Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.
Biomed Res Int
; 2022: 7731618, 2022.
Article
in English
| MEDLINE | ID: covidwho-1745620
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Decision Making, Computer-Assisted
/
Artificial Intelligence
/
Machine Learning
/
Forecasting
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
/
Reviews
/
Systematic review/Meta Analysis
Topics:
Vaccines
/
Variants
Limits:
Humans
Language:
English
Journal:
Biomed Res Int
Year:
2022
Document Type:
Article
Affiliation country:
2022
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