Abstract
Background
Viral zoonoses, particularly RNA viruses, pose a growing public health threat in Sub-Saharan Africa (SSA) due to ecological disruption, rapid urbanization, and weak health systems. This scoping review synthesizes the available evidence on viral zoonotic outbreaks in SSA, focusing on documented public health and medical response strategies and the extent to which the One Health approach has been applied.
Methods
We conducted searches of peer-reviewed and grey literature published between January 2005 and March 2025 in PubMed, Scopus, Google Scholar, Google, and website searches including WHO-AFRO and Africa CDC. The search strategy combined Medical Subject Headings (MeSH) and keywords related to “zoonotic viruses,” “outbreaks,” “response,” and “Sub-Saharan Africa.” Eligible studies included outbreak reports, surveillance summaries, case reports, and epidemiological investigations involving human and/or animal viral zoonotic disease outbreaks in SSA. Data on outbreak characteristics, transmission patterns, response strategies, and One Health implementation were extracted.
Results
From an initial pool of 4,534 studies, fifty-two met the inclusion criteria. Rift Valley Fever virus (RVFV), Ebola virus, Marburg virus, Monkeypox, and Lassa virus were the most frequently reported viruses. A notable case of Lassa fever and SARS-CoV-2 co-infection was reported in Guinea. Transmission routes varied: direct contact, vector-borne, sexual, and nosocomial transmission. Reported public health responses included case isolation, contact tracing, community sensitization, vector control, and livestock surveillance, though there was limited formal assessment of their effectiveness. Integration of the One Health approach was inconsistently applied and explicitly documented in only a few studies.
Conclusion
Zoonotic viral outbreaks in Sub-Saharan Africa remain a recurrent and evolving public health challenge due to persistent gaps in surveillance, preparedness, and cross-sector coordination. Strengthening community-based detection, rapid laboratory confirmation, health system capacity for diagnostics and response, and fully operationalizing One Health frameworks is essential to enhance early warning and outbreak control.
Supplementary Information
The online version contains supplementary material available at 10.1186/s42522-025-00186-0.
Keywords: Viral zoonotic disease, Outbreak, Response strategy, Sub-Saharan Africa
Introduction
Viral zoonoses are infections caused by viruses transmitted from animals to humans. These infections pose a significant threat to public health globally and regionally, especially in sub-Saharan Africa (SSA), where ecological, socio-economic, and health system challenges are increasing [1, 2]. The emergence and re-emergence of viral zoonoses, such as Ebola virus disease (EVD), Lassa fever, Rift Valley fever (RVF), and the more recent Coronavirus disease 2019 (COVID-19), have underscored the critical need to understand the transmission patterns, prevalence, and risk factors driving these outbreaks [3, 4].
EVD has been responsible for repeated epidemics in Central and West Africa, with case fatality rates ranging from 25 to 90% and substantial socio-economic disruption [5]. RVF is endemic in East and Southern Africa, with outbreaks often linked to heavy rainfall and flooding that favour mosquito vector proliferation; the disease causes high livestock mortality and substantial economic losses [6]. Mpox, historically endemic in Central Africa, has re-emerged with broader geographic spread, human-to-human transmission, and a growing public health impact [7]. Together, these pathogens exemplify the diverse transmission routes, direct animal contact, vector-borne spread, and human-to-human transmission, that complicate surveillance and control efforts in the region.
Most viral zoonotic pathogens, particularly RNA viruses, are characterized by high mutation rates and adaptability, which increase their potential for spillover from animal to human populations [8]. Rapid urbanization, deforestation, climate change, agricultural expansion, and unregulated human-animal interactions contribute to the frequency and intensity of these outbreaks in SSA [2]. In addition to facilitating the emergence of zoonoses, these dynamics pose significant challenges to surveillance, early detection, and coordinated responses [9]. Viral zoonoses in SSA not only cause morbidity and mortality in humans but also devastate livestock populations, undermining food security and livelihoods [10]. Health systems in SSA are often overburdened during outbreaks, with limited diagnostic capacity, insufficient isolation facilities, and inadequate protective equipment, which amplify nosocomial transmission and strain already fragile infrastructures, as documented during the West Africa Ebola epidemic [11]. Outbreaks also disrupt routine health services, delay care for chronic conditions, and trigger long-term socio-economic consequences in affected communities [12].
The One Health approach recognizes the interconnectedness of human, animal, and environmental health and is critical for addressing these challenges. By promoting cross-sectoral collaboration among public health, veterinary, and environmental sectors, the One Health framework enhances surveillance, risk assessment, and the design of holistic interventions that are essential for preventing and controlling zoonotic spillover [13, 14].
Standard responses to viral zoonotic outbreaks typically involve a combination of early case detection, isolation and clinical management, contact tracing, community engagement, and infection prevention and control (IPC) measures [15]. In more severe outbreaks, additional steps may include movement restrictions, vector control (in vector-borne diseases), mass vaccination (where applicable), targeted culling of infected or exposed animals, and international coordination under the International Health Regulations (IHR). Strengthening preparedness and response capacities within a One Health framework is essential for mitigating the threat of viral zoonoses in SSA [16, 17].
Despite extensive experience with outbreaks, significant gaps in knowledge and practice remain. The true burden of viral zoonoses in SSA is underestimated due to weak surveillance systems and underreporting. Ecological drivers such as climate variability and land-use change are insufficiently integrated into outbreak prediction models, limiting our ability to anticipate epidemics [18]. In addition, limited cross-sectoral coordination also hinders effective One Health implementation, and systematic evaluations of outbreak response strategies are scarce [19]. This review synthesizes available evidence on documented viral zoonotic disease outbreaks in SSA, with focus on the response strategies and One Health approaches reported in literature. Specifically, we examined studies reporting viral zoonotic disease outbreaks in SSA, prevention, and control efforts, and analyse how public health, medical, veterinary, and ecological responses were coordinated. Emphasis is placed on surveillance systems, case management, and, where applicable, vaccination efforts. Rather than formally evaluating intervention effectiveness, the review aims to map existing strategies and their alignment with One Health principles. Ultimately, this review identifies key gaps and proposes actionable improvements for strengthening outbreak preparedness and response across SSA.
Methods
Settings
This scoping review was guided by the methodological framework developed by Arksey and O’Malley (2005) and examined studies reporting on viral zoonotic disease outbreaks and response strategies in sub-Saharan Africa. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [20].
Protocol registration
The protocol for this review was registered in Open Science Framework (OSF) and can be found at 10.17605/OSF.IO/UENR5.
Information sources and search strategy
We conducted a comprehensive literature search across electronic bibliographic databases and open-access sources, including PubMed, Scopus, Google Scholar, and Google. We initially identified approximately 228,000 and 843,000 search results from Google and Google Scholar respectively, only the 100 (Google) and 200 (Google Scholar) articles from each database were deemed relevant for screening and subsequent selection based on the study’s inclusion criteria [21]. In addition, the reference lists of included studies and other relevant publications were reviewed to identify eligible articles for further research. The search strategy focused on the core concepts of the review: viral zoonotic diseases, outbreaks, response strategies, and Sub-Saharan Africa. These concepts were operationalized using a range of synonyms tailored to each database, incorporating Medical Subject Headings (MeSH) where applicable. Boolean operators (AND, OR) were employed to optimize search sensitivity and specificity. To capture relevant grey literature, we also searched the websites of key public health organizations, such as the Africa Centres for Disease Control and Prevention (Africa CDC) and the WHO Regional Office for Africa (WHO AFRO). The search was limited to studies published in English between 2005 and 2025, aligning with the implementation of the International Health Regulations (IHR) [22], which marked a global shift toward structured outbreak surveillance, reporting, and coordination frameworks relevant to zoonotic disease management in SSA and ensuring inclusion of up-to-date literature.
