Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?
Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?
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使用深度学习模型预测COVID-19病例：是否可靠且具有实际意义？
DOI：
10.1016/j.rinp.2021.103817
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发表时间：
2021-03
影响因子：
5.3
通讯作者：
Hossain E
中科院分区：
文献类型：
--
作者：
Devaraj J;Madurai Elavarasan R;Pugazhendhi R;Shafiullah GM;Ganesan S;Jeysree AK;Khan IA;Hossain E
COVID-19大流行的持续爆发是对全球经济增长的最后通牒，此后，由于既没有发现治疗药物也没有发现预防疫苗，整个社会都是如此。COVID-19的传播日益加剧，危及人类生命和经济。由于COVID-19病例数量的增加，人工智能（AI）的作用在当前情况下势在必行。人工智能将是一个强大的工具，通过提前预测病例数来对抗这种大流行病的爆发。基于深度学习的时间序列技术被认为可以通过自适应学习提前预测全球COVID-19病例的短期和中期依赖性。首先，使用真实的世界COVID-19数据集进行数据预处理和特征提取。随后，使用自回归积分移动平均（ARIMA）、长短期记忆（LSTM）、堆叠长短期记忆（SLSTM）和Prophet方法对累积确诊、死亡和痊愈的全球病例进行预测。对于COVID-19病例的长期预测，采用多变量LSTM模型。计算所有模型的性能指标，并对预测结果进行比较分析，以确定最可靠的模型。从结果中可以看出，与所研究的性能指标的其他算法相比，Stacked LSTM算法产生了更高的准确性，误差小于2%。分别对印度和钦奈的COVID-19病例进行了国家特定分析和城市特定分析，并进行了详细的预测和分析。此外，对COVID-19数据集进行统计假设分析和相关性分析，包括5月，6月，7月和8月的温度，降雨量，人口，总感染病例，面积和人口密度等特征，以找出最适合的模型。此外，从评估疫情特征、情景规划、模型优化和支持可持续发展目标（SDG）等方面阐述了COVID-19病例预测的实际意义。
The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID-19 cases, multivariate LSTM models is employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% as compared to the other considered algorithms for the studied performance metrics. Country-specific analysis and city-specific analysis of COVID-19 cases for India and Chennai, respectively, are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID-19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs).
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作者：
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