Detecting the Behaviour of COVID-19 Based On Parallel Approach of Sequential Rule Mining Algorithm

The COVID-19 (Coronavirus) is a catastrophic disease, as it causes a global health crisis. Due to the nature of COVID-19, it spreads quickly among humans and infects millions of people within a few periods in the world. It is critical to detect the behavior of COVID-19 and the speed of its mutating rapidly for better improvement of medications and assists patients in preventing the progression of the disease. This paper examines the discovery of additional information and interest patterns in COVID-19 genome sequences through using non-redundant sequential rule mining from both frequent closed dynamic bit vector and sequential generator patterns simultaneously. It helps to discover nucleotide rules and make the prediction of the next one. Almost all genotyping tests are partial, time-consuming, and involve multi-step processes. So, we implement a parallel approach to produce the sequential rules in less time required. Our experimental results show that; the proposed PNRD-CloGen algorithm improves the efficiency of prevention procedures and reducing the runtime needed to produce the sequential rules. It also helps to monitor the strain progression of COVID-19 sequentially and enhance clinical management.