Application of Machine Learning Algorithms for Effective Determination of COVID-19 Clusters

Abstract

COVID-19 is a pandemic that cost the lives of over seven hundred thousand human lives. The viral infection is said to have at least five stages where the last stage is lethal. The primary stages are said to be highly infectious. At each stage, different medical tests are deployed to confirm the impact of the virus on a patient. Lockdowns are imposed in different geographical locations based on the possibility of transmission to other people in the region. Currently contact tracing with the help of quick response codes and manual methods of identifying and declaring COVID-19 clusters is in place. The existing machine learning algorithms could be put to the aid of contact tracing and to improve the speed and accuracy of determining COVID-19 clusters. The improved accuracy of determining the clusters would help avoiding unnecessary COVID-19 tests which are inherently expensive and curb the spread of the virus. Early detection of a cluster could also reduce the radius of the cluster thereby avoiding unnecessary lockdowns in some areas.