A comprehensive review published in the Journal of Emergency and Disaster Medicine by Sherief Musa of Cairo University’s Endemic Medicine Department synthesizes decades of research on the virus, drawing on 70 studies selected from an initial pool of 1,564 articles identified through PubMed, Web of Science, and African Journals Online, covering literature from 1968 through the end of 2025, together with guidance documents from the World Health Organization and the Centers for Disease Control and Prevention. The review arrives at a moment of genuine inflection: Marburg virus outbreaks have grown both more frequent and more geographically dispersed, with first-time occurrences reported in Guinea in 2021, Ghana in 2022, Equatorial Guinea and Tanzania in 2023, Rwanda in 2024, and now Ethiopia. The virus was first identified in 1967, when laboratory workers in Marburg and Frankfurt in Germany and in Belgrade in the former Yugoslavia fell ill after handling infected African green monkeys, Cercopithecus aethiops, imported from Uganda for pharmaceutical research. Since then, nearly twenty outbreaks have been documented, almost all in sub-Saharan Africa, ranging from isolated single cases to explosive community epidemics such as those in the Democratic Republic of the Congo in 1998 to 2000 and Angola in 2004 to 2005.

At the molecular level, Marburg virus is a non-segmented, single-stranded, negative-sense RNA virus of the family Filoviridae, a name derived from the Latin word for thread-like, a reference to the filamentous shape of viral particles. Unlike Ebola, the genus Marburgvirus contains a single species, Orthomarburgvirus marburgense, comprising two recognized variants, the Lake Victoria and Ravn viruses, whose genomes share at least 79 percent sequence homology. The viral genome spans approximately 19,000 bases and encodes seven genes arranged in a fixed order, each protein performing a specialized function in the viral life cycle. The nucleoprotein encapsidates the RNA genome into the nucleocapsid, essential for replication and transcription. Viral protein 35 acts as a polymerase cofactor while simultaneously suppressing interferon signaling, one of the body’s first lines of antiviral defense. Viral protein 40 drives the budding of new particles and further antagonizes the interferon response, and the surface glycoprotein binds host receptors to trigger internalization through endocytosis. The large protein, designated L, serves as the RNA-dependent RNA polymerase that copies the genome, and it is precisely this enzyme that several experimental antiviral drugs are designed to inhibit.

The pathogenesis of Marburg virus disease explains its devastating clinical course. After entering through mucosal surfaces or broken skin, the virus preferentially infects mononuclear phagocytic cells, including macrophages and dendritic cells. From these initial targets it spreads to regional lymph nodes and then disseminates through the bloodstream to the liver, spleen, and other lymphoid tissues, where it induces extensive necrosis. The virus suppresses the production of type I interferons and interferes with their signaling pathways, disabling innate immunity before it can mount an effective response. Dendritic cell activation is inhibited, impairing antigen presentation and leaving T-lymphocytes poorly stimulated, while inflammatory mediators drive so-called bystander apoptosis that depletes lymphocytes and hollows out adaptive immunity. Uncontrolled activation of infected macrophages then floods the circulation with pro-inflammatory cytokines such as interleukin-6 and tumor necrosis factor, producing the cytokine storm that drives vascular permeability upward and sets the stage for coagulation abnormalities.

The downstream consequences are what give the disease its hemorrhagic character. Endothelial injury, increased vascular leakage, and microvascular clotting culminate in disseminated intravascular coagulation, in which clotting factors are consumed faster than they can be replaced. Infection of hepatocytes impairs liver function and reduces the production of coagulation proteins, worsening bleeding tendencies, while infection of the adrenal cortex disrupts hormone production and destabilizes blood pressure regulation. Widespread vascular leakage depletes circulating blood volume, producing hypovolemic shock and falling perfusion to vital organs, and the terminal result is a shock-like syndrome combining vascular dysfunction, disseminated coagulopathy, and multi-organ failure. After an incubation period of 3 to 21 days, typically 5 to 10, the illness unfolds in three phases: a generalization phase of roughly five days marked by abrupt high fever around 40 degrees Celsius, severe headache, chills, myalgia, and prostration; an early organ phase from day 5 to day 13, dominated by gastrointestinal symptoms, escalating mucosal or gastrointestinal bleeding, and sometimes neurological manifestations including disorientation, agitation, seizures, and coma; and finally either recovery or a fatal outcome, typically during the second week of illness.

