Abstract
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.
【초록키워드】 Evolution, mRNA-1273, Delta, B.1.617.2, virus, BNT162b2, Spread, sensitivity, specificity, Predictive model, Accuracy, B.1.1.7, mRNA, Effectiveness, COVID-19 virus, Viral variants, Beta, Quality, viral variant, booster dose, Pfizer-BioNTech, AstraZeneca, Moderna, hybrid, Analysis, ARIMA, machine learning models, AUC, literature review, booster doses, Precision, validation cohort, article, predictive models, while, researcher, Effect, mitigate, data-driven, Prevent, articles, spread of COVID-19, initial, selected, analyzed, conducted, were used, groups, mathematical, 【제목키워드】 COVID-19, review, artificial, response, Impact, modeling, intelligence,