Talkhi Nasrin, Akhavan Fatemi Narges, Ataei Zahra, Jabbari Nooghabi Mehdi
Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran.
Biomed Signal Process Control. 2021 Apr;66:102494. doi: 10.1016/j.bspc.2021.102494. Epub 2021 Feb 10.
The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence.
In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020.
Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2484 new confirmed and 114 new death cases of COVID-19.
According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.
新冠疫情状况在伊朗和其他国家仍很普遍，监测系统每天都在逐渐发现新病例。因此，这在全球引起了关注，尽管对未来患者数量和死亡病例的预测并不完全准确，但有助于政府和卫生政策制定者做出必要决策并实施限制措施以降低疫情流行程度。
在本研究中，我们旨在找到预测伊朗确诊病例和死亡病例数量的最佳模型。为此，我们应用了九种模型，包括NNETAR、ARIMA、混合模型、霍尔特 - 温特模型、BSTS、TBATS、先知模型、多层感知器（MLP）和极限学习机（ELM）网络模型。预测模型的质量通过均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）这三个性能指标来评估。通过性能指标的最低值选择最佳模型。然后，预测未来30天的确诊病例和死亡病例数量。本研究中使用的数据是2020年2月20日至8月15日的确诊病例和死亡病例的绝对数量。
我们的研究结果表明，基于伊朗的现有数据，对于确诊病例数据，具有最低性能指标的合适模型是MLP网络模型，而霍尔特 - 温特模型是预测未来死亡病例的合适模型。这些模型预测在2020年9月1日，我们将有2484例新的新冠确诊病例和114例新的死亡病例。
根据本研究结果和现有数据，我们得出结论，与其他方法相比，MLP和霍尔特 - 温特模型在预测中误差最低。一些模型在测试阶段拟合不佳，这是因为许多其他因素在本研究中要么不可用，要么被忽略了，而这些因素会影响预测结果的准确性。基于数据趋势和预测结果，确诊病例数和死亡病例数几乎分别保持稳定和下降趋势。然而由于疾病的发展以及忽视卫生部的建议和规程，在伊朗这种疾病有可能更严重地再次出现，这需要更多的预防护理。
