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A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks

机译:表征接触网络接种行为动力学的仿真分析

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Background Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network. Methods We simulate transmission of a vaccine-prevetable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection. Results We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective. Conclusion For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled.
机译:背景技术人类行为影响传染病的传播,许多“流行行为”模型已经分析了这种相互作用。这些先前的分析假设人口是均匀混合的,没有空间或社会结构。但是,众所周知,空间和社会异质性会极大地影响传播动态,并且与某些疾病特别相关。先前的工作表明,社会接触结构可以改变个人接种疫苗的动机,从而在相应的均质混合模型预测由于搭便车效应而无法根除时,可以根据自愿接种政策根除疾病。在这里,我们扩展了这项工作,并描述了网络上可能的行为流行度动态范围。方法我们模拟了通过随机,静态接触网络传播的可预防疫苗的感染。个人根据感知的疫苗接种和感染风险选择是否在任何给定的日子进行疫苗接种。结果我们发现在这种类型的网络上,行为流行的动态有三种可能的结果:接种的最终人数少和最终的流行病规模(由于通过自愿性环戊疫苗的快速控制);最终疫苗接种人数大,最终流行病规模大(由于不完全的自愿环疫苗接种),很少接种或没有接种疫苗,最终流行病规模大(对应于很少或没有自愿环疫苗接种)。我们还表明,社会接触结构可以在广泛的假设范围内根除,除非疫苗风险足够高,疾病风险足够低或个人接种疫苗的时间太晚,疫苗才有效。结论对于只能通过社会接触网络传播感染的人群,与个人水平的感知风险或疫苗接种行为相关的参数值相对较小的差异可能会转化为人群水平结果的较大差异,例如最终接种的人数和最终接种的人数。自愿接种政策下理性,自利行为的定性结果可能会因社会接触结构,感知的疫苗和疾病风险之间的相互作用以及个体疫苗接种决策的建模方式而有很大差异。

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