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Inferring statistical trends of the COVID19 pandemic from current data. Where probability meets fuzziness
Information Sciences ( IF 8.1 ) Pub Date : 2021-06-09 , DOI: 10.1016/j.ins.2021.06.011
Bruno Apolloni 1
Affiliation  

We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: (1) a basic tool to deal with samples that are both truncated and non independently drawn, and (2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in an intermediate domain between probability models and fuzzy sets, still maintaining probabilistic features of the employed statistics as the reference KPI of the tools. This research uses as a benchmark the daily cumulative death numbers of COVID19 in two countries, with no any ancillary data. Numerical results show: (i) the model capability of capturing the inflection point and forecasting the end-of-infection time and related outbreak size, and (ii) the out-performance of the model inference method according to conventional indicators.



中文翻译:

从当前数据推断 COVID19 大流行的统计趋势。当概率遇到模糊

当遏制措施改变这些特征时,我们引入了前所未有的工具来推断 COVID19 爆发的近似演化特征。在这个框架中,我们提出:(1) 一个基本工具来处理被截断和非独立绘制的样本,以及 (2) 一个两阶段随机变量来捕捉过程演变中的游戏规则改变者。为了克服这些挑战,我们位于概率模型和模糊集之间的中间域,仍然保持所用统计的概率特征作为工具的参考 KPI。本研究以两国 COVID19 每日累计死亡人数为基准,没有任何辅助数据。数值结果表明:(i)捕捉拐点并预测感染结束时间和相关爆发规模的模型能力,

更新日期:2021-06-20
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