SOPHIE

SOPHIE reconstructs transmission networks from viral phylogenies by integrating maximum likelihood phylogenetic inference with random graph models to support transmission history reconstruction in viral outbreak investigations.

Key Features:

• Integration of Phylogenetics and Network Theory: SOPHIE employs a maximum likelihood framework that combines phylogenetic inference with random graph models with specified degree distributions to construct transmission networks directly from viral phylogenies.
• Scalability and Intra-Host Diversity Consideration: SOPHIE is designed to scale to large outbreak datasets and explicitly accounts for intra-host viral diversity in transmission reconstructions.
• Independence from Case-Specific Epidemiological Data: SOPHIE can infer transmission networks without relying on sampling times or infection exposure intervals.
• Addressing Common Source Outbreaks: SOPHIE models network-dependent transmission events to accommodate common-source outbreaks instead of assuming independent transmission events.

Scientific Applications:

• Transmission history reconstruction: Use genomic data and integrated phylogenetic–network models to infer transmission histories during viral outbreaks.
• Outbreak analysis in data-limited settings: Study persistent infections in vulnerable populations or outbreaks in resource-constrained environments where epidemiological details are scarce or unreliable.
• Investigation of common-source outbreaks: Analyze outbreaks driven by common sources by modeling interconnected transmission events within a social network context.

Methodology:

SOPHIE uses maximum likelihood principles to integrate phylogenetic trees and random graph models with specified degree distributions, constructs transmission networks directly from viral phylogenies, and incorporates intra-host viral diversity.