Department of Medical Genetics, Near East University, Mersin 10, Lefkosa, Turkey.
Department of Civil Engineering, Faculty of Engineering, Baze University, Abuja, Nigeria.
Environ Sci Pollut Res Int. 2023 Jan;30(2):3621-3643. doi: 10.1007/s11356-022-22373-6. Epub 2022 Aug 11.
Coronavirus disease 2019 (COVID-19) has produced a global pandemic, which has devastating effects on health, economy and social interactions. Despite the less contraction and spread of COVID-19 in Africa compared to some other continents in the world, Africa remains amongst the most vulnerable regions due to less technology and unequipped or poor health system. Recent happenings showed that COVID-19 may stay for years owing to the discoveries of new variants (such as Omicron) and new wave of infections in several countries. Therefore, accurate prediction of new cases is vital to make informed decisions and in evaluating the measures that should be implemented. Studies on COVID-19 prediction are limited in Africa despite the risks and dangers that the virus possessed. Hence, this study was performed to predict daily COVID-19 cases in 10 African countries spread across the north, south, east, west and central Africa considering countries with few and large number of daily COVID-19 cases. Machine learning (ML) models due to their nonlinearity and accurate prediction capabilities were employed for this purpose, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and conventional multiple linear regression (MLR) models. As any other natural process, the COVID-19 pandemic may contain both linear and nonlinear aspects. In such circumstances, neither nonlinear (ML) nor linear (MLR) models could be sufficient; hence, combining both ML and MLR models may produce better accuracy. Consequently, to improve the prediction efficiency of the ML models, novel ensemble approaches including ANN-E and SVM-E were employed. The advantage of using ensemble approaches is that they provide collective benefits of all the standalone models, thereby reducing their weaknesses and enhancing their prediction capabilities. The obtained results showed that ANFIS led to better prediction performance with MAD = 0.0106, MSE = 0.0003, RMSE = 0.0185 and R = 0.9059 in the validation step. The results of the proposed ensemble approaches demonstrated very high improvements in predicting the COVID-19 pandemic in Africa with MAD = 0.0073, MSE = 0.0002, RMSE = 0.0155 and R = 0.9616. The ANN-E improved the standalone models performance in the validation step up to 10%, 14%, 42%, 6%, 83%, 11%, 7%, 5%, 7% and 31% for Morocco, Sudan, Namibia, South Africa, Uganda, Rwanda, Nigeria, Senegal, Gabon and Cameroon, respectively. This study results offer a solid foundation in the application of ensemble approaches for predicting COVID-19 pandemic across all regions and countries in the world.
新型冠状病毒病 2019（COVID-19）已经在全球范围内引发了大流行，对健康、经济和社会互动造成了毁灭性的影响。尽管非洲的 COVID-19 病例比世界上其他一些大陆少，传播范围也小，但由于技术水平较低，医疗体系不完善或较差，非洲仍然是最脆弱的地区之一。最近的情况表明，由于新变种（如奥密克戎）的发现和一些国家新一波感染的发生，COVID-19 可能会持续多年。因此，准确预测新病例对于做出明智的决策和评估应实施的措施至关重要。尽管 COVID-19 病毒具有风险和危害，但非洲的 COVID-19 预测研究仍然有限。因此，进行了这项研究，以预测非洲 10 个国家的每日 COVID-19 病例，这些国家分布在非洲的北部、南部、东部、西部和中部，考虑到每日 COVID-19 病例数量较少和较多的国家。由于具有非线性和准确预测能力，机器学习（ML）模型被用于此目的，包括人工神经网络（ANN）、自适应神经模糊推理系统（ANFIS）、支持向量机（SVM）和传统的多元线性回归（MLR）模型。与任何其他自然过程一样，COVID-19 大流行可能包含线性和非线性方面。在这种情况下，非线性（ML）和线性（MLR）模型都可能不够充分；因此，结合 ML 和 MLR 模型可能会产生更好的准确性。因此，为了提高 ML 模型的预测效率，采用了新颖的集成方法，包括 ANN-E 和 SVM-E。使用集成方法的优点是它们提供了所有独立模型的集体优势，从而减少了它们的弱点并增强了它们的预测能力。获得的结果表明，在验证步骤中，ANFIS 导致更好的预测性能，MAD=0.0106，MSE=0.0003，RMSE=0.0185 和 R=0.9059。提出的集成方法的结果表明，在预测非洲 COVID-19 大流行方面，非常高的改进，MAD=0.0073，MSE=0.0002，RMSE=0.0155 和 R=0.9616。ANN-E 提高了独立模型在验证步骤中的性能，摩洛哥、苏丹、纳米比亚、南非、乌干达、卢旺达、尼日利亚、塞内加尔、加蓬和喀麦隆的性能分别提高了 10%、14%、42%、6%、83%、11%、7%、5%、7%和 31%。这项研究结果为在世界所有地区和国家应用集成方法预测 COVID-19 大流行提供了坚实的基础。
