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arXiv 2209.12973physics.soc-phcs.MA
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流行病对区域社会经济影响的探讨:模拟方法

Exploration of the effects of epidemics on the regional socio-economics: a modelling approach

Jan E. Snellman, Rafael A. Barrio, Kimmo K. Kaski, Maarit J. Korpi--Lagg

摘要:

大流行病除了影响人口的健康外,还可能对他们的社会和经济行为产生巨大影响。另一方面,这些因素有可能反馈并影响疾病的传播。重要的是要系统地研究这些相互关系,以确定哪些具有显著影响,以及这些影响是不利的还是有益的。我们最近开发的流行病模型与代理为基础的地理元素在这项研究中使用这样一个目的。我们进行了广泛的参数空间探索的社会经济部分的模型,包括因素,如对疾病传播,健康,经济状况,和政府机构的监管机构的代理人的态度(称为值)。我们使用基本的分类工具,即自组织映射和主成分分析,从模拟数据中搜索突出的模式。我们试图隔离影响疾病传播速度和模式的人口和政府代理人的最重要的值参数,并监控不同数量的模型输出,如感染率,流行病的传播速度,经济活动,政府法规和人口的遵守情况。其中,描述传染病传播的那些导致数据的最独特的聚类,并且它们被选择作为剩余分析的基础。我们发现的集群与三种不同类型的疾病传播:波浪状,混乱和过渡的传播模式。最重要的价值参数,有助于这些阶段之间的阶段变化被认为是遵守政府法规的人口代理。

英文摘要:

Pandemics, in addition to affecting the health of populations, can have huge impacts on their social and economic behavior. These factors, on the other hand, have the potential to feed back to and influence the disease spreading. It is important to systematically study these interrelations, to determine which ones have significant effects, and whether the effects are adverse or beneficial. Our recently developed epidemic model with agent-based and geographical elements is used in this study for such a purpose. We perform an extensive parameter space exploration of the socio-economic part of the model, including factors like the attitudes (called values) of the agents towards the disease spreading, health, economic situation, and regulations by government agents. We search for prominent patterns from the resulting simulated data using basic classification tools, namely self-organizing maps and principal component analysis. We seek to isolate the most important value parameters of the population and government agents influencing the disease spreading speed and patterns, and monitor different quantities of the model output, such as infection rates, the propagation speed of the epidemic, economic activity, government regulations, and the compliance of population. Out of these, the ones describing the epidemic spreading were resulting in the most distinctive clustering of the data, and they were selected as the basis of the remaining analysis. We relate the found clusters to three distinct types of disease spreading: wave-like, chaotic, and transitional spreading patterns. The most important value parameter contributing to phase changes between these phases was found to be the compliance of the population agents towards the government regulations.

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