Integrating Household Dynamics in Stochastic Epidemic Modeling: An SDE Approach to the SIR Framework

TL;DR

This paper develops a stochastic SIR model incorporating household structure and heterogeneity, providing more accurate epidemic predictions and insights for public health interventions.

Contribution

It introduces a novel stochastic differential equation model that captures household dynamics and heterogeneity in epidemic spread, extending traditional SIR models.

Findings

Household structure significantly alters epidemic timing and peak intensity.

Stochastic modeling captures outbreak variability overlooked by deterministic models.

Derived basic reproduction number considering household interactions.

Abstract

Understanding infectious disease spread remains a critical public health challenge, particularly given the interplay between household dynamics and community transmission patterns. Traditional epidemiological models often oversimplify these dynamics by treating populations as homogeneous, failing to capture crucial household-level interactions that can significantly impact disease spread. This paper introduces a new stochastic differential equation model extending the SIR framework by capturing the randomness in disease spread and incorporating household structure and heterogeneous mixing patterns. The model divides the population into groups based on age and household size, includes subpopulation-targeted lockdown parameters and constructs detailed contact matrices accounting for both public and within-household interactions. Through the approximation of Markov jump processes by branching processes near the disease free equilibrium, we derive the basic reproduction number of our model and conduct global sensitivity analysis using Sobol indices to identify influential factors. Our simulations reveal that incorporating household structure leads to substantially different predictions compared to traditional models, particularly in epidemic timing and peak intensity. The stochastic framework captures important variations in outbreak trajectories overlooked by deterministic approaches, especially during early and peak phases. This work contributes to both mathematical epidemiology and practical public health planning by providing a sophisticated mathematical understanding of how population structure and randomness influence disease dynamics, offering insights for intervention strategies where household transmission plays a significant role.