A Study of the Impact of Covid-19 Using a Sieve Approach

COVID-19 is an exceptionally infectious illness that has affected the general public. Displaying such sicknesses can be critical in the expectation of their effect. The assessment of the COVID patient and its correlation with sequential age is a significant task in the clinic, however, with the help of machine learning algorithms we can effectively model the expectations. Getting factual expectation can be tedious as movements can be inclined to intra-rater changeability, the utilization of strategies that can computerize it, similar to machine learning techniques, is of value. The objective of this part is to introduce the strainer chart, patterns and holes in the examination identified with age and sex assessment that utilize machine learning techniques and also provides the COVID issues with various problems in combination with sieve methods.