Pairwise Accelerated Failure Time Regression Models for Infectious Disease Transmission in Close-Contact Groups With...

Area of Science:

• Epidemiology
• Biostatistics
• Survival Analysis

Background:

• Infectious disease epidemiology often requires analyzing associations between covariates and disease transmission, which standard regression models struggle with due to dependent outcomes.
• Pairwise survival analysis addresses dependent outcomes by modeling contact interval distributions between individuals.

Purpose of the Study:

• Introduce a novel pairwise accelerated failure time regression model for infectious disease transmission.
• Enable the model to incorporate individual-level infectiousness and susceptibility covariates, as well as pair-level covariates.
• Simultaneously handle both internally transmitted and externally acquired infections.

Main Methods:

• Developed a pairwise accelerated failure time regression model for infectious disease transmission.
• The model's rate parameter depends on covariates for infectiousness, susceptibility, and pair relationships.
• Incorporated methods to handle both internal and external infection sources.

Main Results:

• The proposed model yields consistent and asymptotically normal parameter estimates.
• Simulation studies demonstrated the model's performance regarding bias and confidence interval coverage.
• Analysis of 2009 influenza A (H1N1) pandemic data showed increased statistical power when accounting for external infections.

Conclusions:

• The new regression model effectively analyzes infectious disease transmission with dependent outcomes.
• Accounting for external infection sources significantly enhances the statistical power to evaluate interventions.
• This approach offers a robust tool for infectious disease epidemiological research.