Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

中文翻译：

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匈牙利的COVID-19大流行预测；混合机器学习方法

世界各地正在使用几种流行病学模型来预测受感染个体的数量和COVID-19爆发的死亡率。推进准确的预测模型对于采取适当的行动至关重要。由于缺乏必要的数据和不确定性，因此流行病学模型对于长期预测的更高准确性提出了挑战。作为基于敏感感染抗性（SIR）的模型的替代方法，本研究提出了一种混合机器学习方法来预测COVID-19，并且我们使用匈牙利的数据来举例说明其潜力。提出了一种基于自适应网络的模糊推理系统（ANFIS）和多层感知器-帝国主义竞争算法（MLP-ICA）的混合机器学习方法，以预测感染者的时间序列和死亡率。这些模型预测，到五月下旬，疫情和总体道德将大大下降。验证进行了9天，结果令人满意，这确认了模型的准确性。只要不发生重大中断，就可以期望模型保持其准确性。本文提供了一个初始基准测试，以证明机器学习在未来研究中的潜力。只要不发生重大中断，就可以期望模型保持其准确性。本文提供了一个初始基准测试，以证明机器学习在未来研究中的潜力。只要不发生重大中断，就可以期望模型保持其准确性。本文提供了一个初始基准测试，以证明机器学习在未来研究中的潜力。