Ghafouri-Fard Soudeh, Mohammad-Rahimi Hossein, Motie Parisa, Minabi Mohammad A S, Taheri Mohammad, Nateghinia Saeedeh
Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Dental Research Center, Research Institute of Dental Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Heliyon. 2021 Oct;7(10):e08143. doi: 10.1016/j.heliyon.2021.e08143. Epub 2021 Oct 11.
COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R coefficient of determination (R), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.
新冠疫情已引发全球大流行，影响到世界各地。对新冠病毒传播速率的预测及其病程建模，对卫生系统和政策制定者都具有至关重要的影响。事实上，政策制定依赖于预测模型所形成的判断，以提出新策略并衡量所实施政策的成效。鉴于这种疾病具有非线性和复杂性，且使用传统流行病模型难以估计病毒传播特征，因此已应用人工智能方法来预测其传播情况。基于机器学习和深度学习方法在估计新冠病毒传播趋势方面的重要性，在本研究中，我们回顾了使用这些策略来预测新冠新增病例数的研究。自适应神经模糊推理系统、长短期记忆网络、循环神经网络和多层感知器是这方面最常用的策略。我们比较了几种机器学习方法在预测新冠病毒传播方面的性能。选择均方根误差（RMSE）、平均绝对误差（MAE）、决定系数R以及平均绝对百分比误差（MAPE）参数作为性能指标，以比较模型的准确性。人工神经网络（ANN）和双向长短期记忆网络（LSTM）的R值分别在0.64至1的范围内。自适应神经模糊推理系统（ANFIS）、自回归积分移动平均模型（ARIMA）和多层感知器（MLP）的R值也接近1。ARIMA和LSTM的MAPE值最高。总体而言，这些模型能够识别影响新冠病毒在不同地区或人群中传播差异的学习参数，结合多种干预方法，并通过整合具有与新冠病毒类似趋势的疾病数据来实施假设情景。因此，应用这些方法将有助于进行精准的政策制定，以设计最合适的干预措施并避免无效的限制。
