Centre of Computer, University of Anbar, Ramadi, Iraq; Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK.
College of Medicine, University of Anbar, Ramadi, Iraq.
J Biomed Inform. 2021 Jun;118:103766. doi: 10.1016/j.jbi.2021.103766. Epub 2021 Apr 22.
Iraq is among the countries affected by the COVID-19 pandemic. As of 2 August 2020, 129,151 COVID-19 cases were confirmed, including 91,949 recovered cases and 4,867 deaths. After the announcement of lockdown in early April 2020, situation in Iraq was getting steady until late May 2020, when daily COVID-19 infections have raised suddenly due to gradual easing of lockdown restrictions. In this context, it is important to develop a forecasting model to evaluate the COVID-19 outbreak in Iraq and so to guide future health policy.
COVID-19 lag data were made available by the University of Anbar through their online analytical platform (https://www.uoanbar.edu.iq/covid/), engaged with the day-to-day figures form the Iraqi health authorities. 154 days of patient data were provided covering the period from 2 March 2020 to 2 August 2020. An ensemble of feed-forward neural networks has been adopted to forecast COVID-19 outbreak in Iraq. Also, this study highlights some key questions about this pandemic using data analytics.
Forecasting were achieved with accuracy of 87.6% for daily infections, 82.4% for daily recovered cases, and 84.3% for daily deaths. It is anticipated that COVID-19 infections in Iraq will reach about 308,996 cases by the end of September 2020, including 228,551 to recover and 9,477 deaths.
The applications of artificial neural networks supported by advanced data analytics represent a promising solution through which to realise intelligent solutions, enabling the space of analytical operations to drive a national health policy to contain COVID-19 pandemic.
伊拉克是受 COVID-19 大流行影响的国家之一。截至 2020 年 8 月 2 日，已确诊 COVID-19 病例 129151 例，包括 91949 例康复病例和 4867 例死亡病例。2020 年 4 月初宣布封锁后，伊拉克的情况一直保持稳定，直到 2020 年 5 月底，由于封锁限制逐渐放宽，COVID-19 感染人数突然上升。在这种情况下，开发一种预测模型来评估伊拉克的 COVID-19 疫情并指导未来的卫生政策非常重要。
通过安巴尔大学的在线分析平台（https://www.uoanbar.edu.iq/covid/）提供 COVID-19 滞后数据，并与伊拉克卫生当局的日常数据相结合。提供了 154 天的患者数据，涵盖了 2020 年 3 月 2 日至 2020 年 8 月 2 日期间。采用前馈神经网络的集合来预测伊拉克的 COVID-19 疫情。此外，本研究还使用数据分析突出了这场大流行的一些关键问题。
每日感染的预测准确率为 87.6%，每日康复病例的预测准确率为 82.4%，每日死亡病例的预测准确率为 84.3%。预计到 2020 年 9 月底，伊拉克的 COVID-19 感染人数将达到约 308996 例，其中 228551 例将康复，9477 例死亡。
先进数据分析支持的人工神经网络应用代表了一种有前途的解决方案，可以实现智能解决方案，使分析操作空间能够推动国家卫生政策以遏制 COVID-19 大流行。
