2020/08/27 by Sayantari Ghosh, Ghosh, Sayantari, Saumik Bhattacharya +1
Computer Science · #Cellular Automata and Applications
paper · pdf · doi:10.48550/arxiv.2008.12020
COVID-19 pandemic is severely impacting the lives of billions across the\nglobe. Even after taking massive protective measures like nation-wide\nlockdowns, discontinuation of international flight services, rigorous testing\netc., the infection spreading is still growing steadily, causing thousands of\ndeaths and serious socio-economic crisis. Thus, the identification of the major\nfactors of this infection spreading dynamics is becoming crucial to minimize\nimpact and lifetime of COVID-19 and any future pandemic. In this work, a\nprobabilistic cellular automata based method has been employed to model the\ninfection dynamics for a significant number of different countries. This study\nproposes that for an accurate data-driven modeling of this infection spread,\ncellular automata provides an excellent platform, with a sequential genetic\nalgorithm for efficiently estimating the parameters of the dynamics. To the\nbest of our knowledge, this is the first attempt to understand and interpret\nCOVID-19 data using optimized cellular automata, through genetic algorithm. It\nhas been demonstrated that the proposed methodology can be flexible and robust\nat the same time, and can be used to model the daily active cases, total number\nof infected people and total death cases through systematic parameter\nestimation. Elaborate analyses for COVID-19 statistics of forty countries from\ndifferent continents have been performed, with markedly divergent time\nevolution of the infection spreading because of demographic and socioeconomic\nfactors. The substantial predictive power of this model has been established\nwith conclusions on the key players in this pandemic dynamics.\n