Uncovering the Drivers of Ebola Virus Disease Resurgence in DRC: A Root Cause Analysis of the 16th Outbreak in Mwaka, Kasai Province (2025)

Uncovering the Drivers of Ebola Virus Disease Resurgence in DRC: A Root Cause Analysis of the 16th Outbreak in Mwaka, Kasai Province (2025) | 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 comment Uncovering the Drivers of Ebola Virus Disease Resurgence in DRC: A Root Cause Analysis of the 16th Outbreak in Mwaka, Kasai Province (2025) Jean Paul Muambangu Milambo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7543616/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 Background: The Democratic Republic of the Congo (DRC) experienced its 16th Ebola Virus Disease (EVD) outbreak in 2025, centered in the Bulape Health Zone of Kasai Province. This outbreak occurred amid multiple concurrent epidemics and in a region with limited health infrastructure. Genomic sequencing revealed a new zoonotic spillover, genetically related to the 1976 Yambuku strain. Methods: A Root Cause Analysis (RCA) was conducted using the “5 Whys” framework, integrating epidemiological data, genomic analysis, and surveillance reports. Key contributing factors to delayed detection and response were identified. Comparative insights were drawn from the 2018–2020 North Kivu EVD outbreak. Results: The outbreak resulted in 28 confirmed, probable, or suspected cases and 15 deaths, including four healthcare workers. Root causes included poor ecological surveillance, weak community alert systems, diagnostic delays, health system overload from concurrent outbreaks, and structural underfunding. These factors contrast with North Kivu, where response delays were driven more by security issues. Conclusions: The 2025 Mwaka outbreak highlights how ecological and systemic vulnerabilities allow novel Ebola spillovers to escalate. Effective future preparedness will require sustained investment in One Health surveillance, decentralized diagnostics, and resilient public health governance. Ebola virus disease zoonotic spillover surveillance DRC diagnostics health systems outbreak response Background Ebola Virus Disease (EVD) remains a significant threat to global public health. Although its outbreaks are mostly localized to Africa, the 2014–2016 West African epidemic highlighted EVD’s potential to cause international crises. Its high mortality rate, risk of international spread, and requirement for high-level containment have made it a WHO priority disease for research and response [1]. In North America, significant investments have been made into EVD vaccine research and deployment, such as the development and use of the rVSV-ZEBOV vaccine. Agencies like the U.S. Centers for Disease Control and Prevention (CDC) and the Public Health Agency of Canada (PHAC) have also contributed expert teams and resources during major outbreaks in Africa [1]. Australia has played a role primarily through funding and international health deployments, supporting WHO emergency response missions and vaccine development [2]. In Asia, countries like China and India have extended logistical and technical support, and engaged in research collaboration and construction of healthcare infrastructure in EVD-affected regions [3]. Europe’s response includes field deployments by Médecins Sans Frontières (MSF), genomic surveillance by the European Centre for Disease Prevention and Control (ECDC), and major sequencing and bioinformatics contributions from institutions such as the Institute of Tropical Medicine (ITM) in Antwerp [5]. Africa remains the epicenter of EVD, with the Democratic Republic of the Congo (DRC) reporting 16 outbreaks since the virus was first discovered in 1976. Despite this history, the country continues to face challenges in surveillance, diagnostics, and health system resilience. The 16th outbreak in Mwaka (2025) occurred in the context of simultaneous public health emergencies—namely, mpox, cholera, and malaria—highlighting gaps in multi-outbreak management capacity [4]. Aim This Root Cause Analysis (RCA) aims to systematically identify and understand the upstream factors and operational failures that led to the resurgence of EVD in Mwaka, Kasai Province (2025). The findings are intended to inform sustainable health systems strengthening, outbreak preparedness, and response strategies in DRC and comparable settings. Methods Design and Framework The RCA was conducted using the “5 Whys” method integrated with systems thinking to investigate upstream and system-level drivers of the outbreak. Data sources included Ministry of Health reports, laboratory data from the Institut National de Recherche Biomédicale (INRB), WHO bulletins, and peer-reviewed genomic and epidemiological publications [4], [6], [7]. Laboratory and Bioinformatics Approaches Laboratory confirmation involved molecular diagnostics using GeneXpert, the BioFire Global Fever Panel, and the Altona RealStar Filovirus RT-PCR Kit [4]. Positive samples were sequenced on an Oxford Nanopore GridION system using R10.4.1 flow cells. The sequencing produced a 99.97% complete genome, with a 99.52% match to the 1976 Yambuku-Mayinga strain [4]. Bioinformatics tools included iVar for consensus genome generation, MAFFT for multiple sequence alignment [6], and IQ-TREE for phylogenetic inference [7]. Ethics Statement