Approche Multidimensionnelle Pour La Detection Automatisee Des Chaines De Transmission D’un Virus : Application Au Monkeypox en Republique Democratique Du Congo

Mpox (monkeypox), a re-emerging viral zoonosis caused by the MPXV virus, represents a growing global threat due to its human-to-human transmission and transcontinental spread. In the Democratic Republic of Congo (DRC), the persistence of epidemic hotspots and active viral circulation across multiple provinces intensifies this risk. In response to the limitations of traditional epidemiological approaches such as heavy logistics, bias, and low reproducibility, our study introduces an automated, multidimensional analytical platform developed using Python 3.10 and specialized libraries. The platform simultaneously analyzes genetic, geographic, and temporal dimensions using public viral sequences and metadata. It computes phylogenetic distances (via alignment-free cosine distance), geodesic distances, and sampling intervals. This approach enabled the examination of 118,341 case pairs, heuristic classification of transmission events, and detection of transmission chains. The reconstruction of 25 independent transmission chains linked to Kinshasa highlights its strategic role as both an epidemic hub and a point of interprovincial spread. Results are delivered through interactive visualizations (maps, graphs, reports), providing valuable decision support for public health authorities. Despite certain technical constraints and heterogeneous data, this platform marks a significant advancement in digital epidemiological surveillance.