Abstract:
The current COVID-19 pandemic has seen limited availability of testing kits thus hindering the ability to conduct more tests to determine the infection status among individuals at large which in turn does not allow to quantify the prevalence rate effectively and accurately. Group Testing is a long standing field that has immense potential in helping towards containing this pandemic. In this chapter, we leverage this mathematical tool to show how to effectively test a huge number of individuals while conducting relatively fewer number of tests than the number of individuals in perspective. We propose a theoretical model for accelerated COVID-19 testing which reduces the testing computational complexity appreciably.