Modeling Control Measure Score of COVID-19 Outbreak Using Fuzzy c-Means-Based Adaptive Neuro-Fuzzy Inference System

The current outbreak of COVID-19 continues to threaten across the globe even after 9 months of its starting. The preventive measures and self-protecting measures are the suggested and viable solution to mitigate the spread of pandemic till new vaccine is discovered. The preventive measures are framed based on the detailed analysis of the current data of the ongoing pandemic. The correctness of the policies relies on the quality of the data and its analysis with a meaningful interpretation. In this paper, the present status of the epidemic across the world is analyzed using a control measure score which is computed using adaptive neuro-fuzzy inference system (ANFIS) where partition of data is performed using fuzzy c-means clustering. We have used transmission rate and recovery rate independently obtained from the discrete version of the Susceptible-Infected-Recovered (SIR) model to predict the score. Since the transmission parameters depends on incubation period, recovery and death, the parameter has been calculated based on active cases where its mean value changes significantly. It is observed that the recovery rate is more than the transmission rate of all Gulf countries at present which shows that the outbreak reached its highest value. The current control measure score emphasizes on the normal working atmosphere keeping strict social distancing measures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.