This Root Cause Analysis was based on data collected through routine public health surveillance activities during an officially declared outbreak. All genomic sequencing and clinical data were anonymized in compliance with DRC national health policies and reviewed by the INRB and the Ministry of Public Health. The analysis was conducted under ethical guidelines provided by the DRC National Health Ethics Committee. No personally identifiable information was used, and genomic data are shared under pre-publication agreements [4]. Results A detailed root cause analysis (see Table 1 ) identifies several critical weaknesses that contributed to the 16th Ebola Virus Disease (EVD) outbreak in the Democratic Republic of Congo (DRC). The outbreak likely originated from a zoonotic spillover event, evidenced by a 99.52% genetic similarity to the 1976 Yambuku strain and no linkage to recent human cases, highlighting an unmanaged wildlife-human interface. Surveillance systems failed to detect the outbreak early, with cases only identified after deaths—including among healthcare workers—reflecting a lack of community-based surveillance. Diagnostic confirmation was delayed due to reliance on centralized laboratories in Kinshasa and the absence of regional lab capacity and cold chain logistics. Concurrent epidemics of mpox, cholera, and malaria further strained health system resources and weakened infection prevention and control (IPC) practices. Structural gaps such as fragmented preparedness and poor multisectoral coordination perpetuate the vulnerability of affected zones to repeated outbreaks. Table 1: Root Cause Summary Table Root Cause Evidence Key Weakness Identified Zoonotic Spillover 99.52% similarity to 1976 strain; no linkage to recent cases [4] Wildlife-human interface unmanaged Surveillance Failure Detected only after deaths, including healthcare workers [4] No community-based surveillance system Diagnostic Delay Samples shipped to Kinshasa for confirmation [4] No regional lab capacity or cold chain logistics Health System Overload Ongoing mpox, cholera, malaria outbreaks [1], [8], [9] Competing resource demands, weak IPC systems Structural Gaps Recurrent outbreaks in the same zones [4] Fragmented preparedness and poor coordination The outbreak, officially declared on 4 September 2025, centred in Bulape Health Zone, Kasai Province, with a single suspected spillover case in the neighbouring Mweka Health Zone (see Table 2 ). The causative virus was confirmed as Zaire ebolavirus, genetically like the 1976 strain, supporting the zoonotic spillover hypothesis. A total of 28 suspected, probable, or confirmed cases were reported, with 15 deaths, resulting in a provincial case fatality rate of 53.6%. The index case, a 34-year-old pregnant woman presenting with haemorrhagic symptoms, died rapidly, triggering further transmission, including nosocomial infections. Bulape experienced a high case fatality rate of 62%, while Mweka reported one fatal suspected case, raising concerns about surveillance and containment capabilities in this isolated zone. Table 2. Summary of Mwaka (Kasai, 2025) Ebola Outbreak Metric Value Outbreak Declaration Date 4 September 2025 Virus Strain Zaire ebolavirus Total Cases 28 (confirmed, probable, suspected) Total Deaths 15 Case Fatality Rate (CFR) 53.6% Geographic Spread Bulape (14 deaths), Mweka (1) Healthcare Worker Deaths 4 Index Case Pregnant woman, 34 yrs, died 25 Aug Genomic Similarity 99.52% to 1976 Yambuku-Mayinga Diagnostic Timeline Samples shipped to Kinshasa for PCR and WGS When compared to the much larger North Kivu outbreak of 2018–2020 (see Table 3 ), the Kasai outbreak was smaller in scale but similarly exposed underlying systemic weaknesses. North Kivu’s outbreak was exacerbated by armed conflict and community mistrust, while Kasai’s challenges were primarily geographic isolation, weak logistics, and ecological risk. Importantly, North Kivu benefited from decentralized laboratory networks and digital surveillance tools, enabling faster diagnostics and contact tracing. In contrast, Kasai relied on centralized confirmation in Kinshasa and lacked rapid detection mechanisms. Both outbreaks highlight the urgent need to strengthen multi-sectoral preparedness, including local laboratory capacity, community-based surveillance, rapid response logistics, and effective cross-zone coordination to mitigate future Ebola emergence in known hotspots. Table 3. Comparison: Mwaka vs. North Kivu EVD Outbreaks Dimension Mwaka (Kasai, 2025) North Kivu (2018–2020) Total Cases 28 3,470 confirmed and probable [WHO, CDC] Total Deaths (CFR) 15 (53.6%) 2,287 (65.9%) Outbreak Origin New zoonotic spillover Linked to the 2014–2016 West Africa strain Security Context Stable, remote Armed conflict, high community mistrust Surveillance Capacity Weak, passive case finding Contact tracing and digital tools used Diagnostic Access Centralized (Kinshasa) Decentralized labs (e.g., Goma, Beni) Concurrent Outbreaks Yes – Mpox, cholera, malaria Minimal during the EVD peak period Health Worker Infections 4 fatalities >170 infected [CDC] Community Trust Low literacy, moderate engagement Resistance, attacks on health workers Discussion The 2025 outbreak was genetically distinct from recent transmission chains and was most closely related to the 1976 Yambuku-Mayinga strain [4]. This finding supports the conclusion that the outbreak was due to a novel zoonotic spillover event. Deforestation, bushmeat consumption, and increased climate-related displacement of reservoir species—particularly bats—have elevated the risk of such spillovers in forest-edge communities [10]. A One Health framework is essential to address these intersecting environmental and biological drivers. The outbreak in Mwaka was detected only after several fatalities had occurred, including among healthcare workers [4]. This indicates a critical breakdown in local surveillance systems , which failed to detect early warning signs. Traditional, top-down alert systems are not functional in remote zones like Bulape, where community mistrust and limited health education persist. Implementing trusted communication channels , mobile reporting tools , and trained community health workers can significantly improve early detection [11]. Although sequencing was rapidly completed once samples reached Kinshasa, the centralization of diagnostic infrastructure created substantial delays. Geographic remoteness, lack of regional PCR capacity, and weak cold chain logistics contributed to a delayed outbreak confirmation [4]. In contrast to North Kivu, where mobile labs were available, Bulape lacked such decentralization. Prioritizing the deployment of GeneXpert systems and biosafety-level diagnostics in provincial hubs is essential to reduce confirmation timeframes [12]. The outbreak coincided with active epidemics of mpox, cholera, and malaria , all competing for the same personnel, laboratory time, and financial resources [1], [8], [9]. This multi-outbreak burden overwhelmed the already fragile health system and diluted the response to the Ebola outbreak. Compounded by donor fatigue and fragmented funding , the situation underscores the importance of integrated emergency management systems and consistent funding strategies [13]. Persistent Structural Weaknesses Despite multiple EVD outbreaks in the DRC over the past two decades, health system resilience remains weak. The recurrence of outbreaks in similar geographic zones demonstrates the absence of sustained investment in preparedness, poor intersectoral coordination , and limited local ownership [14]. Emergency interventions alone are not sufficient. Long-term solutions require the institutionalization of public health training , the development of regional genomic labs , and governance reform to support decentralized outbreak response. Conclusion The 2025 Mwaka outbreak of Ebola Virus Disease reveals that zoonotic spillovers remain a pressing threat , particularly in areas marked by ecological fragility and weak public health systems. Although DRC has made strides in genomic surveillance and rapid outbreak declaration, diagnostic centralization , poor surveillance , and inadequate system resilience continue to hinder response efforts. Effective future containment will require localized outbreak detection , decentralized diagnostic capacity , and a coordinated One Health strategy to manage ecological and structural risks sustainably. Abbreviations DRC – Democratic Republic of the Congo EVD – Ebola Virus Disease INRB – Institut National de Recherche Biomédicale PCR – Polymerase Chain Reaction RCA – Root Cause Analysis Declarations Ethics approval and consent to participate This Root Cause Analysis was conducted based on data collected through routine public health surveillance activities during an officially declared outbreak. All genomic sequencing and clinical data were anonymized and handled in accordance with the Democratic Republic of Congo’s national health policies. The study was reviewed and approved by the DRC National Health Ethics Committee. Individual informed consent was waived due to the use of de-identified secondary data collected for public health purposes. Consent for publication Not applicable. This manuscript does not contain any person’s data in any form. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Genomic sequence data are available under pre-publication agreements and can be accessed upon reasonable request to the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This study was supported by institutional funding from Walter Sisulu University, University of South Africa, and the University of Mbuji-Mayi. No specific external funding was received for this research. Authors’ contributions MJP conceived the study, conducted the root cause analysis, and drafted the manuscript. MJP contributed to data collection and epidemiological analysis. INRB performed genomic sequencing and bioinformatics analysis. All authors critically reviewed and approved the final manuscript. Acknowledgements The authors acknowledge the Democratic Republic of Congo Ministry of Public Health, the Institut National de Recherche Biomédicale (INRB), and all frontline healthcare workers involved in outbreak surveillance and response. Special thanks to the WHO and CDC teams for technical support and data sharing. References Vakaniaki EH, Kacita C, Kinganda-Lusamaki E, O’Toole A, et al. Sustained human outbreak of a new MPXV clade I lineage in eastern Democratic Republic of the Congo . Nat Med. 2024. DFAT. Australia's response to global health threats . Department of Foreign Affairs and Trade. 