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在孟加拉国使用时间序列分析预测新冠疫情第三波的传播情况。

Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh.

作者信息

Kibria Hafsa Binte, Jyoti Oishi, Matin Abdul

机构信息

Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

出版信息

Inform Med Unlocked. 2022;28:100815. doi: 10.1016/j.imu.2021.100815. Epub 2021 Dec 22.

Abstract

During the third wave of the coronavirus epidemic in Bangladesh, the death and infection rate due to this devastating virus has increased dramatically. The rapid spread of the virus is one of the reasons for this terrible condition. So, identifying the subsequent cases of coronavirus can be a great tool to reduce the mortality and infection rate. In this article, we used the autoregressive integrated moving average-ARIMA(8,1,7) model to estimate the expected daily number of COVID-19 cases in Bangladesh based on the data from April 20, 2021, to July 4, 2021. The ARIMA model showed the best results among the five executed models over Autoregressive Model (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Rolling Forest Origin. The findings of this article were used to anticipate a rise in daily cases for the next month in Bangladesh, which can help governments plan policies to prevent the spread of the virus. The forecasting outcome indicated that this new trend(named delta variant) in Bangladesh would continue increasing and might reach 18327 daily new cases within four weeks if strict rules and regulations are not applied to control the spread of COVID-19.

摘要

在孟加拉国新冠疫情的第三波期间,这种毁灭性病毒导致的死亡率和感染率急剧上升。病毒的快速传播是造成这种可怕状况的原因之一。因此,识别后续的新冠病例可能是降低死亡率和感染率的一个重要工具。在本文中,我们使用自回归积分移动平均模型(ARIMA(8,1,7)),根据2021年4月20日至2021年7月4日的数据,来估计孟加拉国每日新冠肺炎病例的预期数量。在自回归模型(AR)、移动平均模型(MA)、自回归移动平均模型(ARMA)和滚动森林起源模型这五个执行模型中,ARIMA模型显示出了最佳结果。本文的研究结果被用于预测孟加拉国下个月每日病例数的上升情况,这有助于政府制定预防病毒传播的政策。预测结果表明,如果不实施严格的规章制度来控制新冠疫情的传播,孟加拉国这种新趋势(即德尔塔变种)将持续上升,四周内每日新增病例可能会达到18327例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/5867e7d45599/gr1_lrg.jpg

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