A recent aggregation of clinical data covering 325 patients, approximately 45 percent of all reported Marburg cases across five decades, has sharpened the clinical picture considerably. Fever proved the most consistent symptom, present in 91 percent of cases, followed by fatigue at 75 percent, headache at 64 percent, and myalgia at 47 percent. Gastrointestinal complaints were prominent, with nausea or vomiting in 66 percent of patients, diarrhea in 53 percent, and abdominal pain in 43 percent. Among hemorrhagic manifestations, hematemesis was the most frequent at 43 percent, followed by bloody diarrhea at 34 percent, bleeding gums at 23 percent, and epistaxis at 20 percent. The overall case fatality rate in that pooled analysis was 77 percent, falling to 44 percent among cases confirmed by polymerase chain reaction, and mortality across outbreaks has historically ranged from 25 to 80 percent depending on context and the quality of available medical care. Diagnosis remains difficult because early symptoms mimic malaria, typhoid fever, leptospirosis, and dengue, and hemorrhagic signs appear too late to guide early detection. Reverse transcription polymerase chain reaction testing of whole blood or plasma is the most reliable method, though a negative early sample does not exclude infection, and repeat testing is essential. Samples are extremely biohazardous and must be shipped in triple packaging for testing at biosafety level 3 or level 4 facilities, infrastructure that many African countries lack, and unlike Ebola, no field-validated rapid diagnostic test yet exists for Marburg virus.

Treatment remains the weakest pillar of the response. No licensed specific therapeutics exist, and the cornerstone of survival is intensive supportive care in designated treatment centers: intravenous fluids, vasopressors, and electrolyte correction to maintain hemodynamic stability; blood components to address hemorrhage; mechanical ventilation and renal replacement therapy for organ support; broad-spectrum antibiotics to prevent secondary bacterial infection; adequate nutrition; and psychological support. Rwanda’s 2024 outbreak demonstrated what such care can achieve, with a 77 percent survival rate that inverts historical fatality figures. Promising experimental agents are advancing through the pipeline, guided by knowledge of virus-host interactions. Small-molecule antivirals targeting the RNA-dependent RNA polymerase can impede replication, monoclonal antibodies against the viral glycoprotein have effectively neutralized the virus in preclinical studies, and phosphorodiamidate morpholino oligomers and small interfering RNAs targeting viral messenger RNAs have shown protective effects in non-human primates. Combination therapy pairing remdesivir with monoclonal antibodies was deployed during the 2024 Rwandan outbreak and reportedly improved outcomes. On the vaccine front, the World Health Organization’s Research and Development Blueprint established the Marburg Virus Vaccine Consortium to coordinate candidate development, and its technical advisory group has prioritized four viral-vectored candidates for human trials: two based on non-replicating chimpanzee adenoviruses, ChAd3 and ChAdOx1, and two on replicating vesicular stomatitis virus vectors. During the Ethiopian outbreak, the Ministry of Health reported that 2,500 doses of the cAd3-Marburg vaccine were offered to healthcare professionals and contacts of cases, echoing the experimental deployment of the ChAd3 vaccine in Rwanda in October 2024 as a real-world test of the 100-Day Mission, an initiative to develop and authorize emergency-use vaccines within 100 days of identifying an emerging pathogen.

Ethiopia’s containment of the outbreak offers lessons in adaptability. Health authorities, working with international partners, rapidly trained frontline healthcare workers, distributed critical supplies, intensified community-level monitoring, and traced contacts; as of 25 January 2026, a total of 857 contacts had been listed and had completed 21 days of follow-up. Epidemiological modeling suggests that without mitigation, Marburg virus propagates with a doubling time of 12 days in a susceptible population, but case isolation is effective if initiated no later than three days after symptom onset, underscoring the decisive value of speed. The World Health Organization declares an outbreak over 42 days, two consecutive incubation periods, after the last patient dies or tests negative and is discharged. Yet the regional risk did not vanish with Ethiopia’s declaration: the Africa Centres for Disease Control and Prevention reported suspected Marburg deaths in South Sudan in December 2025 and an alert in Wajaale, a border city in the Somaliland region, reflecting the danger of cross-border transmission along road networks connecting Ethiopia to Kenya, South Sudan, and Somalia.

