HHBayes: A Flexible Bayesian Framework for Simulating and Analyzing Household Transmission Dynamics.

Area of Science:

• Epidemiology
• Biostatistics
• Computational Biology

Background:

• Household transmission studies are crucial for understanding infectious disease dynamics and intervention effectiveness.
• Methodological challenges in study design, sample size, and parameter estimation often limit these studies.
• Existing tools lack flexibility in modeling age-specific factors and intervention impacts.

Purpose of the Study:

• To develop HHBayes, an open-source R package providing a unified framework for simulating and analyzing household transmission data using Bayesian methods.
• To enable researchers to simulate realistic transmission dynamics with customizable variables.
• To incorporate viral load data for time-varying infectiousness modeling and estimate age-dependent parameters.

Main Methods:

• Utilized Bayesian methods with Hamiltonian Monte Carlo implemented in Stan for parameter estimation.
• Developed functionalities for simulating household transmission dynamics with customizable variables.
• Incorporated viral load data (viral copies/mL or cycle threshold values) to model time-varying infectiousness.
• Enabled evaluation of intervention effects through user-defined covariates.

Main Results:

• Demonstrated accurate parameter recovery through simulation studies.
• Applied the package to seasonal respiratory virus transmission data.
• Showcased the impact of vaccination and antiviral prophylaxis on household attack rates.
• Validated the package's ability to handle complex household structures and time-varying infectiousness.

Conclusions:

• HHBayes addresses a critical gap in infectious disease epidemiology by providing accessible tools for study design and data analysis.
• The package's flexibility makes it valuable for studying diverse pathogens and evaluating intervention strategies.
• Facilitates a deeper understanding of infectious disease spread within households.