信息宣传背景下民众疫苗预防立场形成的社会心理过程建模

Q3 Mathematics
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引用次数: 0

摘要

摘要 本文研究了在流感发病率每年反复季节性增长的背景下,居民对流感病毒疫苗预防立场的形成模型的开发和分析。在下一次疫苗接种活动前夕,根据对上一年决定结果的心理反应做出决定。一例疾病被视为一次不成功决策的结果,其变化与对失败的心理反应的性质有关:情绪化或克制。考虑到疫苗接种和反疫苗接种宣传的影响,对这一方法进行了补充。对同意和不同意接种疫苗的固定比例进行了分析,从而可以确定确保达到理想比例的参数值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Sociopsychological Processes of the Formation of the Population’s Position on Vaccine Prevention in the Context of Information Propaganda

Abstract

This article studies the development and analysis of a model for the formation of the population’s position regarding the influenza virus vaccine prophylaxis within the context of annual recurring seasonal increases in the incidence of influenza. The adoption of a decision on the eve of the next vaccination campaign is taken in accordance with the psychological reaction to the result of the previous year’s decision. A case of the disease is considered as the result of an unsuccessful decision, and its change is related to the nature of the psychological reaction to failure: emotional or restrained. This approach is complemented by taking into account the impact of vaccination and antivaccination propaganda. An analytical expression is obtained for the ratio of stationary proportions of those agreeing and disagreeing with vaccination, which makes it possible to determine those parameter values that ensure the desired ratio.

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来源期刊
Mathematical Models and Computer Simulations
Mathematical Models and Computer Simulations Mathematics-Computational Mathematics
CiteScore
1.20
自引率
0.00%
发文量
99
期刊介绍: Mathematical Models and Computer Simulations  is a journal that publishes high-quality and original articles at the forefront of development of mathematical models, numerical methods, computer-assisted studies in science and engineering with the potential for impact across the sciences, and construction of massively parallel codes for supercomputers. The problem-oriented papers are devoted to various problems including industrial mathematics, numerical simulation in multiscale and multiphysics, materials science, chemistry, economics, social, and life sciences.
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