Study selection procedures
The inclusion and exclusion criteria were defined based on the Participants, Intervention/Exposure, Comparator, and Outcome (PICO) framework, as detailed below:
Participants/population
The review included outbreak investigation/report, cross-sectional, case-report, and retrospective cohort studies. Both humans and animals were considered in assessing zoonotic disease outbreaks, including their epidemiological characteristics, transmission patterns, and the effectiveness of response strategies.
Intervention(s)/exposure
The exposure of interest was a zoonotic disease outbreak of viral origin.
Comparator(s)/control
Not applicable.
Main outcome
The primary outcome of this review was to identify and describe the epidemiological characteristics and transmission patterns of viral zoonotic disease outbreaks in SSA, including affected populations, transmission routes, and the zoonotic reservoirs. In addition, the review synthesized evidence on the public health, medical, and ecological interventions implemented during outbreaks and assessed the extent to which One Health integration across detection, surveillance, and response activities.
Additional outcome
Additional outcomes included synthesizing key findings reported in each outbreak study, which provided insight into patterns of zoonotic virus emergence, response strategies, and epidemiological characteristics specific to Sub-Saharan Africa.
Eligibility criteria
Primary articles that reported on viral zoonotic disease outbreaks.
Studies conducted in any of the Sub-Saharan African countries.
Studies published between the years 2005 and 2025.
Outbreak investigation studies, cross-sectional studies, case-report studies, and retrospective studies.
Grey literature from recognized public health organizations including WHO and Africa CDC.
Study inclusion
Two independent investigators, SA and JMG used the eligibility criteria to select studies for inclusion in the review. Any disagreement was resolved by discussion, and/or a third reviewer, PED was consulted for a consensus to be reached.
Data extraction
We extracted the following data: title, author(s), publication year, study type, study country, outbreak year(s), zoonotic virus identified, zoonotic source / reservoir, transmission pattern (e.g., direct contact, vector-borne, airborne), affected population, prevalence / outbreak magnitude, number of deaths, reported risk factors, public health / medical / ecological intervention(s), intervention outcomes / effectiveness, integration of one health approach, surveillance / detection mechanisms, and key findings / notes. Mendeley Desktop Version 1.19.8 was used to identify duplicate records.
Data analysis and synthesis
The extracted data on zoonotic virus outbreaks including prevalence estimates, transmission patterns, and affected populations were summarized in tabular form. Information related to the type of zoonotic virus, human and animal case numbers, and reported intervention strategies was compiled and analyzed descriptively.
Risk of bias (quality) assessment
Consistent with existing methodological guidance for scoping reviews, a formal risk of bias assessment or quality appraisal was not undertaken for the included studies [23].
Results
Study selection procedures
We identified 4,523 records; after deduplication, 4,470 were screened by title/abstract. 56 articles underwent full-text assessment, and 41 met inclusion criteria. An additional 11 outbreak reports from organizational websites were included, yielding 52 studies overall (Fig. 1).
Fig. 1.
PRISMA Flow diagram of the study selection procedure
Characteristics of included studies
Table 1 provides an overview of the 52 studies included in this scoping review, organized chronologically by the year in which each outbreak occurred. The 52 included items comprised peer-reviewed articles, outbreak investigations, national surveillance summaries, and situation reports from 25 sub-Saharan African countries (2006–2025). Most reports originated from East and West Africa, with frequent contributions from Uganda, Nigeria, Sudan, and Kenya. The majority focused on human outbreaks (n = 46); two reported on both humans and animals.
Table 1.
Characteristics of included studies according to the year of outbreak (2006–2025)
| Author | Year | Country | Outbreak Year | Identified virus | Study populations | Study design |
|---|---|---|---|---|---|---|
| Bbosa et al. | 2025 | Uganda | 2024 | Mpox Clade Ib | Humans | Case reports and molecular analysis |
| Sabushimike et al. | 2025 | Burundi | 2024 | Mpox | Humans | Case report |
| Africa CDC | 2024 | Rwanda | 2024 | Marburg | Humans | Outbreak situation report |
| Africa CDC | 2024 | South Africa | 2024 | Mpox | Humans | Outbreak situation report |
| Masirika et al. | 2025 | DRC | 2023–2024 | Mpox Clade Ib | Humans | Observational study |
| Onukak et al. | 2023 | Nigeria | 2023 | *Mpox and (VZV) | Humans | Case Report |
| Africa CDC | 2023 | Tanzania | 2023 | Marburg | Humans | Outbreak situation report |
| Africa CDC | 2023 | Equatorial Guinea | 2023 | Marburg | Humans | Outbreak situation report |
| Zerfu et al. | 2024 | Ethiopia | 2022–2023 | CHIKV | Humans | Institution-based cross-sectional study |
| Tabassum et al. | 2023 | Mauritania | 2022 | RVFV | Humans | Outbreak report |
| Ishema et al. | 2024 | Rwanda | 2022 | RVFV | Animals | Outbreak investigation report |
| Mmerem et al. | 2024 | Nigeria | 2022 | *Mpox and (VZV) | Human | Case report |
| Ramera et al. | 2024 | Rwanda | 2022 | RVFV | Animals | Outbreak report |
| Musoke et al. | 2023 | Uganda | 2022 | Sudan Virus | Humans | Case report |
| Ogoina & James | 2022 | Nigeria | 2022 | Mpox | Humans | Case report |
| WHO | 2022 | Ghana | 2022 | Marburg | Humans | Outbreak situation report |
| WHO | 2022 | DRC | 2022 | Ebola | Humans | Outbreak situation report |
| Besombes et al. | 2023 | CAR | 2021–2022 | Mpox Clade I | Human | Outbreak investigation report |
| Keita et al. | 2022 | Guinea | 2021 | *LASV and SARS-CoV-2 | Humans | Case report |
| Africa CDC | 2021 | Guinea | 2021 | Marburg | Humans | Outbreak situation report |
| Barry et al. | 2022 | Mauritania | 2020 | RVFV | Humans and animals | Outbreak investigation |
| Fourié et al. | 2021 | Djibouti | 2019 | CHIKV | Human | Case Report with molecular characterization |
| Atim et al. | 2023 | Uganda | 2019 | CCHFV | Humans and animals | Outbreak investigation |
| Ahmed et al. | 2021 | Sudan | 2019 | CCHFV | Humans | Case series and molecular diagnostic investigation |
| Mirembe et al. | 2021 | Uganda | 2018–2019 | CCHFV | Humans | Epidemiological investigation and case-control study |
| Fusade-Boyer et al. | 2019 | Togo | 2018 | Inf A H5N1 | Animals | Outbreak investigation and molecular epidemiology study |
| Kayiwa et al. | 2019 | Uganda | 2017 | CHIKV and DENV | Humans | Case report |
| Yinka-Ogunleye et al. | 2018 | Nigeria | 2017 | Mpox West African Clade | Humans | Outbreak investigation report |
