利用高斯模型和梯形模型了解全球新冠肺炎感染病例数据

H. Nieto-Chaupis
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引用次数: 1

摘要

由于Covid-19大流行导致的第一波大流行一旦开始,在大多数国家都可以看到一种常见的模式,即说明感染人数。然而,人们也发现,在大多数这些数据集中,在不均匀的测量机制中可能存在误差。本文从流行病的一般梯形表示出发,用它的三角形部分估计第三波的持续时间。从这个建议中,熵和概率都以自发的方式出现了。因此,目前的理论认为,可以从感染高峰和第二波持续时间来计算第三波持续时间。利用所提出的理论进行了仿真,并对仿真结果进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding the Global Data of Infections Cases by Covid-19 Through Gaussian and Trapezoid Models
Once initialized the first wave of pandemic due to Covid-19 pandemic, it was seen in most countries a common pattern that gives account about the number of infections. Nevertheless one finds also that in mosly of these datasets could have had errors acquired in the inhomogeneous mechanisms of measurement. In this paper from a general trapezoid-like representation of pandemic, the triangular part of it, is used to estimate the duration of a third wave. From this proposal both entropy and probability haved emerged in a spontaneous manner. Therefore the present theory suggests that from the peak of infections as well the duration of second wave, the duration of third wave might be calculated. Simulations from the presented theory are done and the results are discussed.
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