The broader risk assessment is nuanced. The virus’s basic reproduction number is estimated at 1.59 with an average nine-day interval between successive cases, conditions under which large sustained epidemics are unlikely unless the virus mutates to enhance transmissibility, something experts recommend monitoring through sequence analysis of isolates from future outbreaks. A 2015 model estimated that up to 105 million people across 27 countries are vulnerable to zoonotic Marburg spillover, and the reservoir host, the Egyptian fruit bat Rousettus aegyptiacus, whose range defines the potential risk area, carries active infection in roughly 2 to 3 percent of bats at any time, with biannual seasonal pulses that coincide with heightened spillover risk. The 2014 to 2016 West African Ebola epidemic, which arose from a single spillover event and produced 28,000 cases and 11,000 deaths, stands as the cautionary precedent. Marburg virus is classified as a Category A bioterrorism agent by the CDC owing to its severity, mortality, and the absence of licensed countermeasures, adding an intentional-outbreak dimension to preparedness planning. With climate change, deforestation, mining, and urbanization expanding human-bat contact, and with the WHO and GAVI both ranking Marburg among priority pandemic threats, researchers and policymakers argue that sustained investment in diagnostics, vaccines, therapeutics, and One Health surveillance is no longer optional but essential to averting the next high-consequence filovirus crisis.

Subject of Research: Marburg virus disease epidemiology, pathogenesis, and outbreak preparedness in light of Ethiopia's first outbreak

Article Title: Marburg virus disease: the next threat in the making?

Article References: Musa, S. (2026). Marburg virus disease: the next threat in the making?. Journal of Emergency and Disaster Medicine, 2(1), Article 8. https://doi.org/10.1007/s44467-026-00011-2

Image Credits: AI Generated

DOI: 10.1007/s44467-026-00011-2

Keywords: Marburg virus, Marburg virus disease, filoviruses, hemorrhagic fever, zoonosis, Ethiopia outbreak, Rousettus aegyptiacus, vaccine development, antiviral therapeutics, outbreak preparedness, cytokine storm, global health security

The commentary’s timing is pointed. During the COVID-19 recovery period, Ghana simultaneously confronted outbreaks of Marburg virus and Mpox, all against a backdrop of rapid urbanization, increased human-wildlife interaction, shifting transmission dynamics, and environmental degradation. Each of these pressures exposed fresh cracks in modeling approaches that were largely conceived in high-resource settings and then, with often fatal assumptions intact, transplanted into radically different epidemiological soil. The authors contend that the resulting projections were not merely imprecise but potentially harmful, because policymakers relying on them may have misjudged transmission rates, disease severity, and the likely impact of interventions.

At the heart of the problem is data. As of 7 April 2024, Ghana had officially reported 172,075 confirmed COVID-19 cases and 1,462 confirmed deaths. But the researchers note that underreporting analyses suggest the true death toll may have been as much as 27.75 times higher. That staggering discrepancy is not a footnote; it is the difference between a model that calibrates correctly and one that drifts far from reality. The team illustrates the problem with mortality statistics drawn from parallel reporting systems: in 2020, the District Health Information Management System (DHIMS) recorded 38,429 deaths while the Births and Deaths Registry (BDR) logged 51,026; in 2021, DHIMS counted 43,569 against 55,349 in the BDR. Meanwhile, Ghana’s 2021 population census, covering June 2020 through June 2021, estimated 132,199 total deaths—meaning the two routine systems together missed more than half of all mortality events. A scaled-area visualization in the commentary shows BDR capturing roughly 41.9 percent of census-reference deaths and DHIMS2 roughly 32.9 percent, with Health and Demographic Surveillance System sites at Navrongo, Dodowa, and Kintampo adding partial coverage that overlaps with the others in ways revealed only through field triangulation.

The fragmentation runs deeper still. Ghana’s mortality and health data are dispersed across a patchwork of digital platforms—DHIMS, the Lightwave Health Information Management System (LHIMS), the Births and Deaths Registry, HDSS surveillance sites, individual mortuaries, and the Surveillance Outbreak Response Management and Analysis System (SORMAS), which was deployed during COVID-19 to produce daily situational updates. While these systems complement one another, their differing reporting mechanisms, collection methods, and coverage areas generate discrepancies and duplication in national statistics. The National Health Insurance Management System introduces another blind spot: it omits the substantial share of Ghanaians who pay out of pocket or carry private insurance. With only 54 percent of the population holding active National Health Insurance Scheme membership in 2021, models that depend on utilization and coverage data cannot be properly validated, and the effect of insurance coverage itself becomes analytically invisible.

Diagnostic constraints compound the data problem. COVID-19 detection in Ghana leaned heavily on polymerase chain reaction (PCR) testing, which requires samples collected during the acute phase of infection. Reconstructing past transmission dynamics therefore depends on archived samples from infection peaks—a resource made scarce by limited long-term storage capacity and the involvement of multiple laboratories with no centralized biobank. Serological surveys, the classic alternative for estimating cumulative infection, face their own technical hurdles: antibody evidence persists only within specific time windows, and assays can cross-react with the endemic human coronaviruses that circulate widely, requiring additional confirmatory testing that inflates both financial and human resource costs. The upshot, the authors write, is reduced confidence in any retrospective estimate of how the virus actually moved through the population.

