SCOTTI

SCOTTI reconstructs transmission trees of pathogens by integrating genetic (sequencing) and epidemiological data using a structured coalescent Bayesian framework implemented within the BEAST 2 package to infer transmission pathways and evolutionary history.

Key Features:

• Simultaneous data integration: Integrates pathogen genetic (sequencing) data with epidemiological information such as infection times and locations for joint inference of transmission dynamics.
• Structured coalescent inference: Uses structured coalescent models to reconstruct phylogenetic and transmission relationships among pathogen samples.
• Bayesian parameter estimation: Employs Bayesian inference to estimate parameters and provide credible intervals that quantify uncertainty in inferred quantities.
• BEAST 2 implementation: Implemented as part of the BEAST 2 package, enabling specification of evolutionary and population models within that framework.

Scientific Applications:

• Epidemiological studies: Reconstruction of transmission trees to identify sources of outbreaks, track spread between hosts or locations, and characterize patterns of pathogen evolution.
• Public health interventions: Identification of transmission events and clusters to inform targeted public health responses and intervention strategies.

Methodology:

Applies a structured coalescent approach using sequencing data to infer genetic relationships, integrates epidemiological data (e.g., infection times and locations) to contextualize transmission, and uses Bayesian inference to estimate parameters and credible intervals.