To combat the recent coronavirus disease 2019 (COVID-19), academician and clinician are in search of new approaches to predict the COVID-19 outbreak dynamic trends that may slow down or stop the pandemic. Epidemiological models like Susceptible-Infected-Recovered (SIR) and its variants are helpful to understand the dynamics trend of pandemic that may be used in decision making to optimize possible controls from the infectious disease. But these epidemiological models based on mathematical assumptions may not predict the real pandemic situation. Recently the new machine learning approaches are being used to understand the dynamic trend of COVID-19 spread. In this paper, we designed the recurrent and convolutional neural network models: vanilla LSTM, stacked LSTM, ED-LSTM, Bi-LSTM, CNN, and hybrid CNN+LSTM model to capture the complex trend of COVID-19 outbreak and perform the forecasting of COVID-19 daily confirmed cases of 7, 14, 21 days for India and its four most affected states (Maharashtra, Kerala, Karnataka, and Tamil Nadu). The root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metric are computed on the testing data to demonstrate the relative performance of these models. The results show that the stacked LSTM and hybrid CNN+LSTM models perform best relative to other models.
翻译:为了应对最近2019年的冠状病毒疾病(COVID-19),学者和临床医生正在寻找新的方法来预测COVID-19爆发的动态趋势,这些趋势可能会减缓或阻止这一流行病的蔓延。传染病模型,如可感知感染性复苏(SIR)及其变体,有助于了解大流行病的动态趋势,这些模式可用于决策优化传染病控制。但这些基于数学假设的流行病学模型可能无法预测真正的大流行病状况。最近,正在使用新的机器学习方法来了解COVID-19扩散的动态趋势。在本文件中,我们设计了经常性和革命性神经网络模型:Vanilla LSTM、堆叠式LSTM、ED-LSTM、Bi-LSTM、CNNNC和混合CNNN+LSTM模型,以捕捉COVID-19爆发的复杂趋势,并对COVID-19日经证实的7、14天案例进行预报。印度及其四个受影响最严重的州(Maharashtraraftra、Kerala、Karnataka和Tamal Nadu)的动态神经网络模型。这些网络网络网络网络网络网络网络的周期模型的运行和模型的绝对性模型测试结果。这些模型的绝对值测试显示模型的绝对性结果。