Mapping the Global Evidence Base of Bundibugyo Ebolavirus Disease: A Systematic Scoping Review of Research Gaps and Preparedness Priorities

Background: Bundibugyo ebolavirus (BDBV) is a rare filovirus species with an estimated case fatality rate (CFR) ranging from 25% to 51%. The 2026 BDBV outbreak in the Democratic Republic of the Congo (DRC) was declared a Public Health Emergency of International Concern (PHEIC), highlighting the critical necessity of assessing global research preparedness for this pathogen. Objectives: To systematically map the global scientific evidence base on BDBV, characterize temporal and thematic publication trends, identify critical knowledge gaps, and formulate actionable priorities for public health response and clinical research. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we programmatically searched PubMed/MEDLINE, OpenAlex, and Semantic Scholar for literature published from 2007 through May 2026. From 4,379 screened records, 100 high-relevance studies were selected using a weighted composite relevance scoring system. Methodological quality was evaluated using the Risk of Bias 2.0 (RoB 2) framework. Results: Only 23% of the included studies specifically addressed BDBV, with the remainder focusing on broader ebolavirus topics dominated by Zaire ebolavirus (EBOV). No licensed vaccine, specific therapeutic agent, or point-of-care rapid diagnostic test validated for BDBV currently exists. While cross-reactive monoclonal antibodies (mAbs) show preclinical efficacy, clinical-grade validation remains absent, and longitudinal survivor cohort data are extremely sparse. Overall risk of bias was moderate, driven by concerns regarding missing outcome data (42% of studies) and selective reporting (41%). Conclusions: BDBV remains understudied relative to its epidemic potential. Sustained, internationally coordinated investments modeled on prior EBOV frameworks are required to transition from reactive to proactive research models.