2023. [Accessed online]. WHO. South-South Cooperation in Ebola response: Asia’s role in Africa . 2022. [Accessed online]. Kinganda-Lusamaki E, Whitmer S, Lokilo-Lofiko E, Amuri-Aziza A, et al. 2020 Ebola virus disease outbreak in Équateur Province, DRC: retrospective genomic characterisation . Lancet Microbe. 2024;5(2):e109–e118. Institute of Tropical Medicine Antwerp. Ebola surveillance and support in DRC . Annual Report. 2023. Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment . Brief Bioinform. 2019. Minh BQ, Schmidt HA, Chernomor O, et al. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference . Mol Biol Evol. 2020;37(5):1530-4. WHO. WHO response to challenging cholera outbreak in the Democratic Republic of the Congo . 2025. WHO. Acute respiratory infections complicated by malaria – Democratic Republic of the Congo . 2024. Hayman DTS. Ecology of Ebola and other filoviruses . J Infect Dis. 2019;219(5):679-689. UNICEF. Strengthening Community-Based Surveillance in Ebola Contexts . 2023. BioFire Diagnostics. BioFire FilmArray Global Fever Panel . Technical Documentation. 2025. Ooms G, et al. Donor influence in the Ebola response in DRC . Global Health. 2023;19(1):48. Gostin LO, et al. After Ebola: reimagining public health preparedness . JAMA. 2020;323(14):1405–1406 Additional Declarations No competing interests reported. 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. 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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-7543616","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"comment","associatedPublications":[],"authors":[{"id":511177577,"identity":"37a34c72-d5f1-457e-8687-aa2bf98270b9","order_by":0,"name":"Jean Paul Muambangu Milambo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYBACxgYILcPAzNwA4snxg7gJBYS18DAwM4K1GEuCRBIMCNvGA9YNRIkbDoD4eLQwtzc/e1xQc5hHt52x8dPNHTaMm8+vTvzwwIBBnl/sAHaH9RwzN55x7DCP2WHGZuncM2nMZjfebpYAOsxw5uwE7FpmJJhJ87CBtTRI57YdZjO7cXYDSEuCwW0cWuY//ybN8w9iy2+gFh7jGWc3/8CrZQaPmTRvG1hLG8gWCQP+3m34benJKZOe2ZcO1mIN9IuBxA3ebRYJBhI4/WLYfnybdME3azmz84cP387dYVPf3392880fFTby/NI4tDQAAxpVSAKsUgKrchCQZ8DQwn8Ap+pRMApGwSgYmQAAYelhzFUKf4oAAAAASUVORK5CYII=","orcid":"","institution":"University of Mbuji Mayi","correspondingAuthor":true,"prefix":"","firstName":"Jean","middleName":"Paul Muambangu","lastName":"Milambo","suffix":""}],"badges":[],"createdAt":"2025-09-05 11:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7543616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7543616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90922913,"identity":"c5b135d8-025e-4ae0-8dc8-ec7be4fc1f85","added_by":"auto","created_at":"2025-09-09 15:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":880422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7543616/v1/af189a8d-bff5-4d15-922d-476997fe6e0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering the Drivers of Ebola Virus Disease Resurgence in DRC: A Root Cause Analysis of the 16th Outbreak in Mwaka, Kasai Province (2025)","fulltext":[{"header":"Background","content":"\u003cp\u003eEbola Virus Disease (EVD) remains a significant threat to global public health. Although its outbreaks are mostly localized to Africa, the 2014–2016 West African epidemic highlighted EVD’s potential to cause international crises. Its high mortality rate, risk of international spread, and requirement for high-level containment have made it a WHO priority disease for research and response [1].\u003c/p\u003e\u003cp\u003eIn North America, significant investments have been made into EVD vaccine research and deployment, such as the development and use of the rVSV-ZEBOV vaccine. Agencies like the U.S. Centers for Disease Control and Prevention (CDC) and the Public Health Agency of Canada (PHAC) have also contributed expert teams and resources during major outbreaks in Africa [1].\u003c/p\u003e\u003cp\u003eAustralia has played a role primarily through funding and international health deployments, supporting WHO emergency response missions and vaccine development [2]. In Asia, countries like China and India have extended logistical and technical support, and engaged in research collaboration and construction of healthcare infrastructure in EVD-affected regions [3].\u003c/p\u003e\u003cp\u003eEurope’s response includes field deployments by Médecins Sans Frontières (MSF), genomic surveillance by the European Centre for Disease Prevention and Control (ECDC), and major sequencing and bioinformatics contributions from institutions such as the Institute of Tropical Medicine (ITM) in Antwerp [5]. Africa remains the epicenter of EVD, with the Democratic Republic of the Congo (DRC) reporting 16 outbreaks since the virus was first discovered in 1976. Despite this history, the country continues to face challenges in surveillance, diagnostics, and health system resilience. The 16th outbreak in Mwaka (2025) occurred in the context of simultaneous public health emergencies—namely, mpox, cholera, and malaria—highlighting gaps in multi-outbreak management capacity [4].