School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, China.
School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, China.
PLoS One. 2024 Jul 23;19(7):e0307553. doi: 10.1371/journal.pone.0307553. eCollection 2024.
Despite much progress in exploring the coupled epidemic-awareness dynamics in multiplex networks, little attention has been paid to the joint impacts of behavioral observability and social proof on epidemic spreading. Since both the protective actions taken by direct neighbors and the observability of these actions have essential influence on individuals' decisions. Thus, we propose a UAPU-SIR model by integrating the effects of these two factors into the decision-making process of taking preventive measures. Specifically, a new state called taken protective actions is introduced into the original unaware-aware-unaware (UAU) model to characterize the action-taken state of individuals after getting epidemic-related information. Using the Microscopic Markov Chain Approach (MMCA), the methods and model are described, and the epidemic threshold is analytically derived. We find that both observability of protecting behaviors and social proof can reduce the epidemic prevalence and raise the epidemic threshold. Moreover, only if observability of protection actions reaches a certain threshold can accelerating information diffusion is able to inhibit disease spreading and result in higher epidemic threshold. We also discover that, reducing the forgetting rate of information is able to decrease epidemic size.
尽管在探索多重网络中耦合的疫情意识动态方面已经取得了很大进展，但对于行为可观察性和社会证明对疫情传播的联合影响，关注甚少。由于直接邻居采取的保护措施以及这些措施的可观察性对个人决策都有重要影响。因此，我们通过将这两个因素的影响纳入采取预防措施的决策过程，提出了一个 UAPU-SIR 模型。具体来说，我们在原始的无意识-意识-无意识 (UAU) 模型中引入了一个新的状态，称为采取保护措施的状态，以描述个体在获得与疫情相关的信息后采取行动的状态。使用微观马尔可夫链方法 (MMCA)，描述了方法和模型，并分析推导出了疫情阈值。我们发现，保护行为的可观察性和社会证明都可以降低疫情的流行程度并提高疫情阈值。此外，只有当保护措施的可观察性达到一定的阈值时，加速信息传播才能抑制疾病传播并导致更高的疫情阈值。我们还发现，降低信息遗忘率可以减少疫情规模。