But the commentary insists that data infrastructure is only one of three interlocking methodological challenges. The second concerns the evaluation of non-pharmaceutical interventions (NPIs)—mask mandates, social distancing, and similar measures. In higher-income countries, NPI implementation tends to be standardized and enforcement mechanisms are comparatively strong. Ghana, like many low- and middle-income countries, faces far greater variability in compliance, shaped by informal economies, communal living arrangements, and uneven trust in public health institutions. The effectiveness of any given intervention varies widely across regions depending on public trust, risk perception, socioeconomic conditions, and the presence of concurrent measures—which makes isolating the effect of any single intervention statistically treacherous. Existing studies of NPIs in Ghana have focused on selected areas or populations, leaving an incomplete nationwide picture, and the absence of standardized evaluation frameworks makes cross-community comparison nearly impossible.

The third challenge is what the researchers call social-behavioral parameterization: building the human element into the equations. Ghana’s population encompasses more than 70 ethnic and linguistic groups, with a pluralistic society in which regional practices, diverse religions, and community governance shape health behaviors, risk perceptions, and institutional trust. Roughly 30 percent of the population works in agriculture and subsistence farming, pastoralism concentrates in the north, and significant cross-border movement of people, goods, and livestock flows along all of Ghana’s borders, complicating biosecurity management. In rural areas especially, trust in informal networks—elders, religious leaders, traditional healers—strongly conditions public responses to official guidance. A compartmental model that assumes homogeneous mixing and uniform compliance simply cannot represent these dynamics; the parameters it needs, such as compliance levels and risk perception by demographic group, are precisely the ones hardest to measure and most often missing from LMIC datasets. Higher-income countries, by contrast, benefit from more homogeneous behavioral datasets and stronger institutional capacity for regular behavioral surveillance.

To close these gaps, the team proposes five targeted strategies. First, develop culturally adapted metrics for NPI effectiveness, grounded in regular community surveys that capture local attitudes and needs, so that model parameters reflect actual behavioral patterns rather than imported assumptions. Second, overhaul data infrastructure by integrating and centralizing health data from DHIMS, LHIMS, and HDSS into a unified digital repository—with phased implementation and capacity support—while expanding sentinel surveillance sites across diverse regions and implementing real-time reporting for mortality and compliance data. Third, establish national reference biobanks to centralize storage of biological samples, enabling nationwide retrospective studies; the authors are explicit that such investments demand sustained financing and technical capacity, and should be justified through cost-effectiveness evaluations that weigh implementation constraints like infrastructure and workforce availability. Fourth, create guidelines and partnerships for local model adaptation, including pilot testing and validation in selected regions before scaling, supported by regional partnerships, international collaboration, training curricula, and technology transfer programs. Fifth, strengthen a sustainable, needs-oriented financing strategy through increased domestic health funding, pooling of public and private resources, and Program-Based Budgeting that aligns spending with national priorities.

Underlying all five recommendations is a call for genuine interdisciplinarity. The authors argue that models will only track infections effectively while aligning with local realities when epidemiologists work alongside political scientists assessing risk perception and compliance, virologists supplying localized pathogen data, and health systems researchers mapping access disparities. Participatory research and policy co-design involving community leaders, they contend, improves not just model accuracy but also relevance, trust, and adherence—the social substrate on which any projection ultimately rests. At the same time, the team strikes a note of fiscal caution: investments in modeling methodology must be carefully weighed against potentially more critical healthcare needs, so that resource allocation remains responsive to Ghana’s broader public health priorities.

The stakes extend well beyond Ghana’s borders. The authors frame their argument as a test case for the broader problem of model transferability across resource settings—a difficulty documented in the methodological literature on combining data from multiple sources. If Ghana, with its high infectious disease burden, active regional role in surveillance, and strong political commitment to digital health strengthening, cannot make global modeling frameworks work, the implications are sobering for much of Sub-Saharan Africa and the wider Global South. Conversely, the researchers argue that success in Ghana could set a valuable precedent: a demonstration that context-sensitive, interdisciplinary, data-integrated modeling can deliver accurate, policy-relevant projections in settings where the classic assumptions of epidemic mathematics break down. By investing in disaggregated data, biobanks, community-engaged parameterization, and sustainable financing, the commentary concludes, Ghana can build a resilient public health architecture capable of protecting its population against future outbreaks—and, in doing so, help reshape the science of epidemic forecasting into something that works for everyone, not just for the countries whose data pipelines it was designed around.