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Updated: Apr 11, 2026

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

Ke Li1, Yiren Hou2, Bhramar Mukherjee2

  • 1Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.

Medrxiv : the Preprint Server for Health Sciences
|April 10, 2026
PubMed
Summary

HHBayes is a new R package for analyzing household transmission data. It simulates realistic scenarios and uses Bayesian methods to estimate susceptibility and intervention impacts, aiding infectious disease control.

Keywords:
Bayesian inferenceR packagehousehold transmissioninfectious disease modelingviral dynamics

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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.