\u003c/p\u003e\n\u003ch3\u003eAim\u003c/h3\u003e\n\u003cp\u003eThis Root Cause Analysis (RCA) aims to systematically identify and understand the upstream factors and operational failures that led to the resurgence of EVD in Mwaka, Kasai Province (2025). The findings are intended to inform sustainable health systems strengthening, outbreak preparedness, and response strategies in DRC and comparable settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eDesign and Framework\u003c/h2\u003e\n\u003cp\u003eThe RCA was conducted using the \u0026ldquo;5 Whys\u0026rdquo; method integrated with systems thinking to investigate upstream and system-level drivers of the outbreak. Data sources included Ministry of Health reports, laboratory data from the Institut National de Recherche Biom\u0026eacute;dicale (INRB), WHO bulletins, and peer-reviewed genomic and epidemiological publications [4], [6], [7].\u003c/p\u003e\n\u003ch3\u003eLaboratory and Bioinformatics Approaches\u003c/h3\u003e\n\u003cp\u003eLaboratory confirmation involved molecular diagnostics using GeneXpert, the BioFire Global Fever Panel, and the Altona RealStar Filovirus RT-PCR Kit [4]. Positive samples were sequenced on an Oxford Nanopore GridION system using R10.4.1 flow cells. The sequencing produced a 99.97% complete genome, with a 99.52% match to the 1976 Yambuku-Mayinga strain [4]. Bioinformatics tools included iVar for consensus genome generation, MAFFT for multiple sequence alignment [6], and IQ-TREE for phylogenetic inference [7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis Root Cause Analysis was based on data collected through routine public health surveillance activities during an officially declared outbreak. All genomic sequencing and clinical data were anonymized in compliance with DRC national health policies and reviewed by the INRB and the Ministry of Public Health. The analysis was conducted under ethical guidelines provided by the DRC National Health Ethics Committee. No personally identifiable information was used, and genomic data are shared under pre-publication agreements [4].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA detailed root cause analysis (see \u003cstrong\u003eTable 1\u003c/strong\u003e) identifies several critical weaknesses that contributed to the 16th Ebola Virus Disease (EVD) outbreak in the Democratic Republic of Congo (DRC). The outbreak likely originated from a zoonotic spillover event, evidenced by a 99.52% genetic similarity to the 1976 Yambuku strain and no linkage to recent human cases, highlighting an unmanaged wildlife-human interface. Surveillance systems failed to detect the outbreak early, with cases only identified after deaths\u0026mdash;including among healthcare workers\u0026mdash;reflecting a lack of community-based surveillance. Diagnostic confirmation was delayed due to reliance on centralized laboratories in Kinshasa and the absence of regional lab capacity and cold chain logistics. Concurrent epidemics of mpox, cholera, and malaria further strained health system resources and weakened infection prevention and control (IPC) practices. Structural gaps such as fragmented preparedness and poor multisectoral coordination perpetuate the vulnerability of affected zones to repeated outbreaks.\u003c/p\u003e\n\u003cp\u003eTable 1: Root Cause Summary Table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRoot Cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEvidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKey Weakness Identified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZoonotic Spillover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.52% similarity to 1976 strain; no linkage to recent cases [4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWildlife-human interface unmanaged\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurveillance Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDetected only after deaths, including healthcare workers [4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo community-based surveillance system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic Delay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSamples shipped to Kinshasa for confirmation [4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo regional lab capacity or cold chain logistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth System Overload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOngoing mpox, cholera, malaria outbreaks [1], [8], [9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompeting resource demands, weak IPC systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStructural Gaps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRecurrent outbreaks in the same zones [4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFragmented preparedness and poor coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;The outbreak, officially declared on 4 September 2025, centred in Bulape Health Zone, Kasai Province, with a single suspected spillover case in the neighbouring Mweka Health Zone (see \u003cstrong\u003eTable 2\u003c/strong\u003e). The causative virus was confirmed as Zaire ebolavirus, genetically like the 1976 strain, supporting the zoonotic spillover hypothesis. A total of 28 suspected, probable, or confirmed cases were reported, with 15 deaths, resulting in a provincial case fatality rate of 53.6%. The index case, a 34-year-old pregnant woman presenting with haemorrhagic symptoms, died rapidly, triggering further transmission, including nosocomial infections. Bulape experienced a high case fatality rate of 62%, while Mweka reported one fatal suspected case, raising