Topological Analysis of Surveillance Attrition and Geospatial Entry Gaps: Reconstructing Cryptic Transmission Chains via Automated Digital Ingestion in the MVE-17 Outbreak

Abstract and Overview This dataset and accompanying manuscript provide a topological analysis of the 2026 Bundibugyo ebolavirus (MVE-17) outbreak in the Democratic Republic of the Congo, focusing on the reporting window between May 14 and June 25, 2026. The research evaluates the systemic failure of the containment network to meet the required 95.0% contact-tracing coverage threshold during the outbreak's linear and geometric scale phases. Utilizing network graph theory and Markov chain assumptions, the analysis models contact-tracing decay curves, calculates transmission escape probabilities, and proposes a fully automated digital data ingestion framework to resolve spatial metadata gaps and achieve structural containment. Key Epidemiological Findings Systemic Surveillance Attrition: The active surveillance apparatus operated below vital thresholds, stabilizing at a national follow-up rate of 82.8%, generating a 17.2% structural leakage across the aggregate exposure network of 9,305 contacts. Cryptic Community Transmission: The 17.2% tracking decay generated an unmonitored exposure vector of 1,605 individuals moving freely through the community, continuously initializing undetected transmission chains. Regional Degradation: In the Nord-Kivu compartment, acute security disruptions and high regional mobility reduced tracking compliance to just 56.4%, with only 684 of 1,212 total contacts monitored. Geospatial Entry Gaps: Database reconciliation uncovered 17 molecularly confirmed cases ("orphan nodes") completely detached from local health zone metadata. Mathematical Formalization of Transmission Dynamics The manuscript utilizes multi-type branching process models to define the transmission escape probability of clusters experiencing contact-tracing decay. Effective Reproduction Number: The effective reproduction within a tracked network is defined as a function of the basic reproduction number and the proportion of successfully traced contacts: Surveillance Attrition Function: The time-dependent decay parameter acting on the initial contact generation index. Transmission Escape Probability: The probability that a single introduction generates an undetected community cluster of generation. Matrix of Spatial Information Deficits The systematic entry of positive samples lacking spatial linkages generated "orphan nodes" that made targeted interventions operationally impossible. Ingestion Identifier Missing Metadata Attributes Operational Consequence ON-001 to ON-006 Health Zone / Aire de Santé / GPS Coordinates Complete failure to execute targeted ring isolation protocols; generation of unlinked clusters. ON-007 to ON-014 Specific Residence / Exposed Kin Contact Registry Inability to establish immediate epidemiological links; retrospective tracing requirements. ON-015 to ON-017 Inbound Commuter Route / Historical Transit Form Failure to intercept at border crossings; wide-area community diffusion tensors. Strategic Directives: Digital Ingestion Architectures To systematically dismantle the unmonitored transmission vector, the manuscript outlines the transition from manual, paper-based line-listing to a Real-Time Digital Ingestion Architecture (MMSH Configuration). Automated Spatial Geocoding: Positive localized assays (e.g., RADIONE hits) automatically generate an encrypted identity token within the global DHIS2 Tracker. Metadata Interlocking: Entry of precise health zone and residence coordinates is mandated before any diagnostic result can be officially closed. Latency Eradication: Automated cellular network handshakes compress contact registration time from a 48-hour manual synchronization delay to near-zero latency. Data Availability The anonymized individual line lists, tracked exposure vectors, and geospatial tables backing this network topology analysis reside within the secure emergency data layer of the national DHIS2 health registry. Researchers requiring access for non-commercial epidemiological modeling must submit formal data-sharing agreements directly to the Institut National de Santé Publique (INSP) administrative registry via the portal: dieudonne.mwamba@insp.cd.