INTRODUCTION
Marburg virus disease (MVD) is a highly lethal filovirus infection with case fatality rates of up to 88%. Although no virus-specific interventions have yet been approved, recent laboratory breakthroughs have markedly accelerated translational progress in vaccine and therapeutic development.
AREAS COVERED
A hybrid methodological design was employed, combining macro-level bibliometric analysis with a targeted qualitative narrative review. As per the findings, the human monoclonal antibody MR191N provides complete post-exposure protection in non-human primates. Advanced RNA-targeted siRNAs, antisense oligonucleotides, and small-molecule nucleoside analogues (e.g. galidesivir) demonstrate robust preclinical efficacy in suppressing viral replication. Prophylactic platforms - including adenovirus-vectored (ChAd3-MARV), rVSV-MARV, and mRNA-based vaccines - are progressing through early-phase clinical and preclinical evaluation. Bibliometric analysis, however, reveals pronounced geographic disparities: high-income nations dominate scientific output (United States: 31.7%), while endemic African countries remain severely underrepresented in global collaboration networks.
EXPERT OPINION
Addressing MVD requires a coordinated transition from experimental research to licensed, field-ready interventions. This necessitates adaptive trial designs, expanded diagnostic models, and synergistic combination therapies. Ultimately, correcting global research inequities and embedding robust, cross-border One Health surveillance infrastructure are essential to transforming scientific advances into equitable, rapid-response solutions for endemic regions.
Venkataramana Kandi, Francesco Branda, Mohammed Alissa et al.· Expert Review of Anti-Infect...· 0 citations
The study supports the potential usefulness of combining contact-based models with explainable graph neural networks for scenario-based epidemic analysis and suggests that the k-GCN model captures relevant temporal and structural dependencies in the simulated graph-organized data.
Francesco Branda, G. Ceccarelli, Massimo Ciccozzi et al.· Network Modeling Analysis in...· 0 citations