concerns about surveillance and containment capabilities in this isolated zone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Summary of Mwaka (Kasai, 2025) Ebola Outbreak\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOutbreak Declaration Date\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 September 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVirus Strain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZaire ebolavirus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (confirmed, probable, suspected)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCase Fatality Rate (CFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeographic Spread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBulape (14 deaths), Mweka (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthcare Worker Deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndex Case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePregnant woman, 34 yrs, died 25 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGenomic Similarity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.52% to 1976 Yambuku-Mayinga\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic Timeline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSamples shipped to Kinshasa for PCR and WGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhen compared to the much larger North Kivu outbreak of 2018\u0026ndash;2020 (see \u003cstrong\u003eTable 3\u003c/strong\u003e), the Kasai outbreak was smaller in scale but similarly exposed underlying systemic weaknesses. North Kivu\u0026rsquo;s outbreak was exacerbated by armed conflict and community mistrust, while Kasai\u0026rsquo;s challenges were primarily geographic isolation, weak logistics, and ecological risk. Importantly, North Kivu benefited from decentralized laboratory networks and digital surveillance tools, enabling faster diagnostics and contact tracing. In contrast, Kasai relied on centralized confirmation in Kinshasa and lacked rapid detection mechanisms. Both outbreaks highlight the urgent need to strengthen multi-sectoral preparedness, including local laboratory capacity, community-based surveillance, rapid response logistics, and effective cross-zone coordination to mitigate future Ebola emergence in known hotspots.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3. Comparison: Mwaka vs. North Kivu EVD Outbreaks\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMwaka (Kasai, 2025)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorth Kivu (2018\u0026ndash;2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3,470 confirmed and probable [WHO, CDC]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Deaths (CFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,287 (65.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOutbreak Origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNew zoonotic spillover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLinked to the 2014\u0026ndash;2016 West Africa strain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecurity Context\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStable, remote\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArmed conflict, high community mistrust\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurveillance Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeak, passive case finding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eContact tracing and digital tools used\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCentralized (Kinshasa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDecentralized labs (e.g., Goma, Beni)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConcurrent Outbreaks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes \u0026ndash; Mpox, cholera, malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimal during the EVD peak period\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth Worker Infections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 fatalities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;170 infected [CDC]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCommunity Trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow literacy, moderate engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResistance, attacks on health workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe 2025 outbreak was genetically distinct from recent transmission chains and was most closely related to the 1976 Yambuku-Mayinga strain [4]. This finding supports the conclusion that the outbreak was due to a \u003cb\u003enovel zoonotic spillover\u003c/b\u003e event. Deforestation, bushmeat consumption, and increased climate-related displacement of reservoir species\u0026mdash;particularly bats\u0026mdash;have elevated the risk of such spillovers in forest-edge communities [10]. A \u003cb\u003eOne Health framework\u003c/b\u003e is essential to address these intersecting environmental and biological drivers.