD4G22MnpNmDXInEjd7P0DIbk3JkNbObGOWfSczfeTjMAakk2NjuAw5obuRukeRiYczccYDCTzm07nLtxdgJIy4HEbTi0yANt+c3DUJ+7/wD7N+ncf4fTDWenf8CrxeBG7jagLYdzNzDwAG1pOJwgL52D3xbDM2+3WfMYHM+dcZinTDrnWLrhBumcggMJBrj9Inc8d/Ntnorq3P729m3SOTXW8vKz0zd/+FBhJ4fT+wIJIOcBMTPMqWCVBjiUgwA/ulnyDXhUj4JRMApGwYgEAKZgX9vOb/e+AAAAAElFTkSuQmCC","orcid":"","institution":"Rwanda Biomedical Centre","correspondingAuthor":true,"prefix":"","firstName":"Ayman","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2024-08-13 18:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4908948/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4908948/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13104-024-07067-1","type":"published","date":"2024-12-26T15:57:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66378869,"identity":"96ecfcf2-f40a-470e-8b51-d6511d06b532","added_by":"auto","created_at":"2024-10-11 06:40:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102211,"visible":true,"origin":"","legend":"\u003cp\u003eSudan map highlight the seroprevalence of YFV, DENV, and CHIKV in the Greater Darfur region.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4908948/v1/84cba041c2ce43c80ae99169.png"},{"id":72640406,"identity":"79358fff-25fa-451e-a865-f65d90deb925","added_by":"auto","created_at":"2024-12-30 16:05:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":587683,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4908948/v1/90c62630-b27e-4527-96f2-de84521553b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High seroprevalence of yellow fever, dengue, and chikungunya viruses in the Greater Darfur region of Sudan: Implications for national health policy and surveillance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global public health risk of arthropod-borne viral diseases (arboviruses) is rapidly increasing, with these viruses expanding their geographical distribution at an alarming rate [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This exponential rise in arboviral infections is driven by several risk factors, including globalization and unplanned urbanization [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; increased international travel and trade [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; and environmental, ecological, and human practices that create favourable conditions for the breeding of competent arbovirus vectors and their increased contact with humans and animals [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, climate change and armed conflicts are additional risk factors contributing to the rapid spread and prevalence of arboviral diseases [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn Sudan, several major arboviruses are endemic, including yellow fever virus (YFV), dengue fever virus (DENV), Rift Valley fever virus (RVFV), Crimean-Congo hemorrhagic fever virus (CCHFV), West Nile virus (WNV), Chikungunya virus (CHIKV), and Zika virus (ZIKV) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. DENV is widespread across the country, whereas CHIKV and ZIKV are prevalent in East China, whereas YFV and WNV are prevalent in West China, and RVFV and CCHFV are prevalent in Central China [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recently, DENV has emerged in Darfur along with CCHF and WNV [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, human arboviruses are becoming increasingly common in Sudan [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe recent increase in human population movements, including refugees, internally displaced persons (IDPs), war returnees, and humanitarian responders in the Darfur region, has brought more than peace to the war-torn to the communities in western Sudan [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Previously, the Darfur area was characterized by a humanitarian crisis of the post-conflict environment, with a health system that had yet to recover from the war that began in 2003 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Most of the 13.4\u0026nbsp;million people in Darfur are IDPs, refugees, or war returnees living in humanitarian crisis settings [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These significant changes in social structure and living conditions have facilitated the emergence of several infectious diseases, particularly vector-borne viral and parasitic diseases, including dengue fever, CCHF, yellow fever, WNV, and malaria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, several epidemics of arboviral diseases, including dengue fever, chikungunya, RVF, and yellow fever, have occurred in the Darfur region and neighboring areas [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The living conditions in refugee camps\u0026mdash;characterized by a lack of stable water supply, high population density, increased exposure to infective vectors, and ineffective vector control\u0026mdash;have favoured the introduction and establishment of competent vectors for arboviruses such as DENV, YFV, and WNV [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The limited resources and laboratory capacity in the region make the detection of arboviral infections particularly challenging, often leading to misdiagnosis of malaria on the basis of its clinical presentation [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this report, we present the results of serological and molecular analyses of secondary data collected during an epidemic of febrile illness in the Darfur region.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy design and study area\u003c/h2\u003e \u003cp\u003eThis retrospective cross-sectional study analysed secondary data collected during an investigation of an epidemic of febrile illness in the Darfur region. The Darfur region, covering 493,180 km\u003csup\u003e2\u003c/sup\u003e of desert and semidesert, is divided into 5 states: East, West, South, North, and Central Darfur. The prolonged armed conflict in the Darfur region severely disrupted the socioeconomic structure of local communities, altered the environment, and transformed the area into a humanitarian crisis zone. This turmoil has forced the local population to abandon their destroyed homes and villages. As a result of these environmental and socioeconomic changes, many have become IDPs or refugees, fleeing to neighboring countries such as South Sudan, the Central African Republic, China, and Libya, in search of safety and sustenance for themselves and their livestock. Nearly all of the region\u0026rsquo;s 13.4\u0026nbsp;million inhabitants currently reside in the camps of densely populated refugees and IDPs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eBlood samples