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.

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Covid-19 and its continuously evolving viral variants continue to send shocking waves across the globe, with infections seeing a continuous surge in different parts of the world. Given the extensive mutations of the Covid-19 virus in the RBD region, the threat and capability of latent potential for infection through subsequent adaptation via antigenic drift cannot be overlooked. The current review study envisages the efficacy and reliability of vaccines for determining their cogency against different evolving strains of Covid-19. A total of 50 studies have been reviewed-further scrutiny for ensuring the efficiency of selected studies furnished only 13 articles of credible quality. The study concludes overall effectiveness of 95% with Pfizer-BioNTech followed by Moderna with an efficiency of 94% against B.1.1.7 and B.1.617.2.Oxford AstraZeneca is only 84% effective, followed by BBV152 and Janssen, which are yet to be approved for different settings owing to their adverse side effects.

The course management system (CMS) is a basic software piece of any educational institution. Web enabled CMS is a tool to handle large number of students institutions. There are many open sources CMS, they are not having software engineering based documentations. This leads to a lot of problems in its maintenance and operation. These systems do not consider the locality and policy of institutions. All open source CMS do not support quality assurance concepts in education. Authors of that research proposed a CMS based on reverse engineering of the open CMS "Moodle system". Authors applied their reverse engineering approach (Ref. [1]) on moodle to come up with new CMS. The proposed CMS is more maintainable, extendable, and technically updatable. Moodle CMS is analyzed to identify the proposed system's components and their interrelationships. The functionality of the proposed CMS could be improved to be suitable for university with different faculties and departments. New modules for the proposed CMS such as annual course report could be easy to plug to the proposed CMS.

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