The coronavirus disease (COVID-19) outbreak started in December 2019 in Wuhan. The virus has spread around the whole world, and it has caused a strong and serious pandemic. Symptoms such as cough, respiratory distress, diarrhea, and fatigue associated with COVID-19 are typical clinical findings. Coronavirus infection has become an important public health concern because of its increasing prevalence, serious complications, and mortality. In light of this information, we examine different entropy methods for world indices (ISE 30, FTSE 100, NIKKEI 225, SP 500, and DAX 30) in the pre-COVID-19 period (02.01.2019–17.11.2019) and the post-COVID-19 period (18.11.2019–23.11.2020) in this article. Besides, we discuss the performances of entropies such as Shannon, Renyi, Tsallis, and approximate entropy (ApEn) in detail and perform the notion of entropy for volatility measure. As a result, we present the numerical results for the data set.

中文翻译：

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SARS-CoV-2流行病对世界指数的影响：熵值法

冠状病毒病（COVID-19）爆发于2019年12月在武汉开始。该病毒已散布到全世界，并引起了强烈而严重的大流行。典型的临床发现是与COVID-19相关的症状，例如咳嗽，呼吸窘迫，腹泻和疲劳。冠状病毒感染由于其患病率增加，严重的并发症和死亡率上升，已成为重要的公共卫生问题。根据这些信息，我们研究了COVID-19之前（02.01.2019–17.11.2019）和世界指数（ISE 30，FTSE 100，NIKKEI 225，SP 500和DAX 30）的不同熵方法。本文中的COVID-19后时期（18.11.2019–23.11.2020）。此外，我们讨论了诸如Shannon，Renyi，Tsallis，和近似熵（ApEn）的详细信息，并执行用于波动率度量的熵的概念。结果，我们给出了数据集的数值结果。