were collected during a survey investigating a febrile illness epidemic that occurred in late 2018. Blood samples were collected from febrile patients at outpatient clinics within refugee and IDP camps. These patients, who tested negative for malaria, had experienced febrile illness within the previous 2\u0026ndash;3 weeks. Sera were separated from the blood samples and stored at -20\u0026deg;C until they were shipped to the National Public Health Laboratory in Khartoum for further analysis. All the data were anonymised, and personal identifiers were removed to ensure confidentiality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory testing\u003c/h2\u003e \u003cp\u003eSerum samples were tested for antibodies against DENV, RVFV, CCHFV, YFV, and CHIKV via commercially available IgM capture ELISA kits following the manufacturer\u0026rsquo;s instructions (Panbio, Inverness Medical Innovations Australia Pty Ltd., Brisbane, Australia). Additionally, an RT‒PCR confirmatory test was performed on samples positive for YFV-IgM via the RealStar\u0026reg; yellow fever RT‒PCR Kit (Altona Diagnostics GmbH, Hamburg, Germany) to reduce the bias of false-positive results due to previous YFV vaccination campaigns in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were analysed, and the frequencies of the variables were calculated via the Statistical Package for the Social Sciences (SPSS v20). Frequencies were calculated for variables, including age group, sex, and laboratory test results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBlood samples were collected from 152 febrile patients who tested negative for malaria parasites through microscopic examination. The samples were evenly distributed among 4 states\u0026mdash;North, South, West, and Central Darfur\u0026mdash;with only 6 samples (4%) obtained from East Darfur state. The majority of the patients, 123 (80.9%), were males. One-third of all patients were children younger than 20 years of age, 96 (63.2%) were between 20 and 45 years, and only 6 (3.9%) were older than 45 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShows the demographics and laboratory tests results of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral Darfur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEast Darfur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorth Darfur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth Darfur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWest Darfur\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (86.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (79.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e123 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;45 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYFV-IgM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84 (55.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYFV-RT-PCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (93.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (94.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (98.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e141 (92.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCICKV-IgM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e147 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDENV-IgM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (84.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e129 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e152 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSerological and molecular analyses of the serum samples revealed widespread exposure to YFV across all Darfur states, with 68 (44.7%) of the samples indicating previous exposure to YFV, primarily from South and West Darfur states: 21 (40.4%) and 19 (65.5%), respectively. The RT‒PCR assay confirmed 11 recent YFV infections (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, five participants had previously been exposed to CHIKV, with cases originating from southern and central Darfur states (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The seroprevalence of DENV antibodies was 15.1%, with 23 positive individuals detected across four of the five states, except East Darfur (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No prior exposure to RVFV or CCHFV was detected.\u003c/p\u003e \u003cp\u003eInterestingly, co-exposure to two or more of these viruses was notably prevalent. Among the patients with recent YFV infections, three patients were also positive for DENV-IgM, and one was positive for CHIKV-IgM. Among the 68 individuals with YFV antibodies, 15 individuals had also been exposed to DENV, and two had been exposed to CHIKV. Additionally, co-exposure to both DENV and CHIKV was detected in three participants. Notably, two cases of triple exposure to YFV, CHIKV, and DENV were identified through IgM testing (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCo-exposure to Yellow fever, Dengue, and Chikungunya viruses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVirus infections/exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYFV-RT-PCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYFV-IgM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCHIKV-IgM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDENV-IgM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive all IgM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNegative all IgM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYFV-RT-PCR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYFV-IgM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHIKV-IgM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDENV-IgM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePositive all IgM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNegative all IgM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGeospatial analysis of seroprevalence by state revealed that populations in Central Darfur and South Darfur were exposed to YFV, DENV, and CHIKV, whereas populations in West Darfur and North Darfur were exposed to YFV and DENV. In contrast, only exposure to YFV was detected in East Darfur state (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study are alarming, as they present the first report of the cocirculation of YFV, CHIKV, and DENV in the Greater Darfur region. The high seroprevalence of YFV antibodies was anticipated because of the previous vaccination campaign following the 2012 YFV epidemic [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the detection of a few YFV cases via RT‒PCR suggests that the virus is still actively circulating in the area. This persistence of YFV could be attributed to