Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
University of Development Studies, Electrical Engineering Department, School of Engineering, Nyankpala Campus, Ghana.
Biomed Res Int. 2022 Mar 14;2022:7731618. doi: 10.1155/2022/7731618. eCollection 2022.
While the world continues to grapple with the devastating effects of the SARS-nCoV-2 virus, different scientific groups, including researchers from different parts of the world, are trying to collaborate to discover solutions to prevent the spread of the COVID-19 virus permanently. Henceforth, the current study envisions the analysis of predictive models that employ machine learning techniques and mathematical modeling to mitigate the spread of COVID-19. A systematic literature review (SLR) has been conducted, wherein a search into different databases, viz., PubMed and IEEE Explore, fetched 1178 records initially. From an initial of 1178 records, only 50 articles were analyzed completely. Around (64%) of the studies employed data-driven mathematical models, whereas only (26%) used machine learning models. Hybrid and ARIMA models constituted about (5%) and (3%) of the selected articles. Various Quality Evaluation Metrics (QEM), including accuracy, precision, specificity, sensitivity, Brier-score, F1-score, RMSE, AUC, and prediction and validation cohort, were used to gauge the effectiveness of the studied models. The study also considered the impact of Pfizer-BioNTech (BNT162b2), AstraZeneca (ChAd0x1), and Moderna (mRNA-1273) on Beta (B.1.1.7) and Delta (B.1.617.2) viral variants and the impact of administering booster doses given the evolution of viral variants of the virus.
当世界继续应对 SARS-nCoV-2 病毒的破坏性影响时，包括来自世界不同地区的研究人员在内的不同科学团体正在努力合作，以寻找永久防止 COVID-19 病毒传播的解决方案。因此，本研究旨在分析使用机器学习技术和数学建模来减轻 COVID-19 传播的预测模型。进行了系统文献综述（SLR），其中对不同数据库（即 PubMed 和 IEEE Explore）进行了搜索，最初获得了 1178 条记录。在最初的 1178 条记录中，只有 50 篇文章被完整分析。大约（64%）的研究采用了数据驱动的数学模型，而只有（26%）使用了机器学习模型。混合和 ARIMA 模型约占（5%）和（3%）选定文章。各种质量评估指标（QEM），包括准确性、精度、特异性、敏感性、Brier 得分、F1 得分、RMSE、AUC、预测和验证队列，用于评估所研究模型的有效性。该研究还考虑了辉瑞-生物技术公司（BNT162b2）、阿斯利康（ChAd0x1）和莫德纳（mRNA-1273）对 Beta（B.1.1.7）和 Delta（B.1.617.2）病毒变体的影响，以及在病毒变体不断进化的情况下，给予加强剂量对病毒的影响。
