The effect of competition between health opinions on epidemic dynamics

多稳态 基本再生数 竞赛(生物学) 人口 动力学(音乐) 疾病 流行病模型 社会心理学 心理学 经济 医学 生物 生态学 环境卫生 物理 非线性系统 教育学 量子力学 病理
作者
Alexandra Teslya,Hendrik Nunner,Vincent Buskens,Mirjam Kretzschmar
出处
期刊:PNAS nexus [Oxford University Press]
卷期号:1 (5): pgac260-pgac260 被引量:21
标识
DOI:10.1093/pnasnexus/pgac260
摘要

Abstract Past major epidemic events showed that when an infectious disease is perceived to cause severe health outcomes, individuals modify health behavior affecting epidemic dynamics. To investigate the effect of this feedback relationship on epidemic dynamics, we developed a compartmental model that couples a disease spread framework with competition of two mutually exclusive health opinions (health-positive and health-neutral) associated with different health behaviors. The model is based on the assumption that individuals switch health opinions as a result of exposure to opinions of others through interpersonal communications. To model opinion switch rates, we considered a family of functions and identified the ones that allow health opinions to coexist. Finally, the model includes assortative mixing by opinions. In the disease-free population, either the opinions cannot coexist and one of them is always dominating (mono-opinion equilibrium) or there is at least one stable coexistence of opinions equilibrium. In the latter case, there is multistability between the coexistence equilibrium and the two mono-opinion equilibria. When two opinions coexist, it depends on their distribution whether the infection can invade. If presence of the infection leads to increased switching to a health-positive opinion, the epidemic burden becomes smaller than indicated by the basic reproduction number. Additionally, a feedback between epidemic dynamics and health opinion dynamics may result in (sustained) oscillatory dynamics and a switch to a different stable opinion distribution. Our model captures feedback between spread of awareness through social interactions and infection dynamics and can serve as a basis for more elaborate individual-based models.
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