Review of COVID-19 Propagation Prediction Methods

doi:10.3778/j.issn.1002-8331.2209-0451

Abstract

Abstract: At the end of 2019, novel coronavirus pneumonia（COVID-19） broke out. In order to suppress the spread of the virus and formulate countermeasures in advance, predicting the virus transmission trend has become a research hotspot. Various COVID-19 prediction methods have played a key role in epidemic prevention and control and stabilizing the people’s hearts. According to the literature on COVID-19 transmission trend prediction methods in the past three years, prediction methods are divided into mathematical model prediction methods, artificial intelligence prediction methods and swarm intelligence optimization prediction methods are three categories. Firstly, this paper describes the characteristics of novel coronavirus and the impact of the epidemic situation, and briefly describes the characteristics of various prediction methods at present. Secondly, the development history and classification of classical models in the three prediction methods are introduced respectively, and the advantages, disadvantages and performance of various improved models are compared and analyzed in detail. Finally, this paper summarizes the limitations of prediction methods, analyzes the shortcomings of various methods, and looks forward to the future research direction of communication prediction methods.

Key words: corona virus disease 2019（COVID-19）, propagation trend forecast, mathematical model, artificial intelligence, swarm intelligence algorithm

摘要： 2019年底，新型冠状病毒肺炎（COVID-19）疾病爆发，为抑制病毒扩散并提前制定应对措施，预测病毒传播趋势成为了研究热点，各种COVID-19预测方法在疫情防控和稳定民心等方面起到了关键作用。根据近三年来的COVID-19传播趋势预测方法的文献资料，将预测方法分为数学模型预测方法、人工智能预测方法和群智能优化预测方法三类。陈述了新冠病毒的特性以及疫情带来的影响，并简要描述了目前各类预测方法的特点。分别介绍了三种预测方法中经典模型的发展历史以及分类，并从优缺点和性能等方面对各类改进模型进行了详细的对比分析。最后进行归纳总结，从预测方法的局限性入手，分析了各类方法的不足，并对传播预测方法的未来研究方向进行了展望。

关键词: 新型冠状病毒肺炎, 传播趋势预测, 数学模型, 人工智能, 群智能算法

WANG Zheng’an, XU Zhenshun, LIN Lingde. Review of COVID-19 Propagation Prediction Methods[J]. Computer Engineering and Applications, 2023, 59(12): 49-61.

王正安, 徐贞顺, 林令德. 新冠肺炎疫情传播预测方法综述[J]. 计算机工程与应用, 2023, 59(12): 49-61.

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