COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

世界卫生组织于 2020 年 3 月宣布 COVID-19 为全球大流行病，截至 2020 年 5 月上旬，全球已感染超过 400 万人，死亡人数超过 30 万人。世界各地的许多研究人员采用了各种预测技术，例如易感者-感染者-恢复模型、易感-暴露-感染-恢复模型和自回归综合移动平均模型（ARIMA）来预测这种流行病的传播。研究人员并未大量使用 ARIMA 技术来预测 COVID-19，因为该技术声称不适合在复杂和动态的环境中使用。本研究的目的是测试 ARIMA 最佳拟合模型预测与整个预测时间过后报告的实际值的准确性。我们以科威特为案例研究，在相对较长的时间内调查并验证了 ARIMA 模型的准确性。我们首先优化模型的参数，通过检查自相关函数和部分自相关函数图以及不同的精度度量来找到最佳拟合。然后，我们使用最佳拟合模型来预测科威特逐步预防计划的不同阶段的 COVID-19 确诊病例和康复病例。结果表明，尽管疾病具有动态性质并且科威特政府不断进行修订，但观察到的大部分时间段的实际值都在我们选择的 ARIMA 模型预测的 95% 置信区间范围内。预测点与实际记录数据的 Pearson 相关系数为 0.996。这表明这两个集合高度相关。我们的 ARIMA 模型提供的预测准确性是适当且令人满意的。