Mpox 爆发:利用多分形和深度学习网络进行时间序列分析。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
T M C Priyanka, A Gowrisankar, Santo Banerjee
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引用次数: 0

摘要

本文概述了疫情最严重地区--非洲、美洲和欧洲--的天花流行情况,并采用分形插值法对天花病例进行预处理。这种敏锐的分析凸显了天花传播趋势中的不规则和分形模式。在当前因天花爆发而引起国际关注的公共卫生紧急事件中,本文的另一个意义在于从多分形的角度解释天花的传播。本文利用自相似度量,即多分形度量,来探讨水痘病例的异质性。此外,本文还利用双向长短期记忆神经网络来预测未来的天花传播,以提醒人们注意疫情的爆发,因为天花似乎是全球流行病的一种无声症状。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mpox outbreak: Time series analysis with multifractal and deep learning network.

This article presents an overview of an mpox epidemiological situation in the most affected regions-Africa, Americas, and Europe-tailoring fractal interpolation for pre-processing the mpox cases. This keen analysis has highlighted the irregular and fractal patterns in the trend of mpox transmission. During the current scenario of public health emergency of international concern due to an mpox outbreak, an additional significance of this article is the interpretation of mpox spread in light of multifractality. The self-similar measure, namely, the multifractal measure, is utilized to explore the heterogeneity in the mpox cases. Moreover, a bidirectional long-short term memory neural network has been employed to forecast the future mpox spread to alert the outbreak as it seems to be a silent symptom for global epidemic.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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