Perisic Ana, Bauch Chris T
Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, N1G 2W1, Canada.
BMC Infect Dis. 2009 May 28;9:77. doi: 10.1186/1471-2334-9-77.
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.
We simulate transmission of a vaccine-preventable 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.
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.
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.
人类行为会影响传染病传播,众多“流行率 - 行为”模型已对这种相互作用进行了分析。此前的这些分析假定人群均匀混合,不存在空间或社会结构。然而,已知空间和社会异质性会显著影响传播动态,且对某些疾病尤为重要。此前的研究表明,社会接触结构会改变个体接种疫苗的动机,从而在相应的均匀混合模型因搭便车效应预测无法根除疾病的情况下,通过自愿接种政策实现疾病的根除。在此,我们扩展这项工作,并描述网络上可能的行为 - 流行率动态范围。
我们通过随机、静态接触网络模拟一种可通过疫苗预防的感染的传播。个体根据对接种疫苗和感染风险的认知,在任何给定日子选择是否接种疫苗。
我们发现这种类型网络上行为 - 流行率动态有三种可能结果:最终接种人数少且最终疫情规模小(由于通过自愿环状接种迅速控制);最终接种人数多且最终疫情规模大(由于不完全的自愿环状接种),以及接种人数很少或不接种且最终疫情规模大(对应很少或没有自愿环状接种)。我们还表明,社会接触结构在广泛的假设下能够实现根除,除非疫苗风险足够高、疾病风险足够低,或者个体接种疫苗过晚以至于疫苗无法生效。
对于感染仅能通过社会接触网络传播的人群,个体层面与感知风险或接种行为相关的参数值相对较小的差异,可能转化为人群层面结果的巨大差异,如最终规模和最终接种人数。在自愿接种政策下,理性、自利行为的定性结果可能会因社会接触结构、感知到的疫苗和疾病风险以及个体接种决策建模方式之间的相互作用而有很大不同。