Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
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DOI：
10.1016/j.chaos.2020.110027
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发表时间：
2020-10-01
影响因子：
7.8
通讯作者：
Coelho, Leandro dos Santos
中科院分区：
文献类型：
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作者：
da Silva, Ramon Gomes;Dal Molin Ribeiro, Matheus Henrique;Coelho, Leandro dos Santos
这种新型的冠状病毒病(新冠肺炎)曾经是一个公共卫生问题，根据世界卫生组织的数据，截至2020年6月24日，全球已有超过910万人感染，超过47万人死亡。在目前的情况下，巴西和美利坚合众国每天的新增病例和死亡人数都很高。因此，重要的是预测一周内的新病例数量，一旦这可以帮助公共卫生系统制定战略规划来应对新冠肺炎。人工智能预测模型的应用具有处理新冠肺炎等时间序列动态行为的潜力。本文使用贝叶斯回归神经网络、三次回归、k近邻、分位数随机森林和支持向量回归等方法，并结合最新的前处理变分模式分解(VMD)方法，将时间序列分解为多个固有模式函数。所有人工智能技术都在时间序列预测任务中进行评估，巴西和美国五个州的累计新冠肺炎病例提前一天、三天和六天，截至2020年4月28日的病例数量很高。所有预报模型都采用了以往累积的新冠肺炎个例和日气温、降水量等外生变量作为输入。根据性能标准对模型的有效性进行了评估。总体而言，混合VMD预测模型的准确性优于单一预测模型，特别是当预测时间提前6天时，混合VMD-单一预测模型在70%的情况下获得了更好的准确性。对于外生变量，预测变量的重要性排序从高到低依次为：过去案例、气温和降水量。因此，由于评估的模型对提前6天预测累积新冠肺炎病例的效率，所采用的模型可被推荐为一种有前景的预测模型，并可用于帮助制定缓解新冠肺炎疫情影响的公共政策。(C)2020爱思唯尔有限公司。保留所有权利。y
The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has the potential of deal with dynamical behavior of time-series like of COVID-19. In this paper, Bayesian regression neural network, cubist regression, k-nearest neighbors, quantile random forest, and support vector regression, are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions. All AI techniques are evaluated in the task of time-series forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American states, with a high number of cases up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation were employed as inputs for all forecasting models. The models' effectiveness are evaluated based on the performance criteria. In general, the hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, the hybrid VMD-single models achieved better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is, from the upper to the lower, past cases, temperature, and precipitation. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak. (C) 2020 Elsevier Ltd. All rights reserved.y