ABSTRACT
The world has faced the challenges of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) for the last two years, first diagnosed at the end of 2019 in Wuhan and widely distributed worldwide. As a result, the WHO has proclaimed the illness brought on by this virus to be a global pandemic. To combat COVID-19, researcher communities continuously develop and implement rapid diagnoses, safe and effective vaccinations and other alternative therapeutic procedures. However, synthetic drug-related side effects and high costs have piqued scientists' interest in natural product-based therapies and medicines. In this regard, antiviral substances derived from natural resources and some medicines have seen a boom in popularity. For instance, algae are a rich source of compounds such as lectins and sulfated polysaccharides, which have potent antiviral and immunity-boosting properties. Moreover, Algae-derived compounds or metabolites can be used as antibodies and vaccine raw materials against COVID-19. Furthermore, some algal species can boost immunity, reduce viral activity in humans and be recommended for usage as a COVID-19 preventative measure. However, this field of study is still in its early stages of development. Therefore, this review addresses critical characteristics of algal metabolites, their antioxidant potential and therapeutic potential in COVID-19.
ABSTRACT
Research is very important in the pandemic situation of COVID-19 to deliver a speedy solution to this problem. COVID-19 has presented governments, corporations and ordinary citizens around the world with technology playing an essential role to tackle the crisis. Moderate and flexible innovation arrangements that can speed up progress towards giving critical well-being ability are proved hourly. Knowledge with the aid of creativity must be obtained, accepted and analysed in a short time frame. In this example, the machine learning model has a major role to play in predicting the number of next positive COVID-19 cases to come. For government departments to take effective and strengthened future COVID-19 planning and innovation. The ongoing global pandemic of COVID-19 has been non-linear and dynamic. Due to the especially perplexing nature of the COVID-19 episode and its diversity from country to country, this study recommends machine learning as a convincing means to demonstrate flare-up. In this linear regression, polynomial regression, ridge regression, polynomial ridgeregression, support vector regression models, the COVID-19 data set from multiple on-line tools have been evaluated. During the work process comprehensive experiments were performed and each test was evaluated with the parameters mean square error (MSE), medium absolute error (MAE), root mean square error (RMSE) and R2 score. This study also offers a path for future research using regression models based on machine learning. Precise validation and data analysis can contribute to strategies for healing and disease prevention at an early stage. A systematic comprehensive strategy is a new philosophy in which statistical data for government agencies and community can be forecast.