| Nyakarahuka et al. | 2019 | Uganda | 2017 | Marburg | Humans | Epidemiological and laboratory investigation |
| Yaro et al. | 2021 | Nigeria | 2017–2020 | LASV | Humans | Retrospective epidemiological analysis using national surveillance data from December 2016 to September 2020 |
| Eltvedt et al. | 2020 | DRC | 2016 | Mpox | Human | Case report |
| Dokubo et al. | 2018 | Liberia | 2015 | Ebola virus | Humans | Case investigation |
| Christie et al. | 2015 | Liberia | 2015 | Ebola virus | Human | Case report and epidemiological investigation |
| Balinandi et al. | 2018 | Uganda | 2015 | CCHFV | Humans | Case investigation and outbreak response |
| Chérif et al. | 2017 | Guinea | 2014–2015 | Ebola virus | Humans | Nationwide retrospective cohort study |
| Bonney et al. | 2018 | Ghana | 2014–2015 | DENV 2 and 3 | Humans | Cross-sectional surveillance study using serological and molecular assays (retrospective) |
| Dunn et al. | 2016 | Sierra Leone | 2014 | Ebola virus | Human | Outbreak investigation |
| Nyakarahuka et al. | 2017 | Uganda | 2014 | Marburg virus | Human | Case Report |
| Ka et al. | 2017 | Senegal | 2014 | Ebola virus (Zaire) | Humans | Case report and outbreak investigation |
| WHO | 2014 | Guinea | 2014 | Ebola virus | Humans | Outbreak surveillance & response report |
| Liberia | ||||||
| Sierra Leone | ||||||
| Mali | ||||||
| Nigeria | ||||||
| Shoemaker et al. | 2012 | Uganda | 2011 | Sudan virus | Humans | Case report and outbreak investigation |
| Konongoi et al. | 2016 | Kenya | 2011–2014 | DENV 1–3 | Humans | Cross-sectional outbreak investigation |
| Ahmed et al. | 2022 | Sudan | 2010, 2011, 2015, 2019, 2020 | RVFV | Humans | Retrospective epidemiological study |
| Aradaib et al. | 2010 | Sudan | 2008 | CCHFV | Humans | Outbreak investigation and case series |
| Adjemian et al. | 2011 | Uganda | 2007 | Marburg | Humans | Outbreak investigation and case series |
| Chengula et al. | 2014 | Tanzania | 2006–2007 | RVFV | Animals | Cross-sectional sero-epidemiological study |
| Nguku et al. | 2010 | Kenya | 2006–2007 | RVFV | Humans | Epidemiological investigation combining surveillance data, serosurveys, and laboratory diagnostics |
| Peyrefitte et al. | 2007 | Cameroon | 2006 | CHIKV | Humans | Outbreak investigation |
| WHO | 2025 | Kenya | 2006–2007 | RVFV | Humans | Outbreak situation report |
| Somalia | 2006–2007 | |||||
| Tanzania | 2007 | |||||
| Sudan | 2007–2008 | |||||
| Madagascar | 2008 | |||||
| Madagascar | 2008–2009 | |||||
| South Africa | 2010 | |||||
| Mauritania | 2012 | |||||
| Niger | 2016 | |||||
| WHO | 2017 | Uganda | 2007 | Marburg | Humans | Outbreak situation report |
| 2008 | ||||||
| 2012 | ||||||
| 2014 | ||||||
| 2017 | ||||||
| WHO | 2016 | Kenya | 2016 | CHIKV | Humans | Outbreak situation report |
| Senegal | 2015 |
CAR: Central African Republic
DRC: Democratic Republic of Congo
CCHFV: Crimean Congo Haemorrhagic Fever Virus
CHKIV: Chikungunya Virus
DENV: Dengue Virus
VZV: Varicella Zoster Virus
*: Co-infection
Epidemiological patterns reported by included studies
The most frequently reported viral zoonoses were Ebola virus, Mpox, Rift Valley fever virus (RVFV), and Marburg virus. Outbreak sizes ranged from isolated index cases to large-scale epidemics. The 2014 West Africa Ebola outbreak included > 18,000 cases and > 6,000 deaths, while the 2023–2024 Mpox outbreak in DRC involved > 600 cases. Case fatality rates varied: Ebola virus disease 25–90%, Marburg virus 33–50%, CCHFV up to 75%, and Mpox generally < 10%. Common risk factors included direct animal contact, vector exposure, bushmeat handling, and nosocomial transmission (Table S1; Supplementary File 1). Figure 2 illustrates the geographic distribution of reported viral zoonotic outbreaks, revealing clusters primarily in East and West Africa, which appear to be recurrent hotspots for zoonotic spillover or emergence. Across studies, common risk factors included contact with infected animals or animal products, vector exposure, and behaviors such as bushmeat handling. These findings highlight geographic and behavioral patterns that may inform targeted interventions.
Fig. 2.
Geographic distribution of reported zoonotic virus occurrence in SSA
Transmission patterns
This included studies that identified a range of transmission modes associated with zoonotic viruses, with human-to-human transmission via direct contact being the most commonly reported. This was evident in outbreaks involving filoviruses such as the Marburg virus and the Ebola virus, where transmission occurred through close physical contact with infected individuals or their bodily fluids within households and healthcare settings. Other studies also reported nosocomial transmission, which were linked to insufficient use of personal protective equipment (PPE) and inadequate infection prevention and control (IPC) practices.
Transmission through sexual contact was highlighted as a potential route in a few of the studies, particularly during outbreaks of Mpox and Ebola virus disease [24–27]. Monkeypox cases reported in Nigeria, DR Congo, and South Africa included individuals with genital lesions and anal proctitis, with histories of recent unprotected sexual encounters, suggesting sexual transmission as a key mode of spread in certain contexts [24, 26, 27]. The reported Ebola virus transmission through sexual contact with a convalescent male patient highlights the risk of viral persistence in semen after clinical recovery.
Other transmission routes reported by the included studies were vector-borne transmission and zoonotic spillover from wildlife [28–34]. Outbreaks of Rift Valley Fever virus and Dengue virus were typically linked to vector-borne transmission through mosquitoes, while Crimean–Congo hemorrhagic fever (CCHFV) was associated with tick-borne transmission [28, 34–38]. For example, RVFV outbreaks often followed periods of heavy rainfall that favored mosquito proliferation. Studies on Lassa fever and Marburg virus outbreaks reported zoonotic spillover from wildlife to humans, with Mastomys natalensis rodents serving as the primary reservoir for Lassa virus, and Rousettus aegyptiacus bats for Marburg virus [30, 39–42].
Outbreak magnitude and case fatality rates
The magnitude of viral zoonotic outbreaks reported across the included studies ranged from isolated index cases to large-scale epidemics affecting human and animal populations. The 2024 Marburg virus outbreak in Rwanda involved 27 confirmed cases and 9 deaths, while the Mpox outbreak in the Democratic Republic of Congo (DRC) documented 646 cases and 7 deaths, predominantly among sex workers [27, 43]. Outbreaks of Rift Valley Fever virus (RVFV) was mostly zoonotic in nature, with one study reporting 78 human cases alongside 186 confirmed animal infections [34]. Additionally, the 2014 West Africa Ebola outbreak, documented by WHO [44], is one of the most severe zoonotic viral outbreaks, with over 18,000 cases and more than 6,000 deaths across five countries.