the significant cross-border movement between vaccinated and nonvaccine-susceptible areas due to conflict and political instability, which may have reduced vaccination coverage among residents [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Alternatively, YFV might be maintained within populations of nonhuman primates [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDENV emerged in the area in 2015, starting in North Darfur, and soon after, an epidemic spread across Central, North, South, and West Darfur states, during which the co-transmission of WNV and CCHFV has also been documented [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, a massive outbreak of CHIKV fever occurred in eastern Sudan between May 2018 and March 2019, with over 47,000 cases reported [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The region's heightened population dynamics, cross-border movement, trade, and influx of refugees contribute to its status as a hotspot for multiple vector-borne diseases, leading to annual outbreaks [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The low seroprevalence of CHIKV antibodies detected in this study suggests either that exposure to CHIKV might have occurred in East Sudan (with CHIKV-positive individuals being returnees) or that there is independent and unrecognized local transmission of CHIKV in the area [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The latter hypothesis is more likely, given the detection of major arboviral disease vector in the region; \u003cem\u003eAedes albopictus\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe emergence and re-emergence of arboviral diseases pose a significant public health challenge, particularly in resource-limited settings with relatively weak health systems, where clinicians often rely heavily on clinical diagnosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This reliance has resulted in numerous arboviral infections being misdiagnosed and treated as malaria [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The lack of publicly available up-to-date information on circulating diseases further limits the quality and capacity of healthcare services, as clinical diagnosis is heavily influenced by a clinician\u0026rsquo;s awareness of endemic diseases, as well as the health and travel history of patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Also, coinfections with other parasitic infections, such as malaria, which are also characterized by fever, further complicates diagnosis. Without adequate laboratory diagnostic capacity to detect concurrent viral infections, especially when signs and symptoms become more severe and complicated, accurate diagnosis becomes even more challenging [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, the absence of a robust arboviral disease surveillance system significantly increases the risk of future epidemics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo effectively combat arboviral diseases, it is crucial to establish a nationwide surveillance system that integrates both human and vector surveillance, with a strong focus on vector control. This system should include regular entomological surveys to monitor vector populations, assess their susceptibility to control measures, and detect the presence of arboviruses [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Strengthening diagnostic capacity across national and regional laboratories is essential for accurate and rapid diagnosis, particularly in high-risk areas like refugee and IDP camps [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Implementing standardized data collection and reporting systems will enable timely detection and response to outbreaks [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Building local capacity through ongoing training for healthcare workers, entomologists, and public health professionals is necessary, alongside community engagement to raise awareness about prevention and early detection [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Continuous monitoring and evaluation are key to ensuring the system's sustainability and effectiveness in controlling arboviral diseases.\u003c/p\u003e \u003cp\u003eIn areas endemic for multiple infectious diseases, the use of molecular and serological diagnostic tests is particularly imperative, as symptoms can be similar and microscopic tests cannot detect viral infections [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Therefore, the country would benefit greatly from rebuilding its health system, with a focus on primary healthcare, by improving diagnostic capacity, surveillance, and reporting systems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, adopting a One Health approach would be instrumental in the early detection of and response to outbreaks of arboviral diseases [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The need for this approach is underscored by the increasing frequency of epidemics and epizootics of zoonotic arboviral diseases such as RVF, especially given the recent spatiotemporal changes in disease transmission [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eArboviral epidemics are rapidly growing and expanding their geographical reach globally. In the absence of effective and sensitive surveillance systems for the early detection of arboviral disease outbreaks, these outbreaks will escalate into global threats. We, therefore, emphasize the urgent need for a nationwide surveillance system for arboviral diseases in Sudan. While the current local capacity is limited due to resource constraints, international support for building local health capacity and preparedness for the early detection and containment of arboviral epidemics is worth the global investment to avoid the emergence of larger multi-country pandemics. We also urge local and international health partners to support efforts in increasing local diagnostic capacity, surveillance, reporting, prevention and control of arboviruses. Special attention should also be given to the health of displaced persons living in overcrowded refugee and IDP camps.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA major limitation of this study is the lack of participants travel history, entomological and disease vector-related data, which should be considered in future research. Understanding vector composition, their role in disease transmission, and their susceptibility to current vector control measures is essential for effective intervention planning.