Dynamic modeling of vaccinating behavior as a function of individual beliefs.
Dynamic modeling of vaccinating behavior as a function of individual beliefs.
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DOI：
10.1371/journal.pcbi.1000425
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发表时间：
2009-07
影响因子：
4.3
通讯作者：
Codeço CT
中科院分区：
文献类型：
--
作者：
Coelho FC;Codeço CT
Individual perception of vaccine safety is an important factor in determining a person's adherence to a vaccination program and its consequences for disease control.这种对特定疫苗安全性的看法或信念不是静态参数，而是受环境影响的变量。使问题变得复杂的是，对风险（或安全）的感知与实际风险并不相符。在本文中，我们提出了一种方法，将这种信念的动态纳入现实的流行病学模型，从而更完整地描述疫苗接种运动瓦解的潜在机制。所提出的方法基于贝叶斯推理，可以扩展到对与决策模型相关的更复杂的信念系统进行建模。我们发现该方法能够产生与真实疫苗和疾病恐慌情况下观察到的行为相似的行为。提出的框架包括一组有用的工具，用于充分定量地表示常见但复杂的公共卫生问题。这些工具包括将信念表示为贝叶斯概率、使用对数池来组合表示观点的概率分布，以及使用自然共轭先验来有效计算贝叶斯后验。这种方法可以在现实的流行病学模型中综合处理疫苗接种行为的不确定性。人口模型中经常做出的假设是，个体以标准方式做出决策，这种方式往往是根据建模者对个体最可能的行为方式的看法来固定和设置的。在本文中，我们承认将行为变化（以信念/意见的形式）建模为模型中的动态变量的重要性。我们还提出了一种对动态信念更新进行数学建模的方法，该方法基于作为概率分布的信念概念及其作为贝叶斯定理的直接应用的时间演化的概念。我们还建议使用对数池作为结合不同意见的最佳方式，在做出决策时必须考虑这些意见。为了论证这个问题的相关性，我们提出了一个具有动态信念更新的疫苗接种行为模型，该模型以最近文献中记录的疫苗和疾病恐慌的真实场景为模型。
Individual perception of vaccine safety is an important factor in determining a person's adherence to a vaccination program and its consequences for disease control. This perception, or belief, about the safety of a given vaccine is not a static parameter but a variable subject to environmental influence. To complicate matters, perception of risk (or safety) does not correspond to actual risk. In this paper we propose a way to include the dynamics of such beliefs into a realistic epidemiological model, yielding a more complete depiction of the mechanisms underlying the unraveling of vaccination campaigns. The methodology proposed is based on Bayesian inference and can be extended to model more complex belief systems associated with decision models. We found the method is able to produce behaviors which approximate what has been observed in real vaccine and disease scare situations. The framework presented comprises a set of useful tools for an adequate quantitative representation of a common yet complex public-health issue. These tools include representation of beliefs as Bayesian probabilities, usage of logarithmic pooling to combine probability distributions representing opinions, and usage of natural conjugate priors to efficiently compute the Bayesian posterior. This approach allowed a comprehensive treatment of the uncertainty regarding vaccination behavior in a realistic epidemiological model. A frequently made assumption in population models is that individuals make decisions in a standard way, which tends to be fixed and set according to the modeler's view on what is the most likely way individuals should behave. In this paper we acknowledge the importance of modeling behavioral changes (in the form of beliefs/opinions) as a dynamic variable in the model. We also propose a way of mathematically modeling dynamic belief updates which is based on the very well established concept of a belief as a probability distribution and its temporal evolution as a direct application of the Bayes theorem. We also propose the use of logarithmic pooling as an optimal way of combining different opinions which must be considered when making a decision. To argue for the relevance of this issue, we present a model of vaccinating behaviour with dynamic belief updates, modeled after real scenarios of vaccine and disease scare recorded in the recent literature.
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影响因子：
0.7
作者：
Hennock, EP
通讯作者：
Hennock, EP
轶事证据与科学研究证据的定性综合是否有助于理解公共卫生问题：对 MMR 低吸收率的回顾
影响因子：
4.4
作者：
Nagaraj, A
通讯作者：
Nagaraj, A
影响因子：
3.9
作者：
Codeco, Claudia T.;Luz, Paula M.;Struchiner, Claudio
通讯作者：
Struchiner, Claudio
DOI：
10.1590/s0037-86822003000200012
发表时间：
2003-04-01
影响因子：
2
作者：
Vasconcelos, Pedro Fernando da Costa
通讯作者：
Vasconcelos, Pedro Fernando da Costa
影响因子：
2.4
作者：
Willink, R
通讯作者：
Willink, R