An Approach to Identify Covid Infected People Due to Contact by using Machine Learning

Since the year 2020, the world has faced an unprecedented situation resulting from the spread of a deadly and highly contagious Coronavirus disease. Ever since the inception of the Coronavirus disease 2019 (Covid- 19), scientists all over the world have started preparing methods to fight the disease to save as many human lives as possible. One preliminary solution was hidden in the spreading mechanism of the virus, which was physical contact. So, the solution was to follow social distancing as much as possible. Another solution, which was the second stage solution, was to find the affected patients and isolate them to prevent the disease from spreading. Thus, contact tracing mechanisms were developed to find potential patients in as little time as possible. The Density-based spatial clustering of applications with noise (DBSCAN) algorithm proves to be an efficient solution to implement the proposed solution by making use of the physical locations of patients and geotagging them. This paper focuses on the development of a standalone web application that performs the task of contact tracing by making use of machine learning clustering algorithms. This reduces the time taken by competent authorities and helps them to find probable covid -positive patients in a much lesser period.