论文标题
使用基于顺序遗传算法的概率细胞自动机对COVID-19动力学的数据驱动理解
A Data-driven Understanding of COVID-19 Dynamics Using Sequential Genetic Algorithm Based Probabilistic Cellular Automata
论文作者
论文摘要
Covid-19-大流行正在严重影响全球数十亿美元的生活。即使采取了大规模的保护措施，例如全国范围内的封锁，中断国际飞行服务，严格的测试等，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，鉴定这种感染动力学的主要因素对于最大程度地减少了Covid-19的影响和寿命至关重要。在这项工作中，已经采用了一种基于概率的细胞自动机方法来对许多不同国家的感染动态进行建模。这项研究提出，为了对该感染扩散的准确数据驱动建模，细胞自动机提供了一个出色的平台，具有顺序的遗传算法，可有效估计动力学的参数。据我们所知，这是通过遗传算法使用优化的细胞自动机理解和解释COVID-19数据的首次尝试。已经证明，所提出的方法可以同时具有灵活性和鲁棒性，可用于通过系统的参数估计来对每日活跃病例，感染者总数和总死亡案例进行建模。已经对来自不同大陆的40个国家的共同统计数据进行了详尽的分析，由于人口统计学和社会经济因素，感染传播的时间显着分歧。该模型的实质性预测能力已经建立了这一大流行动态的主要参与者的结论。
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 modeling 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.
