Timeliness of Response and Case-Fatality Patterns in Ugandan Ebola Virus Disease Outbreaks (2000 – 2023): a Rapid Systematic Review and Meta-Analysis

This study will undertake a comprehensive systematic review and random‐effects meta-regression to characterize outbreak‐level case fatality rates (CFRs) of all ebolavirus species in Uganda from 2000 through 2023, and to quantify how delays between symptom onset and laboratory confirmation modify those risks. Following PRISMA 2020 guidelines, we will search PubMed, Embase, Web of Science, African Index Medicus and relevant grey-literature sources for published and unpublished outbreak reports. For each eligible report we will extract ebolavirus species, geographic location, year, number of confirmed cases and deaths (to calculate CFR), and metrics of timeliness (e.g., interval in days from symptom onset to sample collection and from sample collection to laboratory confirmation). Using random‐effects models we will derive pooled and species‐specific CFR estimates (Zaire ebolavirus, Sudan ebolavirus, Bundibugyo ebolavirus, etc.), assess between-study heterogeneity with I² statistics, and evaluate small-study effects with funnel plots and Egger’s test. Meta-regression analyses will then test the extent to which reporting and confirmation delays explain variability in CFRs across outbreaks. We expect this work to yield three key outputs: (1) robust, up-to-date pooled and species-specific CFR estimates for ebolavirus outbreaks in Uganda; (2) identification of critical timeliness thresholds—quantified as days from symptom onset to laboratory confirmation—beyond which mortality rises significantly; and (3) actionable recommendations for Uganda’s Integrated Disease Surveillance and Response (IDSR) guidelines, establishing explicit evidence-based targets for sample collection and confirmation intervals. By linking outbreak severity to surveillance timeliness, our findings will inform national outbreak preparedness and response strategies, support policy‐makers in setting quantifiable performance metrics, and ultimately contribute to reducing Ebola-related mortality in Uganda and similar settings