SIRE

SIRE estimates genetic (including single nucleotide polymorphism, SNP) and non-genetic effects on host susceptibility, infectivity, and recoverability to quantify factors governing infectious disease transmission.

Key Features:

• Simultaneous Estimation: Concurrently estimates SNP effects and non-genetic influences on susceptibility, infectivity, and recoverability.
• Bayesian Algorithm Implementation: Employs a flexible Bayesian algorithm for parameter inference from surveillance data.
• Data Type Compatibility: Handles any combination of recorded infection times, recovery times, or diagnostic test results.
• SIR Framework Modeling: Models multiple epidemics using the susceptible-infectious-recovered (SIR) framework.
• Simulation-Based Validation: Simulates genetic and non-genetic regulation scenarios to assess precision, accuracy, and bias in parameter estimation.
• Robustness to Incomplete Data: Accommodates incomplete, censored, and infrequent measurements of infection or survival status.
• Trait-Specific Precision and Power: Produces highest precision for SNP effects on recoverability, followed by susceptibility, and shows increased power to detect infectivity effects in epidemics with fewer individuals.

Scientific Applications:

• Genetic Discovery and Validation: Estimates SNP effects to identify and validate genetic factors that influence disease transmission dynamics.
• Intervention Strategies: Quantifies genetic and non-genetic determinants of host traits to inform targeted interventions aimed at reducing transmission.
• Experimental and Field Data Analysis: Applies to analysis of both experimental and field epidemic data for parameter estimation.

Methodology:

Implements Bayesian inference on epidemics modeled by the SIR framework using combinations of infection times, recovery times, and diagnostic test results, and uses simulation of genetic and non-genetic regulation scenarios to validate precision, accuracy, and bias while accommodating incomplete, censored, and infrequent measurements.