Most previous models for epidemic spreading are based on an assumption that all individual characteristics in a population are identical and stochastically distributed. However, in the real world, individuals have different behavioral characteristics for resisting infections, one of which is self-protective behavior. In this study, we suggest an equation describing self-protective behavior and use the equation in an agent-based model to simulate epidemic spreading in a population. The self-protective behavior was simply defined as the behavioral rule that when a susceptible individual meets an infective individual, the susceptible tends to move in the opposite direction of the infective individual. The degree of the tendency was quantified as a value of [Formula: see text] ranging from 0.0 to 1.0, with a higher [Formula: see text] representing a stronger tendency. The simulation results showed that when the recovery and infection probability are balanced to some extent, the [Formula: see text] effect clearly appeared. The [Formula: see text] effect led to a reduction in the number of infective individuals in a stable state. In addition, the effect decreased with an increase in population size. We briefly discuss how the results can be applied in real life situations.

中文翻译：

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个体自我保护行为对流行病传播的影响

大多数以前的流行病传播模型都基于一个假设，即群体中的所有个体特征都是相同的并且是随机分布的。然而，在现实世界中，个体在抵抗感染方面具有不同的行为特征，其中之一就是自我保护行为。在这项研究中，我们提出了一个描述自我保护行为的方程，并在基于代理的模型中使用该方程来模拟人群中的流行病传播。自我保护行为被简单地定义为当易感个体遇到感染个体时，易感个体倾向于向感染个体相反的方向移动的行为规则。趋势程度量化为[公式：见正文]的值，范围从0.0到1.0，[公式：见文字]代表更强的趋势。模拟结果表明，当恢复和感染概率达到一定程度平衡时，[公式：见正文]效应明显出现。[公式：见正文]效应导致处于稳定状态的感染个体数量减少。此外，这种影响随着人口规模的增加而下降。我们简要讨论了如何将结果应用于现实生活中。