Case fatality rates (CFRs) significantly varied across virus types and settings. The Ebola virus disease outbreak reported by WHO [45] showed CFRs as high as 62.9% among paediatric cases. Depending on the setting and healthcare response, Marburg virus outbreaks reported CFRs ranging from 33% to over 50%. CCHFV was associated with CFRs of up to 75%, particularly in small outbreaks with delayed diagnosis. Monkeypox generally exhibited lower mortality; however, fatal outcomes were documented in immunocompromised individuals, particularly those co-infected with HIV.
Effectiveness of public health, medical, and ecological interventions
A variety of interventions were documented across the included studies in response to zoonotic viral outbreaks, as summarized in Table S2 (Supplementary File 2). These interventions comprised case detection and isolation, contact tracing, health education, vector control, and ecological or livestock-focused measures. These reflect varied approaches depending on the virus, setting, and available public health infrastructure. For MARV outbreaks reported in Ghana and Equatorial Guinea, the deployment of rapid response teams, contact tracing, and laboratory confirmation were central components of outbreak control [46, 47].
Mpox outbreaks, including a large outbreak in the Democratic Republic of Congo with over 600 cases, involved interventions focused on community sensitization, surveillance, and clinical management, especially among vulnerable populations such as sex workers and people living with HIV [27]. Despite these efforts, several studies reported persistent challenges related to delayed case recognition, and limited access to health services, which may have constrained the impact of public health interventions. In the Rift Valley Fever virus (RVFV) case, both medical and ecological measures were implemented. Interventions in Mauritania, Rwanda, and Kenya included livestock movement restrictions, vector control, animal health surveillance, and public awareness campaigns targeting high-risk occupational groups [28, 29, 33, 48].
Similarly, CCHFV outbreaks prompted tick control programs, public health awareness, and enhanced surveillance for healthcare and veterinary workers. In Uganda, occupational exposure among farmers and abattoir workers was noted, with response strategies tailored to high-risk communities [36, 38]. However, intervention outcomes were often sketchy, with few studies employing formal monitoring or impact evaluation. Although several studies reported successful containment following the deployment of control measures, a consistent gap across the literature was the absence of rigorous outcome evaluation. Most interventions were described narratively, with few studies providing quantitative data on effectiveness, such as incidence reduction, or speed of containment. These findings highlight the critical role of integrated public health, clinical, and ecological interventions in controlling zoonotic virus outbreaks. They also underscore the need for improved monitoring and evaluation frameworks to assess intervention effectiveness in real time. Future outbreak responses would benefit from adopting One Health strategies that combine human, animal, and environmental health approaches with robust data collection systems to inform policy and practic.
Integration of the One Health approach by included studies
The One Health approach, emphasizing the interconnectedness of human, animal, and environmental health was explicitly reported in only a limited number of the included studies. While zoonotic viruses inherently require multisectoral coordination, only four (7.7%) studies mentioned active integration of One Health principles in outbreak detection, surveillance, or response activities. In outbreaks reported by Ishema et al. (2024), Nguku et al. (2010) and Remera et al. (2024), the One Health approach was operationalized through coordinated response involving veterinary, public health, and environmental sectors. This included joint human and animal surveillance, integrated laboratory diagnostics, and multisectoral field investigations. Specifically, Ishema et al. (2024), Nguku et al. (2010) and Remera et al. (2024) documented district-level collaboration and RT-PCR testing in livestock during Rwanda’s 2022 RVF outbreak; Ishema et al. (2024), Nguku et al. (2010) and Remera et al. (2024) detailed concurrent entomological, human, and livestock surveillance during Kenya’s 2006–2007 RVF outbreak; and by Ishema et al. (2024), Nguku et al. (2010) and Remera et al. (2024) highlighted decentralized outbreak response teams, environmental risk mapping, and cross-sector sample tracking as key elements of the operational One Health strategy.
In another study by Barry et al. (2022), the One Health approach was fully implemented during the outbreak with coordinated weekly meetings across all sectors. In contrast, majority (48/52; 92.3%) of included studies, particularly those focusing on human clinical cases (12/52) or hospital-based surveillance (9/52), did not mention involvement of veterinary or ecological health sectors, where such multisectoral integration was relevant. Moreover, while zoonotic reservoirs (e.g., livestock, rodents, bats) were discussed in many studies, corresponding surveillance or intervention measures targeting these reservoirs were seldom integrated into outbreak response plans. Although 48 (92.3%) studies did not explicitly reference the use of a One Health approach, strategy elements may have been implemented but not expressly documented in the published reports. The findings highlight a critical implementation gap between One Health theory and practice. Although zoonotic virus outbreaks demand a multidisciplinary response, integrating human, animal, and environmental health responses remain rarely documented and underreported in the literature. Strengthening One Health capacity at the national and regional level through formal frameworks, multisectoral coordination mechanisms, and joint outbreak investigations remains a pressing need for improving preparedness and response to emerging zoonoses in Sub-Saharan Africa. Table 2 summarises the integration of the one health approach by the included studies.
Table 2.
Integration of One Health approach by included studies
| Author(s) | Integration of one health approach | Surveillance / detection mechanisms |
|---|---|---|
| Masirika et al. | Not stated | Genomic sequencing; epidemiological data collection; contact tracing |
| Tabassum et al. | Not stated | Laboratory confirmation of cases; monitoring of animal and human cases |
| Dokubo et al. | Not explicitly applied but survivor monitoring aligns with One Health principles | Laboratory testing (RT-PCR), contact tracing |
| Kayiwa et al. | Not stated | Arbovirus surveillance at Uganda Virus Research Institute (UVRI); diagnostic testing including ELISA, RT-PCR, virus isolation |
| Ishema et al. | Yes; coordinated response involving veterinary, public health, and environmental sectors | Active surveillance; laboratory testing (RT-PCR); community reporting systems |
| Yinka-Ogunleye et al. | Not stated | Laboratory testing of blood, lesion swabs, and crusts; epidemiological investigations |
| Mmerem et al. | Not stated | PCR confirmation of Mpox and chickenpox; wound swab microscopy and culture |
| Besombes et al. | Not stated | Laboratory confirmation via PCR testing; clinical assessments; monitoring of contacts |
| Fusade-Boyer et al. | Partially — collaboration among veterinary and lab agencies | PCR confirmation, genome sequencing, phylogenetic analysis |
| Peyrefitte et al. | Not discussed | Laboratory testing (RT-PCR, serology, virus isolation); patient monitoring |
| Remera et al. | Yes – coordinated cross-sectoral response involving public health, veterinary, and community actors | Syndromic surveillance, molecular diagnostics (PCR), national laboratory support |
| Fourié et al. | Not discussed | RT-PCR and full genome sequencing |
| Dunn et al. | Not stated | Medical record review, interviews with healthcare workers and caregivers, daily monitoring of contacts for EVD symptoms |
| Eltvedt et al. | Not stated | Clinical diagnosis based on symptoms; delayed serological testing; no PCR confirmation due to logistical challenges |
