An algorithm to identify the positive COVID-19 cases using genetic algorithm (GABFCov 19)

Abstract

The entire world is under the existential health emergency: the spread of a contagious corona virus. Since this pandemic can be classified as moderate, mild and high risk. But the same method is there to quarantine, monitor and treat with the same set of drugs. Technology is growing at a fast pace and is trying its level best to control, cure and diagnose at the earliest so that the contagious nature of this disease is controlled by Big Data , Artificial Intelligence , Genetic algorithms etc. The only solution to stop this community spread is to have a full proof contact tracing mechanism wherein the diagnosed corona positive patient can be backtracked in real time and all the proximity users are warned and take precautionary measures to stop the spread of this disease. Till now the contact tracing mechanism only highlight the proximity user but no such mechanism is available to signify the risk analysis of those users. In this paper the new algorithm has been developed GABFCov-19 which is based on genetic algorithm. This algorithm will generate high probability positive cases so that it can reduce the community spread. This algorithm is the extended version of the contact tracing mechanism where the clusters are identified. The clusters have been taken from the proximity cluster users and converted into chromosomes that are again operated using selection, crossover and mutation operators' in order to categorize the proximity users as mild, moderate and high risk. The proposed algorithm is tested on dataset and obtained significant results.

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