COVID-19 Outbreak Prediction with Machine Learning
COVID-19 Outbreak Prediction with Machine Learning
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DOI：
10.3390/a13100249
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发表时间：
2020-10-01
期刊：
影响因子：
2.3
通讯作者：
Atkinson, Peter M.
中科院分区：
文献类型：
--
作者：
Ardabili, Sina F.;Mosavi, Amir;Atkinson, Peter M.
世界各地的官员正在使用多种 COVID-19 疫情预测模型来做出明智的决策并执行相关的控制措施。在COVID-19全球大流行预测的标准模型中，简单的流行病学和统计模型受到了当局更多的关注，这些模型在媒体上很受欢迎。由于高度不确定性和缺乏基本数据，标准模型的长期预测准确性较低。尽管文献中包括了解决这个问题的几种尝试，但现有模型的基本泛化能力和鲁棒性能力需要提高。本文对机器学习和软计算模型进行了比较分析，以预测 COVID-19 爆发，作为易感者-感染者-恢复 (SIR) 和易感者-暴露-感染者移除 (SEIR) 模型的替代方案。在研究的各种机器学习模型中，有两个模型显示出有希望的结果（即多层感知器，MLP；和基于自适应网络的模糊推理系统，ANFIS）。根据此处报告的结果，并且由于 COVID-19 爆发的高度复杂性以及各国行为的差异，本研究表明机器学习是对爆发进行建模的有效工具。本文提供了初步基准测试，以展示机器学习在未来研究中的潜力。本文进一步表明，通过整合机器学习和 SEIR 模型，可以实现爆发预测的真正新颖性。
Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible-infected-recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.