| Christie et al. | Not stated | Lab confirmation (RT-PCR), semen testing, contact tracing |
| Bbosa et al. | Not stated | PCR testing, genomic sequencing, phylogenetic analysis |
| Atim et al. | Not stated | ELISA for antibody detection; qRT-PCR and next-generation sequencing for virus detection in ticks |
| Nyakarahuka et al. | Not explicitly mentioned | Real-time RT-PCR, antigen detection, IgM ELISA, virus isolation, whole-genome sequencing |
| Konongoi et al. | Not explicitly mentioned | RT-PCR, IgM ELISA, sequencing of PCR-positive samples |
| Musoke et al. | Not stated | Real-time PCR testing for SUDV |
| Chengula et al. | Not stated | RVF-specific inhibition ELISA (I-ELISA); Reverse transcription polymerase chain reaction (RT-PCR) |
| Chérif et al. | Not stated | National coordination; RT-PCR lab confirmation for all included cases |
| Nguku et al. | Yes; collaboration among human health, animal health, and environmental sectors | Patient interviews and medical record reviews, Community engagement for case detection, Laboratory confirmation via ELISA and RT-PCR, Establishment of field laboratories for sample processing |
| Yaro et al. | Not stated | Use of national surveillance data from the Nigeria Centre for Disease Control (NCDC), Laboratory confirmation of cases using RT-PCR, Deployment of the Surveillance Outbreak Response Management and Analysis System (SORMAS) |
| Ahmed et al., 2021 | Not stated | Blood samples from patients were tested using real-time qPCR for RVFV, dengue virus (DENV), and chikungunya virus (CHIKV) |
| Shoemaker et al. | Not mentioned | Laboratory confirmation using real-time PCR, Epidemiological investigation and contact tracing |
| Zerfu et al. | Not stated | Blood samples were collected and tested for anti-CHIKV IgM and IgG antibodies using enzyme-linked immunosorbent assay (ELISA), Microscopy was used to examine blood films for Plasmodium infection |
| Balinandi et al. | Not stated | Laboratory confirmation using RT-PCR and serological assays, Field investigations and tick sampling, Genomic sequencing to determine virus lineage |
| Konongoi et al. | Not stated | Samples collected from febrile patients in hospitals across Nairobi, northern, and coastal Kenya, Testing for IgM antibodies against dengue, yellow fever, West Nile, and Zika viruses using ELISA, Detection of acute arbovirus infections and determination of infecting serotypes using RT-PCR, Sequencing of representative PCR-positive samples to confirm circulation of dengue serotypes |
| Onukak et al. | Not stated | Polymerase Chain Reaction (PCR) testing of skin lesion samples, confirming co-infection with Mpox and VZV |
| Aradaib et al. | Not stated | Reverse transcription–PCR (RT-PCR) was used to detect CCHFV RNA in serum samples from eight patients, Phylogenetic analysis identified the virus as belonging to group III lineage, indicating links to strains from South Africa, Mauritania, and Nigeria |
| Adjemian et al. | Not stated | Laboratory confirmation of Marburg virus infection, Epidemiologic investigation to identify source and transmission patterns |
| Bonney et al. | Not mentioned | Serological testing using ELISA, Molecular testing using RT-PCR, Phylogenetic analysis of the envelope gene of DENV-3, showing close homology with sequences from Senegal and India |
| Sabushimike et al. | Not mentioned | Clinical evaluation, Laboratory confirmation of Mpox and HIV infections |
| Ka et al. | Not mentioned | Laboratory confirmation via reverse transcription PCR (RT-PCR). Epidemiological investigation |
| Keita et al. | Not mentioned | Reverse transcriptase PCR (RT-PCR) testing for SARS-CoV-2 and Lassa virus. EVD ruled out through negative PCR testing. Biochemical and haematological analyses to assess organ function |
| Ahmed et al. | Not mentioned | Laboratory confirmation through RT-PCR and serological assays; epidemiological investigations |
| Nyakarahuka et al. | Not explicitly mentioned; however, ecological investigations were conducted to identify potential sources of infection | RT-PCR testing for Marburg virus. Serological assays (IgM and IgG). Genome sequencing of viral isolates |
| Mirembe et al., 2021 | Not implemented but recommended | RT-PCR testing for CCHFV. Case-control study to identify risk factors |
| Ogoina & James | Not mentioned | Clinical observation of symptoms. Laboratory testing for Mpox virus |
| Barry et al. | Yes — fully implemented during the outbreak with coordinated weekly meetings across sectors | Laboratory confirmation (RT-PCR and serology in humans and animals). Vector surveillance and mosquito species identification. Epidemiological data collection and case mapping |
| WHO | Not stated | RT-PCR testing, WHO Integrated Disease Surveillance |
| Africa CDC | Not stated | Contact tracing, regional surveillance, expert deployment from Africa CDC |
| Africa CDC | Not stated | PCR confirmation; contact tracing and active case search |
| Africa CDC | Not stated | Lab confirmation by Institute Pasteur (Senegal); contact tracing and surveillance initiated |
| Africa CDC | Not stated | Testing and active monitoring |
| Africa CDC | Not stated | Confirmed at NICD, contact monitoring for 21 days, genomic sequencing done |
| WHO | Not stated | Active contact tracing, laboratory diagnostics in all affected districts, community surveillance, healthcare worker monitoring |
| WHO | Not stated | Not stated |
| WHO | Not stated | Laboratory confirmation at UVRI; active case search |
| WHO | Not stated | Not stated |
| WHO | Not stated | Lab confirmation (INRB & ETC), contact tracing, |
Discussion of key findings
This scoping review synthesizes evidence on viral zoonotic outbreaks in sub-Saharan Africa (SSA) from 2005 to 2025, describing the diversity of pathogens, geographic distribution, transmission dynamics, response strategies, and the extent to which One Health approaches have been applied. Across 52 items from 25 SSA countries, the most frequently reported pathogens were RVFV, MARV, Mpox virus, Ebola virus, LASV, and CCHFV. Outbreak responses commonly included case detection/isolation, contact tracing, community engagement, vector control, livestock measures, and laboratory confirmation; however, standardized outcome metrics were seldom reported. Explicit use of One Health was rare and unevenly documented.
Outbreaks clustered in East and West Africa and reflected diverse transmission routes: human-to-human contact (notably filoviruses in households and health facilities, with nosocomial spread), vector-borne transmission (mosquitoes for RVFV and dengue; ticks for CCHFV), and zoonotic spillover (e.g., Rousettus aegyptiacus for MARV; Mastomys natalensis for LASV). Seasonality was prominent for RVFV (post-rainfall amplification), with site-specific deviations (e.g., dual seasonality in Rwanda), suggesting additional socio-ecological drivers such as livestock mobility and climate variability [33]. Many reports implicated reservoirs or vectors but lacked confirmatory entomological or animal data, limiting species-specific control options and risk-based targeting [49]. Re-emergence patterns for Mpox and sporadic co-infections (e.g., LASV/SARS-CoV-2) illustrate evolving risk profiles in interconnected human–animal ecologies.
Core response activities, rapid case identification, isolation, contact tracing, and risk communication, were widely implemented, with additional vector control and livestock movement restrictions for arboviral and haemorrhagic fever events. MARV responses in Ghana and Equatorial Guinea leveraged rapid response teams and surveillance. RVFV responses in Mauritania and Rwanda combined veterinary and vector measures. Nevertheless, reporting on effectiveness was inconsistent, few studies quantified incidence reduction, time-to-containment, reproduction number changes, or excess-risk attenuation [50]. Identified barriers included delayed recognition, limited diagnostics, under-resourced facilities, and IPC gaps, especially early in outbreaks and in nosocomial settings [51]. Differential impacts were noted in vulnerable groups (e.g., sex workers, immunocompromised individuals in Mpox clusters), underscoring the need for contextualized risk communication and service accessibility [27, 52].
