Deterministic Modeling of the MVE-17 Outbreak Trajectory: Implications of Surveillance Attrition and Structural Saturation in the DRC
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Title: Deterministic Modeling of the MVE-17 Outbreak Trajectory: Implications of Surveillance Attrition and Structural Saturation in the DRC
Description This study presents a high-precision deterministic analysis of the 17th Bundibugyo ebolavirus (MVE-17) outbreak in the Democratic Republic of the Congo, covering the period from May 14 to June 27, 2026. Utilizing data from the Institut National de Santé Publique (INSP RDC) Situation Reports (001–044), the research employs a modified SEIR epidemiological model to evaluate the primary drivers of epidemic progression.
Unlike traditional models that focus solely on contact rates, this work identifies structural infrastructure saturation—specifically diagnostic turnaround times and isolation center occupancy—as the primary determinants of the epidemic trajectory. Key findings include:
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Epidemiological Thresholds: The effective reproduction number remains significantly above unity, sustained by a 12.9% surveillance leakage and severe diagnostic delays in the Nord-Kivu province.
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Operational Bottlenecks: Analysis confirms that high case fatality rates (up to 54.8% in Nord-Kivu) are artifacts of diagnostic backlogs and facility overcrowding (138.8% bed occupancy) rather than viral biology.
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Actionable Directives: The paper provides a strategic framework for health authorities to force an inflection point in transmission, requiring a 95% surveillance capture threshold, diagnostic velocity of under 24 hours, and expanded isolation capacity.
This submission includes the research findings and an integrated Python-based operational decision-support tool, enabling health coordinators to simulate the impact of specific infrastructure interventions on outbreak trajectory. This document serves as a critical resource for policymakers, epidemiologists, and public health practitioners managing high-stakes containment efforts in resource-constrained settings.