Fatimah Binish, Aggarwal Priya, Singh Pushpendra, Gupta Anubha
Department of ECE, CMR Institute of Technology, Bengaluru, India.
Vehant Technologies Pvt. Ltd., Noida, India.
Appl Soft Comput. 2022 Jun;122:108806. doi: 10.1016/j.asoc.2022.108806. Epub 2022 Apr 7.
COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.
由新型冠状病毒（SARS-CoV-2）引起的COVID-19大流行使世界经济陷入瘫痪，并对数以百万计的生命和健康造成了无法弥补的损害。为了控制疾病的传播，在正确的时间做出适当的政策决策非常重要。这可以通过一个强大的数学模型来促进，该模型能够更准确地预测COVID-19的流行率和发病率。本研究提出了一种优化的ARIMA模型来预测COVID-19病例。所提出的方法首先使用低通高斯滤波器获得COVID-19数据的趋势，然后使用ARIMA模型预测数据。我们将优化的ARIMA模型针对7天和14天预测与最近用于COVID-19数据的五种预测策略进行了基准测试。这些策略包括自回归积分移动平均（ARIMA）模型、易感-感染-康复（SIR）模型、复合高斯增长模型、复合逻辑斯蒂增长模型和基于字典学习的模型。我们考虑了世界上受影响最严重的十个国家（包括印度、美国、英国、俄罗斯、巴西、德国、法国、意大利、土耳其和哥伦比亚）的COVID-19数据中的每日感染病例、累计死亡病例和累计康复病例。在所考虑的大多数国家的数据上，所提出的算法优于现有模型。