Although zoonoses inherently demand multisectoral action, only a small fraction of the studies explicitly operationalized One Health (≈ 7.7%), typically during RVFV events integrating joint human–animal surveillance, cross-sector field investigations, and molecular diagnostics. Most reports mentioned reservoirs without corresponding veterinary or ecological interventions, indicating an implementation and documentation gap. Institutionalizing cross-sector frameworks, interoperable data systems, and shared field protocols, consistent with Quadripartite (WHO–WOAH–FAO–UNEP) guidance, remains a central unmet need [53].
Observed trends are consistent with broader literature: ecological factors (rainfall anomalies, flooding, land-use change), social or behavioural risks (bushmeat handling, caregiving or burial practices, unprotected sexual contact), and structural determinants (urban density, mobility, health-system capacity) jointly shape spillover and amplification [54, 55]. Political determinants, including governance stability, leadership commitment, coordination, and public trust, also influence outbreak dynamics; strong political will and cross-sectoral collaboration enable early detection and control, whereas weak governance, insecurity, and poor coordination prolong transmission and elevate case-fatality rates [56, 57]. Compared with high-income settings, SSA outbreaks more frequently encounter diagnostic delays, supply constraints, and IPC gaps, which can elevate CFRs, particularly for filoviruses and CCHFV, while Mpox CFRs remain lower but increase in immunocompromised populations. Where integrated surveillance and rapid diagnostics are available, containment is faster and secondary transmission is reduced, mirroring gains seen in other regions.
Policy and research implications of these findings are clear. Strengthening preparedness requires the institutionalization of One Health at national and subnational levels through mandated joint surveillance, co-financed veterinary and public health operations, and shared laboratory and data systems. Event-based and community surveillance should be reinforced with real-time data flows and feedback loops to local responders, while entomological and animal reservoir surveillance must be scaled up to enable species-specific vector and tick control and targeted livestock measures. Standardized response metrics, including the reproduction number, time to isolation, days to laboratory confirmation, attack rate, and case fatality rates stratified by context, are urgently needed for comparative evaluation across outbreaks. Parallel investment is necessary to expand surge diagnostic capacity and infection prevention and control, including rapid testing, protective equipment, and isolation capacity, combined with tailored risk communication for high-risk and marginalized populations. In addition, the integration of climate and mobility analytics, such as rainfall anomalies, droughts, or pastoralist movements, into predictive models would enhance early warning and pre-emptive controls. Finally, building interoperable data systems across human, animal, and environmental health sectors, in line with Quadripartite guidance, is critical for strengthening coordinated preparedness and response.
This review has several limitations. From the initial 228,000 Google and 843,000 Google Scholar results, only the first 100 and 200 articles, respectively, were screened, following established rapid appraisal methods for large databases, which may have led to omission of some relevant studies [21]. We prioritized breadth over critical appraisal; consistent with guidance, we did not perform formal risk-of-bias assessments [23]. Grey literature and situation reports may vary in completeness and verification. To mitigate this, we triangulated across multiple sources where available. Language restrictions (English-only) and the 2005–2025 window may exclude relevant evidence. Heterogeneity in outbreak definitions, case ascertainment, and reporting limited quantitative synthesis. We addressed this by presenting standardized summary tables and narratively synthesizing patterns rather than pooling estimates. Limited documentation of intervention outcomes constrained inferences about effectiveness. Some relevant outbreak reports, particularly recurrent Ebola virus investigations in the Democratic Republic of the Congo, may not have been captured due to their dissemination through grey literature or non-indexed sources. Future studies should report comparable metrics to enable cross-setting evaluation.
Conclusion
Viral zoonotic outbreaks continue to pose a major public health threat in SSA, reflecting the interplay of ecological, socio-economic, and behavioural drivers at the human, animal, environment interface. This review shows that, despite progress in outbreak detection and response, critical weaknesses persist in surveillance, preparedness, and cross-sector coordination. Early detection must be reinforced through community-based and event-driven surveillance linked to rapid laboratory confirmation, while health system capacity for diagnostics, IPC, and emergency logistics requires further investment.
Scaling up the One Health approach is central to improving preparedness. This involves not only policy endorsement but also practical mechanisms for multisectoral collaboration, joint outbreak investigations, integrated data systems, and sustainable funding. More systematic application of One Health principles will strengthen early warning and enhance coordinated responses. At the same time, operational research is needed to evaluate interventions using standardized metrics, while ecological and longitudinal studies should be expanded to better understand spillover dynamics. Integrating climate and mobility analytics into predictive models will further support proactive outbreak control.
In summary, embedding a stronger and operationalized One Health framework into policy, practice, and research agendas is essential for reducing the risk and impact of future zoonotic pandemics in Africa.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We express our sincere appreciation to the German-West African Centre for Global Health and Pandemic Prevention (G-WAC) and the Berlin University Alliance (BUA) for providing both financial and technical support.
Author contributions
SA, PED, JMG, GA, AAS, RMD, WT, YAS, ROP, CD, MO made substantial contributions to the conception, design and write-up of this review. SA and JMG performed the screening, study selection and data extraction from all studies using the eligibility criteria. All authors approved the final version of this manuscript.
Funding
The work has been made possible by the German Academic Exchange Service (DAAD) through the German-West African Centre for Global Health and Pandemic Prevention (G-WAC) scholarship as part of the Global Centers Program funded by the German Federal Foreign Office. Additional financial support for supervision was made available by the Flattening the Curve Project of the Berlin University Alliance (BUA).
Data availability
No datasets were generated or analyzed during the current study.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Karesh WB, Dobson A, Lloyd-Smith JO, Lubroth J, Dixon MA, Bennett M, et al. Ecology of zoonoses: natural and unnatural histories. Lancet. 2012;380:1936–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Moyo E, Mhango M, Moyo P, Dzinamarira T, Chitungo I, Murewanhema G. Emerging infectious disease outbreaks in Sub-Saharan Africa: Learning from the past and present to be better prepared for future outbreaks. Front Public Health [Internet]. 2023 [cited 2025 May 12];11:1049986. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10203177/. [DOI] [PMC free article] [PubMed]
- 3.Johnson CK, Hitchens PL, Smiley Evans T, Goldstein T, Thomas K, Clements A, et al. Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci Rep [Internet]. 2015 Oct 7 [cited 2023 Feb 2];5(1):1–8. Available from: https://www.nature.com/articles/srep14830. [DOI] [PMC free article] [PubMed]
- 4.Ateudjieu J, Siewe Fodjo JN, Ambomatei C, Tchio-Nighie KH, Zoung Kanyi Bissek AC. Zoonotic diseases in Sub-Saharan Africa: a systematic review and meta-analysis. Zoonotic Dis. 2023;3:251–65. 10.3390/zoonoticdis3040021
- 5.Ebola disease [Internet]. [cited 2025 Oct 2]. Available from: https://www.who.int/news-room/fact-sheets/detail/ebola-disease?utm.
- 6.Hartman A. Rift Valley fever. Clin Lab Med [Internet]. 2017 Jun 1 [cited 2025 Oct 3];37(2):285. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5458783/. [DOI] [PMC free article] [PubMed]
- 7.WHO. Mpox [Internet]. 2024 [cited 2025 Oct 3]. Available from: https://www.who.int/news-room/fact-sheets/detail/mpox?utm.