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eArboviruses\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earthropod-borne viral diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYFV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eyellow fever virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDENV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDengue fever virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVFV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRift Valley fever virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVFV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRift Valley fever virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrimean\u0026ndash;Congo hemorrhagic fever\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCHFV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCrimean\u0026ndash;Congo hemorrhagic fever virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWest Nile virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHIKV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChikungunya virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZIKV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZika virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the National Health Research Ethics Committee, Federal Ministry of Health, Khartoum, Sudan. The requirement for informed consent was waived,\u0026nbsp;as this study exclusively utilized retrospective secondary data from epidemiological reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot\u0026nbsp;applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;the\u0026nbsp;data generated or analysed during this study are included in this published article.\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\u003cstrong\u003e\u003cem\u003e.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot\u0026nbsp;applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, design, and investigation: AA, AE; Data acquisition: AA, AE; Formal analysis and interpretation: NSM, AE, and AA; Editing and writing:\u0026nbsp;AA, NSM, EES, AOM, and AE; Original draft preparation:\u0026nbsp;NSM and AA.\u0026nbsp;All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the local communities for their cooperation and our colleagues at the State Ministries of Health for their help in sample collection and shipment. Additionally, we thank our colleagues at the National Public Health Laboratory for their help in the analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496:504\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGubler DJ, Dengue. Urbanization and Globalization: The Unholy Trinity of the 21st Century. Trop Med Health. 2011;39:S3\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalstead SB. Travelling arboviruses: A historical perspective. Travel Med Infect Dis. 2019;:101471.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed A, Ali Y, Elmagboul B, Mohamed O, Elduma A, Bashab H, et al. Dengue Fever in the Darfur Area, Western Sudan. Emerg Infect Dis. 2019;25:2126.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeaver SC. 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Pan Afr Med J. 2014;19.\u003c/span\u003e\u003c/li\u003e\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":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"yellow fever, dengue fever, chikungunya virus, arboviruses, Darfur, Sudan","lastPublishedDoi":"10.21203/rs.3.rs-4908948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4908948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eArboviruses pose a significant global health challenge. This study investigated the seroprevalence of major human arboviral infections, including yellow fever (YFV), dengue (DENV), Crimean-Congo hemorrhagic fever (CCHF), Rift Valley fever (RVFV), West Nile virus (WNV), and chikungunya (CHIKV), in the Darfur region from September to December 2018. ELISA-IgM was used to detect antibodies. RT‒PCR was used to confirm YFV infection in positive IgM samples.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 152 blood samples were collected, with 123 (80.9%) from males and 29 (19.1%) from females. The participants were grouped by age: 50 (32.9%) were under 20 years, 96 (63.2%) were aged 20\u0026ndash;45 years, and 6 (3.9%) were over 45 years. The seroprevalence rates for YFV, DENV, and CHIKV were 68 (44.7%), 23 (15.1%), and 5 (3.3%), respectively. There were 11 confirmed YFV cases (7.2%) using RT-PCR. Among these, 3/11 were positive for DENV-IgM, and 1/11 was positive for CHIKV-IgM. Among the 68 YFV-positive individuals, 15 (22.1%) had been exposed to DENV, and 2 (2.9%) had been exposed to CHIKV. Coexposure to DENV and CHIKV was detected in 3 (1.9%) patients, while 2 (1.3%) patients had triple exposure to YFV, CHIKV, or DENV. No exposure to CCHF, RVFV, or WNV was detected.\u003c/p\u003e","manuscriptTitle":"High seroprevalence of yellow fever, dengue, and chikungunya viruses in the Greater Darfur region of Sudan: Implications for national health policy and surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-11 06:40:01","doi":"10.21203/rs.3.rs-4908948/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-30T16:24:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-30T11:15:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T23:16:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182179591105218652236475343700245558042","date":"2024-09-12T22:21:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221037872046470863679503933648244911758","date":"2024-09-10T16:23:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T03:41:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62354800785976834370075907395214277733","date":"2024-08-18T17:22:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-16T12:41:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-14T10:41:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-14T07:01:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-14T07:01:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2024-08-13T18:21:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f67bfc41-07a3-43bf-9f97-3a2ff3b964e7","owner":[],"postedDate":"October 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T15:59:16+00:00","versionOfRecord":{"articleIdentity":"rs-4908948","link":"https://doi.org/10.1186/s13104-024-07067-1","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2024-12-26 15:57:06","publishedOnDateReadable":"December 26th, 2024"},"versionCreatedAt":"2024-10-11 06:40:01","video":"","vorDoi":"10.1186/s13104-024-07067-1","vorDoiUrl":"https://doi.org/10.1186/s13104-024-07067-1","workflowStages":[]},"version":"v1","identity":"rs-4908948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4908948","identity":"rs-4908948","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}