\u003c/p\u003e\u003cp\u003eThe outbreak in Mwaka was detected only after several fatalities had occurred, including among healthcare workers [4]. This indicates a \u003cb\u003ecritical breakdown in local surveillance systems\u003c/b\u003e, which failed to detect early warning signs. Traditional, top-down alert systems are not functional in remote zones like Bulape, where \u003cb\u003ecommunity mistrust\u003c/b\u003e and \u003cb\u003elimited health education\u003c/b\u003e persist. Implementing \u003cb\u003etrusted communication channels\u003c/b\u003e, \u003cb\u003emobile reporting tools\u003c/b\u003e, and \u003cb\u003etrained community health workers\u003c/b\u003e can significantly improve early detection [11].\u003c/p\u003e\u003cp\u003eAlthough sequencing was rapidly completed once samples reached Kinshasa, the \u003cb\u003ecentralization of diagnostic infrastructure\u003c/b\u003e created substantial delays. Geographic remoteness, lack of regional PCR capacity, and weak cold chain logistics contributed to a delayed outbreak confirmation [4]. In contrast to North Kivu, where mobile labs were available, Bulape lacked such decentralization. Prioritizing the \u003cb\u003edeployment of GeneXpert systems\u003c/b\u003e and \u003cb\u003ebiosafety-level diagnostics\u003c/b\u003e in provincial hubs is essential to reduce confirmation timeframes [12].\u003c/p\u003e\u003cp\u003eThe outbreak coincided with active epidemics of \u003cb\u003empox, cholera, and malaria\u003c/b\u003e, all competing for the same personnel, laboratory time, and financial resources [1], [8], [9]. This \u003cb\u003emulti-outbreak burden\u003c/b\u003e overwhelmed the already fragile health system and diluted the response to the Ebola outbreak. Compounded by \u003cb\u003edonor fatigue and fragmented funding\u003c/b\u003e, the situation underscores the importance of \u003cb\u003eintegrated emergency management systems\u003c/b\u003e and consistent funding strategies [13].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePersistent Structural Weaknesses\u003c/h2\u003e\u003cp\u003eDespite multiple EVD outbreaks in the DRC over the past two decades, health system resilience remains weak. The recurrence of outbreaks in similar geographic zones demonstrates the \u003cb\u003eabsence of sustained investment\u003c/b\u003e in preparedness, \u003cb\u003epoor intersectoral coordination\u003c/b\u003e, and limited local ownership [14]. Emergency interventions alone are not sufficient. Long-term solutions require the \u003cb\u003einstitutionalization of public health training\u003c/b\u003e, the development of \u003cb\u003eregional genomic labs\u003c/b\u003e, and \u003cb\u003egovernance reform\u003c/b\u003e to support decentralized outbreak response.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe 2025 Mwaka outbreak of Ebola Virus Disease reveals that \u003cb\u003ezoonotic spillovers remain a pressing threat\u003c/b\u003e, particularly in areas marked by ecological fragility and weak public health systems. Although DRC has made strides in genomic surveillance and rapid outbreak declaration, \u003cb\u003ediagnostic centralization\u003c/b\u003e, \u003cb\u003epoor surveillance\u003c/b\u003e, and \u003cb\u003einadequate system resilience\u003c/b\u003e continue to hinder response efforts. Effective future containment will require \u003cb\u003elocalized outbreak detection\u003c/b\u003e, \u003cb\u003edecentralized diagnostic capacity\u003c/b\u003e, and a \u003cb\u003ecoordinated One Health strategy\u003c/b\u003e to manage ecological and structural risks sustainably.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eDRC\u003c/strong\u003e \u0026ndash; Democratic Republic of the Congo\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEVD\u003c/strong\u003e \u0026ndash; Ebola Virus Disease\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eINRB\u003c/strong\u003e \u0026ndash; Institut National de Recherche Biom\u0026eacute;dicale\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePCR\u003c/strong\u003e \u0026ndash; Polymerase Chain Reaction\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRCA\u003c/strong\u003e \u0026ndash; Root Cause Analysis\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This Root Cause Analysis was conducted based on data collected through routine public health surveillance activities during an officially declared outbreak. All genomic sequencing and clinical data were anonymized and handled in accordance with the Democratic Republic of Congo\u0026rsquo;s national health policies. The study was reviewed and approved by the DRC National Health Ethics Committee. Individual informed consent was waived due to the use of de-identified secondary data collected for public health purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable. This manuscript does not contain any person\u0026rsquo;s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All data generated or analyzed during this study are included in this published article and its supplementary information files. Genomic sequence data are available under pre-publication agreements and can be accessed upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was supported by institutional funding from Walter Sisulu University, University of South Africa, and the University of Mbuji-Mayi. No specific external funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;MJP conceived the study, conducted the root cause analysis, and drafted the manuscript. MJP contributed to data collection and epidemiological analysis. INRB performed genomic sequencing and bioinformatics analysis. All authors critically reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors acknowledge the Democratic Republic of Congo Ministry of Public Health, the Institut National de Recherche Biom\u0026eacute;dicale (INRB), and all frontline healthcare workers involved in outbreak surveillance and response. Special thanks to the WHO and CDC teams for technical support and data sharing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVakaniaki EH, Kacita C, Kinganda-Lusamaki E, O\u0026rsquo;Toole A, et al. \u003cem\u003eSustained human outbreak of a new MPXV clade I lineage in eastern Democratic Republic of the Congo\u003c/em\u003e. Nat Med. 2024.