- 8.Woolhouse MEJ, Gowtage-Sequeria S. Host range and emerging and reemerging pathogens. Emerg Infect Dis [Internet]. 2005 [cited 2025 Apr 9];11(12):1842. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3367654/. [DOI] [PMC free article] [PubMed]
- 9.Wang CX, Xiu LS, Hu QQ, Lee TC, Liu J, Shi L, et al. Advancing early warning and surveillance for zoonotic diseases under climate change: interdisciplinary systematic perspectives. Adv Clim Change Res [Internet]. 2023 Dec 1 [cited 2025 Jul 15];14(6):814–26. Available from: https://www.sciencedirect.com/science/article/pii/S167492782300151X?utm_source=chatgpt.com.
- 10.Mariner JC, Raizman E, Pittiglio C, Bebay C, Kivaria F, Lubroth J, et al. Rift Valley fever action framework. Food & Agriculture Organization; 2022. Vol. 29.
- 11.Health worker Ebola infections in Guinea, Liberia and Sierra Leone: a preliminary report. 2015.
- 12.Huber C, Finelli L, Stevens W. The economic and social burden of the 2014 Ebola outbreak in West Africa. J Infect Dis [Internet]. 2018 Nov 22 [cited 2025 Oct 3];218(Suppl 5):S698–704. Available from: 10.1093/infdis/jiy213. [DOI] [PubMed]
- 13.WHO. One Health [Internet]. 2023 [cited 2025 May 12]. Available from: https://www.who.int/news-room/fact-sheets/detail/one-health.
- 14.CDC. About One Health [Internet]. 2024 [cited 2025 May 12]. Available from: https://www.cdc.gov/one-health/about/index.html.
- 15.WHO. Managing epidemics: key facts about major deadly diseases. 2023.
- 16.Mackenzie JS, Jeggo M. The One Health approach—why is it so important? Trop Med Infect Dis [Internet]. 2019 May 31 [cited 2025 May 12];4(2):88. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6630404/. [DOI] [PMC free article] [PubMed]
- 17.The World Bank. One Health: operational framework. 2018;152.
- 18.Anyamba A, Chretien JP, Small J, Tucker CJ, Formenty PB, Richardson JH, et al. Prediction of a Rift Valley fever outbreak. Proc Natl Acad Sci U S A [Internet]. 2009 Jan 20 [cited 2025 Oct 3];106(3):955–9. Available from: 10.1073/pnas.0806490106?download. [DOI] [PMC free article] [PubMed]
- 19.Buregyeya E, Atusingwize E, Nsamba P, Musoke D, Naigaga I, Kabasa JD, et al. Operationalizing the One Health approach in Uganda: challenges and opportunities. J Epidemiol Glob Health [Internet]. 2020 Dec 1 [cited 2025 Oct 3];10(4):250. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7758849/. [DOI] [PMC free article] [PubMed]
- 20.Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med [Internet]. 2018 Oct 2 [cited 2025 May 12];169(7):467–73. Available from: https://pubmed.ncbi.nlm.nih.gov/30178033/. [DOI] [PubMed]
- 21.Haddaway NR, Collins AM, Coughlin D, Kirk S. The role of Google Scholar in evidence reviews and its applicability to grey literature searching. PLoS One [Internet]. 2015 Sep 17 [cited 2025 Oct 11];10(9):e0138237. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC4574933/. [DOI] [PMC free article] [PubMed]
- 22.WHO. Health Regulations. 3rd ed. 2005.
- 23.Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol [Internet]. 2005 Feb [cited 2025 May 12];8(1):19–32. Available from: https://www.tandfonline.com/doi/pdf/10.1080/1364557032000119616.
- 24.Africa CDC. Mpox outbreak in South Africa [Internet]. [cited 2025 Apr 20]. Available from: https://africacdc.org/news-item/mpox-outbreak-in-south-africa/.
- 25.Christie A, Davies-Wayne GJ, Cordier-Lassalle T, Blackley DJ, Laney AS, Williams DE, et al. Possible sexual transmission of Ebola virus - Liberia, 2015. MMWR Morb Mortal Wkly Rep. 2015;64(17):479–81. [PMC free article] [PubMed] [Google Scholar]
- 26.Ogoina D, James IH. Mpox in a female sex worker in nigeria: A case report. IJID Reg. 2023;7:143–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Masirika LM, Udahemuka JC, Schuele L, Nieuwenhuijse DF, Ndishimye P, Boter M, et al. Epidemiological and genomic evolution of the ongoing outbreak of clade Ib mpox virus in the eastern Democratic Republic of the Congo. Nat Med [Internet]. 2025. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218678129%26doi=10.1038%2Fs41591-025-03582-1%26partnerID=40%26md5=3d75d642b92258863e74845e292e51df. [DOI] [PMC free article] [PubMed]
- 28.Tabassum S, Naeem F, Azhar M, Naeem A, Oduoye MO, Dave T. Rift Valley fever virus outbreak in Mauritania yet again in 2022: no room for complacency. Health Sci Rep. 2023;6(5):e1278. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Remera E, Rwagasore E, Nsekuye O, Semakula M, Gashegu M, Rutayisire R, et al. Rift Valley fever Epizootic, Rwanda, 2022. Emerg Infect Dis. 2024;30(10):2191–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Nyakarahuka L, Ojwang J, Tumusiime A, Balinandi S, Whitmer S, Kyazze S, et al. Isolated case of Marburg virus Disease, Kampala, Uganda, 2014. Emerg Infect Dis. 2017;23(6):1001–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Besombes C, Mbrenga F, Malaka C, Gonofio E, Schaeffer L, Konamna X, et al. Investigation of a mpox outbreak in Central African Republic, 2021–2022. One Health [Internet]. 2023;16. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150525383%26doi=10.1016%2Fj.onehlt.2023.100523%26partnerID=40%26md5=2718ff33117475ecf88307eae98e457e. [DOI] [PMC free article] [PubMed]
- 32.Chengula AA, Kasanga CJ, Mdegela RH, Sallu R, Yongolo M. Molecular detection of Rift Valley fever virus in serum samples from selected areas of Tanzania. Trop Anim Health Prod [Internet]. 2014;46(4):629–34. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896041539%26doi=10.1007%2Fs11250-014-0540-6%26partnerID=40%26md5=61d88be4709c6727958a1a4026590791. [DOI] [PubMed]
- 33.Nguku PM, Sharif SK, Mutonga D, Amwayi S, Omolo J, Mohammed O, et al. An investigation of a major outbreak of Rift Valley fever in Kenya: 2006–2007. Am J Trop Med Hyg [Internet]. 2010;83(2 Suppl):5–13. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955598320%26doi=10.4269%2Fajtmh.2010.09-0288%26partnerID=40%26md5=dfd2e5de9273243db00c993993b0f348. [DOI] [PMC free article] [PubMed]
- 34.Barry Y, Elbara A, Bollahi MA, Ould El Mamy AB, Fall M, Beyit AD, et al. Rift Valley fever, Mauritania, 2020: lessons from a One Health approach. One Health [Internet]. 2022;15. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132970343%26doi=10.1016%2Fj.onehlt.2022.100413%26partnerID=40%26md5=4c4f3cf350c33e85eb217d08a29d1eaf. [DOI] [PMC free article] [PubMed]
