Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
Microbiology and Immunology Department, Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt.
Int J Clin Pract. 2021 Jun;75(6):e14116. doi: 10.1111/ijcp.14116. Epub 2021 Mar 8.
SARS-CoV-2 is affecting different countries all over the world, with significant variation in infection-rate and death-ratio. We have previously shown a presence of a possible relationship between different variables including the Bacillus Calmette-Guérin (BCG) vaccine, average age, gender, and malaria treatment, and the rate of spread, severity and mortality of COVID-19 disease. This paper focuses on developing machine learning models for this relationship.
We have used real-datasets collected from the Johns Hopkins University Center for Systems Science and Engineering and the European Centre for Disease Prevention and Control to develop a model from China data as the baseline country. From this model, we predicted and forecasted different countries' daily confirmed-cases and daily death-cases and examined if there was any possible effect of the variables mentioned above.
The model was trained based on China data as a baseline model for daily confirmed-cases and daily death-cases. This machine learning application succeeded in modelling and forecasting daily confirmed-cases and daily death-cases. The modelling and forecasting of viral spread resulted in four different regions; these regions were dependent on the malarial treatments, BCG vaccination, weather conditions, and average age. However, the lack of social distancing resulted in variation in the effect of these factors, for example, double-humped spread and mortality cases curves and sudden increases in the spread and mortality cases in different countries. The process of machine learning for time-series prediction and forecasting, especially in the pandemic COVID-19 domain, proved usefulness in modelling and forecasting the end status of the virus spreading based on specific regional and health support variables.
From the experimental results, we confirm that COVID-19 has a very low spread in the African countries with all the four variables (average young age, hot weather, BCG vaccine and malaria treatment); a very high spread in European countries and the USA with no variable (old people, cold weather, no BCG vaccine and no malaria). The effect of the variables could be on the spread or the severity to the extent that the infected subject might not have symptoms or the case is mild and can be missed as a confirmed-case. Social distancing decreases the effect of these factors.
SARS-CoV-2 正在影响世界各国，其感染率和死亡率差异很大。我们之前已经表明，包括卡介苗（BCG）疫苗、平均年龄、性别和疟疾治疗在内的不同变量与 COVID-19 疾病的传播速度、严重程度和死亡率之间可能存在一定关系。本文重点研究了这种关系的机器学习模型。
我们使用了约翰霍普金斯大学系统科学与工程中心和欧洲疾病预防与控制中心收集的真实数据集，从中国数据开发了一个模型作为基线国家。从该模型中，我们预测和预测了不同国家的每日确诊病例和每日死亡病例，并检查了上述变量是否存在任何可能的影响。
该模型是基于中国数据作为每日确诊病例和每日死亡病例的基线模型进行训练的。该机器学习应用程序成功地对每日确诊病例和每日死亡病例进行了建模和预测。病毒传播的建模和预测产生了四个不同的区域；这些区域取决于疟疾治疗、BCG 疫苗接种、天气条件和平均年龄。然而，缺乏社交距离导致这些因素的影响发生变化，例如双峰传播和死亡率曲线以及不同国家的传播和死亡率突然增加。时间序列预测和预测的机器学习过程，特别是在 COVID-19 大流行领域，证明了基于特定区域和健康支持变量对病毒传播结束状态进行建模和预测的有用性。
从实验结果来看，我们确认 COVID-19 在非洲国家的传播非常缓慢，因为有四个变量（平均年龄小、天气炎热、BCG 疫苗和疟疾治疗）；在欧洲国家和美国的传播非常迅速，因为没有变量（老年人、寒冷天气、没有 BCG 疫苗和没有疟疾）。这些变量的影响可能是在传播速度或严重程度上，感染的患者可能没有症状，或者病情较轻，可能被漏诊为确诊病例。社交距离减少了这些因素的影响。
