Shams Fazila, Abbas Assad, Khan Wasiq, Khan Umar Shahbaz, Nawaz Raheel
Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
Department of Computing and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, United Kingdom.
Comput Methods Programs Biomed Update. 2022;2:100047. doi: 10.1016/j.cmpbup.2021.100047. Epub 2021 Dec 28.
The SARS-Cov-2 virus (commonly known as COVID-19) has resulted in substantial casualties in many countries. The first case of COVID-19 was reported in China towards the end of 2019. Cases started to appear in several other countries (including Pakistan) by February 2020. To analyze the spreading pattern of the disease, several researchers used the Susceptible-Infectious-Recovered (SIR) model. However, the classical SIR model cannot predict the death rate.
In this article, we present a Death-Infection-Recovery (DIR) model to forecast the virus spread over a window of one (minimum) to fourteen (maximum) days. Our model captures the dynamic behavior of the virus and can assist authorities in making decisions on non-pharmaceutical interventions (NPI), like travel restrictions, lockdowns, etc.
The size of training dataset used was 134 days. The Auto Regressive Integrated Moving Average (ARIMA) model was implemented using XLSTAT (add-in for Microsoft Excel), whereas the SIR and the proposed DIR model was implemented using python programming language. We compared the performance of DIR model with the SIR model and the ARIMA model by computing the Percentage Error and Mean Absolute Percentage Error (MAPE).
Experimental results demonstrate that the maximum% error in predicting the number of deaths, infections, and recoveries for a period of fourteen days using the DIR model is only 2.33%, using ARIMA model is 10.03% and using SIR model is 53.07%.
This percentage of error obtained in forecasting using DIR model is significantly less than the% error of the compared models. Moreover, the MAPE of the DIR model is sufficiently below the two compared models that indicates its effectiveness.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2病毒，通常称为新冠病毒）已在许多国家造成大量人员伤亡。2019年底中国报告了首例新冠病毒病例。到2020年2月，其他几个国家（包括巴基斯坦）也开始出现病例。为了分析该疾病的传播模式，一些研究人员使用了易感-感染-康复（SIR）模型。然而，经典的SIR模型无法预测死亡率。
在本文中，我们提出了一种死亡-感染-康复（DIR）模型，用于预测病毒在一（最小）到十四（最大）天的时间窗口内的传播情况。我们的模型捕捉了病毒的动态行为，并可协助当局就非药物干预措施（如旅行限制、封锁等）做出决策。
使用的训练数据集大小为134天。自回归积分移动平均（ARIMA）模型使用XLSTAT（微软Excel的插件）实现，而SIR模型和提出的DIR模型使用Python编程语言实现。我们通过计算百分比误差和平均绝对百分比误差（MAPE）来比较DIR模型与SIR模型和ARIMA模型的性能。
实验结果表明，使用DIR模型预测十四天内的死亡人数、感染人数和康复人数时，最大百分比误差仅为2.33%，使用ARIMA模型为10.03%，使用SIR模型为53.07%。
使用DIR模型预测时获得的该误差百分比明显低于所比较模型的误差百分比。此外，DIR模型的MAPE远低于两个所比较的模型，这表明了其有效性。
