Azizi Asma, Montalvo Cesar, Espinoza Baltazar, Kang Yun, Castillo-Chavez Carlos
School of Human Evolution and Social Change, Simon A. Levin Mathematical Computational Modeling Science Center, Arizona State University, Tempe, AZ, 85281, USA.
Division of Applied Mathematics, Brown University, Providence, RI, 02906, USA.
Infect Dis Model. 2019 Dec 11;5:12-22. doi: 10.1016/j.idm.2019.11.002. eCollection 2020.
Understanding individual decisions in a world where communications and information move instantly via cell phones and the internet, contributes to the development and implementation of policies aimed at stopping or ameliorating the spread of diseases. In this manuscript, the role of official social network perturbations generated by public health officials to slow down or stop a disease outbreak are studied over distinct classes of static social networks. The dynamics are stochastic in nature with individuals (nodes) being assigned fixed levels of education or wealth. Nodes may change their epidemiological status from susceptible, to infected and to recovered. Most importantly, it is assumed that when the prevalence reaches a pre-determined threshold level, , information, called awareness in our framework, starts to spread, a process triggered by public health authorities. Information is assumed to spread over the same static network and whether or not one becomes a informer, is a function of his/her level of education or wealth and epidemiological status. Stochastic simulations show that threshold selection and the value of the average basic reproduction number impact the final epidemic size differentially. For the Erdős-Rényi and Small-world networks, an optimal choice for that minimize the final epidemic size can be identified under some conditions while for Scale-free networks this is not case.
在一个通过手机和互联网实现通信和信息即时传递的世界中，理解个体决策有助于制定和实施旨在阻止或缓解疾病传播的政策。在本手稿中，我们研究了公共卫生官员在不同类型的静态社交网络上生成的官方社交网络扰动对减缓或阻止疾病爆发的作用。这种动态本质上是随机的，个体（节点）被赋予固定的教育水平或财富水平。节点的流行病学状态可能从易感转变为感染再到康复。最重要的是，假设当患病率达到预定阈值水平时，在我们的框架中称为意识的信息开始传播，这一过程由公共卫生当局触发。假设信息在相同的静态网络上传播，一个人是否成为传播者是其教育水平或财富水平以及流行病学状态的函数。随机模拟表明，阈值选择和平均基本再生数的值对最终疫情规模有不同的影响。对于厄多斯 - 雷尼网络和小世界网络，在某些条件下可以确定一个使最终疫情规模最小化的最优阈值选择，而对于无标度网络则并非如此。
