Jones Andrew, Strigul Nikolay
Stevens Institute of Technology, Hoboken, New Jersey, USA.
Washington State University, Vancouver, WA, USA.
Chaos Solitons Fractals. 2021 Jan;142:110376. doi: 10.1016/j.chaos.2020.110376. Epub 2020 Oct 20.
The COVID-19 epidemic challenges humanity in 2020. It has already taken an enormous number of human lives and had a substantial negative economic impact. Traditional compartmental epidemiological models demonstrated limited ability to predict the scale and dynamics of COVID-19 epidemic in different countries. In order to gain a deeper understanding of its behavior, we turn to chaotic dynamics, which proved fruitful in analyzing previous diseases such as measles. We hypothesize that the unpredictability of the pandemic could be a fundamental property if the disease spread is a chaotic dynamical system. Our mathematical examination of COVID-19 epidemic data in different countries reveals similarity of this dynamic to the chaotic behavior of many dynamics systems, such as logistic maps. We conclude that the data does suggest that the COVID-19 epidemic demonstrates chaotic behavior, which should be taken into account by public policy makers. Furthermore, the scale and behavior of the epidemic may be essentially unpredictable due to the properties of chaotic systems, rather than due to the limited data available for model parameterization.
2020年，新冠疫情给人类带来了挑战。它已经夺走了大量人类生命，并对经济产生了重大负面影响。传统的分区流行病学模型在预测不同国家新冠疫情的规模和动态方面能力有限。为了更深入地了解其行为，我们转向混沌动力学，事实证明，混沌动力学在分析麻疹等以往疾病时卓有成效。我们假设，如果疾病传播是一个混沌动力系统，那么大流行的不可预测性可能是其基本属性。我们对不同国家新冠疫情数据的数学检验表明，这种动态与许多动力系统（如逻辑斯蒂映射）的混沌行为相似。我们得出结论，数据确实表明新冠疫情呈现出混沌行为，公共政策制定者应予以考虑。此外，由于混沌系统的特性，而非用于模型参数化的数据有限，疫情的规模和行为可能本质上是不可预测的。