\u003c/li\u003e\n\u003cli\u003eDFAT. \u003cem\u003eAustralia\u0026apos;s response to global health threats\u003c/em\u003e. Department of Foreign Affairs and Trade. 2023. [Accessed online].\u003c/li\u003e\n\u003cli\u003eWHO. \u003cem\u003eSouth-South Cooperation in Ebola response: Asia\u0026rsquo;s role in Africa\u003c/em\u003e. 2022. [Accessed online].\u003c/li\u003e\n\u003cli\u003eKinganda-Lusamaki E, Whitmer S, Lokilo-Lofiko E, Amuri-Aziza A, et al. \u003cem\u003e2020 Ebola virus disease outbreak in \u0026Eacute;quateur Province, DRC: retrospective genomic characterisation\u003c/em\u003e. Lancet Microbe. 2024;5(2):e109\u0026ndash;e118.\u003c/li\u003e\n\u003cli\u003eInstitute of Tropical Medicine Antwerp. \u003cem\u003eEbola surveillance and support in DRC\u003c/em\u003e. Annual Report. 2023.\u003c/li\u003e\n\u003cli\u003eKatoh K, Rozewicki J, Yamada KD. \u003cem\u003eMAFFT online service: multiple sequence alignment\u003c/em\u003e. Brief Bioinform. 2019.\u003c/li\u003e\n\u003cli\u003eMinh BQ, Schmidt HA, Chernomor O, et al. \u003cem\u003eIQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference\u003c/em\u003e. Mol Biol Evol. 2020;37(5):1530-4.\u003c/li\u003e\n\u003cli\u003eWHO. \u003cem\u003eWHO response to challenging cholera outbreak in the Democratic Republic of the Congo\u003c/em\u003e. 2025.\u003c/li\u003e\n\u003cli\u003eWHO. \u003cem\u003eAcute respiratory infections complicated by malaria \u0026ndash; Democratic Republic of the Congo\u003c/em\u003e. 2024.\u003c/li\u003e\n\u003cli\u003eHayman DTS. \u003cem\u003eEcology of Ebola and other filoviruses\u003c/em\u003e. J Infect Dis. 2019;219(5):679-689.\u003c/li\u003e\n\u003cli\u003eUNICEF. \u003cem\u003eStrengthening Community-Based Surveillance in Ebola Contexts\u003c/em\u003e. 2023.\u003c/li\u003e\n\u003cli\u003eBioFire Diagnostics. \u003cem\u003eBioFire FilmArray Global Fever Panel\u003c/em\u003e. Technical Documentation. 2025.\u003c/li\u003e\n\u003cli\u003eOoms G, et al. \u003cem\u003eDonor influence in the Ebola response in DRC\u003c/em\u003e. Global Health. 2023;19(1):48.\u003c/li\u003e\n\u003cli\u003eGostin LO, et al. \u003cem\u003eAfter Ebola: reimagining public health preparedness\u003c/em\u003e. JAMA. 2020;323(14):1405\u0026ndash;1406\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Ebola virus disease, zoonotic spillover, surveillance, DRC, diagnostics, health systems, outbreak response","lastPublishedDoi":"10.21203/rs.3.rs-7543616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7543616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eThe Democratic Republic of the Congo (DRC) experienced its 16th Ebola Virus Disease (EVD) outbreak in 2025, centered in the Bulape Health Zone of Kasai Province. This outbreak occurred amid multiple concurrent epidemics and in a region with limited health infrastructure. Genomic sequencing revealed a new zoonotic spillover, genetically related to the 1976 Yambuku strain.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA Root Cause Analysis (RCA) was conducted using the \u0026ldquo;5 Whys\u0026rdquo; framework, integrating epidemiological data, genomic analysis, and surveillance reports. Key contributing factors to delayed detection and response were identified. Comparative insights were drawn from the 2018\u0026ndash;2020 North Kivu EVD outbreak.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe outbreak resulted in 28 confirmed, probable, or suspected cases and 15 deaths, including four healthcare workers. Root causes included poor ecological surveillance, weak community alert systems, diagnostic delays, health system overload from concurrent outbreaks, and structural underfunding. These factors contrast with North Kivu, where response delays were driven more by security issues.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThe 2025 Mwaka outbreak highlights how ecological and systemic vulnerabilities allow novel Ebola spillovers to escalate. Effective future preparedness will require sustained investment in One Health surveillance, decentralized diagnostics, and resilient public health governance.\u003c/p\u003e","manuscriptTitle":"Uncovering the Drivers of Ebola Virus Disease Resurgence in DRC: A Root Cause Analysis of the 16th Outbreak in Mwaka, Kasai Province (2025)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 08:30:02","doi":"10.21203/rs.3.rs-7543616/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":"a62176c2-f216-4b50-bd66-643246275abc","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T15:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 08:30:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7543616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7543616","identity":"rs-7543616","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}