标题： 印度新冠疫情病例预测的时序深度学习架构 显示英文标题

标题： Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India

摘要： 为了应对最近的冠状病毒疾病2019（COVID-19），院士和临床医生正在寻找新的方法来预测可能减缓或阻止大流行的 COVID-19 爆发动态趋势。 流行病学模型如易感-感染-恢复（SIR）及其变体有助于理解可能用于决策制定以优化传染病控制的流行病动态趋势。 但这些基于数学假设的流行病学模型可能无法预测真实的流行病情况。 最近，新的机器学习方法被用来理解 COVID-19 传播的动态趋势。 在本文中，我们设计了循环神经网络和卷积神经网络模型：普通 LSTM、堆叠 LSTM、ED-LSTM、Bi-LSTM、CNN 和混合 CNN+LSTM 模型，以捕捉 COVID-19 爆发的复杂趋势，并对印度及其四个最受影响的州（马哈拉施特拉邦、喀拉拉邦、卡纳塔克邦和泰米尔纳德邦）的 COVID-19 每日确诊人数进行 7、14、21 天的预测。 在测试数据上计算均方根误差（RMSE）和平均绝对百分比误差（MAPE）评估指标，以展示这些模型的相对性能。 结果表明，堆叠 LSTM 和混合 CNN+LSTM 模型相对于其他模型表现最好。 显示英文摘要

摘要： 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.