Traditional epidemic models frequently assume behavioral homogeneity. The susceptible-infected-recovered model provides a robust foundation for characterizing disease transmission, but it does so without accounting for how people actually respond to risk. In contrast, behavioral change models incorporate mechanisms that capture how individuals adjust their actions during an outbreak, recognizing that rising infection risk typically motivates protective behaviors. Yet both approaches share a key limitation: they overlook the inherent heterogeneity of a population. In reality, communities are a complex mixture of risk tolerances and behavioral tendencies. Ignoring this inherent heterogeneity can obscure important differences in how individuals perceive and respond to disease threats. This paper introduces a novel Bayesian mixture model designed to address this limitation by partitioning the population into two distinct behavioral patterns: risk-neutral individuals, who maintain baseline contact rates, and risk-averse individuals, who modulate their behavior in response to epidemic severity. By integrating these disparate dynamics into a unified transmission framework, the proposed model explicitly accounts for varying population behaviors often overlooked by aggregate approaches. Through simulation studies and empirical data applications, we demonstrate that this approach significantly outperforms traditional models in parameter recovery, epidemic trajectory estimation, and forecasting precision. The findings suggest that failing to account for behavioral diversity leads to biased peak estimates and artificially stretched epidemic curves. Consequently, this research provides a more nuanced computational toolkit for predicting outbreak trajectories in socially fragmented environments, ensuring that public health intervention strategies are informed by a foundation of behavioral realism.

翻译：传统流行病模型通常假设行为同质性。易感-感染-康复模型为描述疾病传播提供了坚实的基础，但未能考虑人们实际如何应对风险。相比之下，行为变化模型纳入了捕捉个体在疫情暴发期间调整自身行为的机制，认识到感染风险的上升通常会促使保护性行为。然而，这两种方法共有一个关键局限性：它们忽视了人群固有的异质性。现实中，社区是由风险容忍度和行为倾向构成的复杂混合体。忽视这种固有异质性会掩盖个体在感知和应对疾病威胁时的重要差异。本文提出一种新颖的贝叶斯混合模型，旨在通过将人群划分为两种截然不同的行为模式来解决这一局限性：风险中性个体（维持基准接触率）和风险规避个体（根据疫情严重程度调整行为）。通过将这些异质性动态整合到一个统一的传播框架中，所提出的模型明确考虑了群体方法常忽视的多样化人群行为。通过模拟研究和实证数据应用，我们证明该方法在参数恢复、疫情轨迹估计和预测精度方面显著优于传统模型。研究结果表明，未能考虑行为多样性会导致峰值估计偏差，并人为拉长疫情曲线。因此，本研究为预测社会分化环境中的疫情暴发轨迹提供了更精细的计算工具，确保公共卫生干预策略建立在行为现实性的基础之上。