COVID-19 pandemic is severely impacting the lives of billions across the globe. Even after taking massive protective measures like nation-wide lockdowns, discontinuation of international flight services, rigorous testing etc., the infection spreading is still growing steadily, causing thousands of deaths and serious socio-economic crisis. Thus, the identification of the major factors of this infection spreading dynamics is becoming crucial to minimize impact and lifetime of COVID-19 and any future pandemic. In this work, a probabilistic cellular automata based method has been employed to model the infection dynamics for a significant number of different countries. This study proposes that for an accurate data-driven modelling of this infection spread, cellular automata provides an excellent platform, with a sequential genetic algorithm for efficiently estimating the parameters of the dynamics. To the best of our knowledge, this is the first attempt to understand and interpret COVID-19 data using optimized cellular automata, through genetic algorithm. It has been demonstrated that the proposed methodology can be flexible and robust at the same time, and can be used to model the daily active cases, total number of infected people and total death cases through systematic parameter estimation. Elaborate analyses for COVID-19 statistics of forty countries from different continents have been performed, with markedly divergent time evolution of the infection spreading because of demographic and socioeconomic factors. The substantial predictive power of this model has been established with conclusions on the key players in this pandemic dynamics.

COVID-19 大流行正在严重影响全球数十亿人的生活。即使采取了全国封锁、停止国际航班服务、严格检测等大规模保护措施，感染传播仍在稳步增长，造成数千人死亡和严重的社会经济危机。因此，确定这种感染传播动态的主要因素对于最大限度地减少 COVID-19 和任何未来大流行的影响和生命周期变得至关重要。在这项工作中，基于概率元胞自动机的方法已被用于对大量不同国家的感染动态进行建模。这项研究提出，对于这种感染传播的准确数据驱动建模，元胞自动机提供了一个极好的平台，使用顺序遗传算法有效地估计动力学参数。据我们所知，这是第一次尝试通过遗传算法使用优化的细胞自动机来理解和解释 COVID-19 数据。已经证明，所提出的方法同时具有灵活性和鲁棒性，可用于通过系统参数估计对每日活跃病例、感染者总数和死亡病例总数进行建模。对来自不同大陆的 40 个国家的 COVID-19 统计数据进行了详细分析，由于人口和社会经济因素，感染传播的时间演变明显不同。该模型的强大预测能力已经建立，并得出了对这种大流行动态中的关键参与者的结论。