- 35.Konongoi L, Ofula V, Nyunja A, Owaka S, Koka H, Makio A, et al. Detection of dengue virus serotypes 1, 2 and 3 in selected regions of Kenya: 2011–2014. Virol J [Internet]. 2016;13(1):1–11. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994031825%26doi=10.1186%2Fs12985-016-0641-0%26partnerID=40%26md5=602a7d2d2ef285d114bb19cd255ccc85. [DOI] [PMC free article] [PubMed]
- 36.Atim SA, Niebel M, Ashraf S, Vudriko P, Odongo S, Balinandi S, et al. Prevalence of Crimean-Congo haemorrhagic fever in livestock following a confirmed human case in Lyantonde district, Uganda. Parasit Vectors. 2023;16(1):7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Ahmed A, Ali Y, Salim B, Dietrich I, Zinsstag J. Epidemics of Crimean-Congo hemorrhagic fever (CCHF) in Sudan between 2010 and 2020. Microorganisms [Internet]. 2022;10(5). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128872277%26doi=10.3390%2Fmicroorganisms10050928%26partnerID=40%26md5=201b68d397c10c4f103a315cd5475ad4. [DOI] [PMC free article] [PubMed]
- 38.Mirembe BB, Musewa A, Kadobera D, Kisaakye E, Birungi D, Eurien D, et al. Sporadic outbreaks of crimean-congo haemorrhagic fever in Uganda, July 2018-January 2019. PLoS Negl Trop Dis. 2021;15(3):e0009213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Yaro CA, Kogi E, Opara KN, Batiha GES, Baty RS, Albrakati A, et al. Infection pattern, case fatality rate and spread of Lassa virus in Nigeria. BMC Infect Dis [Internet]. 2021;21(1). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100569831%26doi=10.1186%2Fs12879-021-05837-x%26partnerID=40%26md5=72e747d88e099f77b67df3f73e50e283. [DOI] [PMC free article] [PubMed]
- 40.Keita M, Cherif MS, Sivahera B, Boland ST, Banza-Mutoka F, Kourouma M, et al. Case report: COVID-19 and Lassa fever coinfection in an Ebola suspected patient in Guinea. Am J Trop Med Hyg [Internet]. 2022;106(4):1094–7. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129471786%26doi=10.4269%2Fajtmh.21-0713%26partnerID=40%26md5=e7219ab25e83e9ab6529d63896f2653b. [DOI] [PMC free article] [PubMed]
- 41.Nyakarahuka L, Shoemaker TR, Balinandi S, Chemos G, Kwesiga B, Mulei S, et al. Marburg virus disease outbreak in Kween district Uganda, 2017: epidemiological and laboratory findings. PLoS Negl Trop Dis. 2019;13(3):e0007257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Adjemian J, Farnon EC, Tschioko F, Wamala JF, Byaruhanga E, Bwire GS, et al. Outbreak of Marburg hemorrhagic fever among miners in Kamwenge and Ibanda Districts, Uganda, 2007. J Infect Dis. 2011;204(SupplSuppl 3):S796–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Africa CDC. Marburg virus disease outbreak in Rwanda [Internet]. [cited 2025 Apr 20]. Available from: https://africacdc.org/news-item/marburg-virus-disease-outbreak-in-rwanda/.
- 44.WHO. Ebola response roadmap situation report 1 summary. 2014 Dec 17 [cited 2025 May 26]; Available from: http://esa.un.org/unpd/wpp/Excel-Data/population.htm.
- 45.Chérif MS, Koonrungsesomboon N, Kassé D, Cissé SD, Diallo SB, Chérif F, et al. Ebola virus disease in children during the 2014–2015 epidemic in guinea: a nationwide cohort study. Eur J Pediatr. 2017;176(6):791–6. [DOI] [PubMed] [Google Scholar]
- 46.Africa CDC. Press release on Marburg virus disease in Equatorial Guinea [Internet]. [cited 2025 Apr 20]. Available from: https://africacdc.org/news-item/press-release-on-marburg-virus-disease-in-equatorial-guinea/.
- 47.WHO. Marburg virus disease - Ghana [Internet]. 2022 [cited 2025 Apr 30]. Available from: https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON409.
- 48.Ishema L, Colombe S, Ndayisenga F, Uwibambe E, Van Damme E, Meudec M, et al. One Health investigation and response to a nationwide outbreak of Rift Valley fever in Rwanda – March to December 2022. One Health [Internet]. 2024;19. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198528709%26doi=10.1016%2Fj.onehlt.2024.100854%26partnerID=40%26md5=e79b7d47eca87766bbe7bf5d58a760f9. [DOI] [PMC free article] [PubMed]
- 49.Markwardt R, Sorosjinda-Nunthawarasilp P. Innovations in the entomological surveillance of vector-borne diseases [Internet]. 2021 [cited 2025 Jul 15]. 314 p. Available from: https://www.researchgate.net/publication/359336598_Innovations_in_Entomological_Surveillance_of_Vector-borne_DIseases.
- 50.Morse SS, Mazet JAK, Woolhouse M, Parrish CR, Carroll D, Karesh WB, et al. Prediction and prevention of the next pandemic zoonosis. The Lancet [Internet]. 2012 Dec 1 [cited 2025 Jun 8];380(9857):1956–65. Available from: https://www.thelancet.com/action/showFullText?pii=S0140673612616845. [DOI] [PMC free article] [PubMed]
- 51.Dunn AC, Walker TA, Redd J, Sugerman D, McFadden J, Singh T, et al. Nosocomial transmission of Ebola virus disease on pediatric and maternity wards: Bombali and Tonkolili, Sierra Leone, 2014. Am J Infect Control. 2016;44(3):269–72. [DOI] [PubMed] [Google Scholar]
- 52.Cutler SJ, Van Der Fooks AR. Public health threat of new, reemerging, and neglected zoonoses in the industrialized world. Emerg Infect Dis [Internet]. 2010 Jan [cited 2023 May 9];16(1):1–7. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC2874344/. [DOI] [PMC free article] [PubMed]
- 53.WHO. One Health joint plan of action (2022–2026): working together for the health of humans, animals, plants and the environment [Internet]. 2022 [cited 2025 Jul 5]. Available from: https://www.who.int/publications/i/item/9789240059139.
- 54.Karesh WB, Dobson A, Lloyd-Smith JO, Lubroth J, Dixon MA, Bennett M, et al. Ecology of zoonoses: natural and unnatural histories. Lancet [Internet]. 2012 Dec 1;1936–45. Available from: https://www.thelancet.com/action/showFullText?pii=S014067361261678X. [DOI] [PMC free article] [PubMed]
- 55.Bonwitt J, Dawson M, Kandeh M, Ansumana R, Sahr F, Brown H, et al. Unintended consequences of the ‘bushmeat ban’ in West Africa during the 2013–2016 Ebola virus disease epidemic. Soc Sci Med [Internet]. 2018 Mar 1 [cited 2025 Jun 10];200:166–73. Available from: https://www.sciencedirect.com/science/article/pii/S027795361730758X?via%3Dihub. [DOI] [PubMed]
- 56.WHO. Report of the Ebola Interim Assessment Panel [Internet]. 2015 [cited 2025 Oct 21]. Available from: https://www.who.int/docs/default-source/documents/evaluation/report-ebola-interim-assessment-panel.pdf?sfvrsn=df4e705d_2&utm.
- 57.Oleribe OO, Crossey MME, Taylor-Robinson SD. Nigerian response to the 2014 Ebola viral disease outbreak: lessons and cautions. Pan Afr Med J [Internet]. 2015 [cited 2025 Oct 21];22(Suppl 1):13. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC4695516/. [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analyzed during the current study.