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....
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| Veröffentlicht in: | Chaos (Woodbury, N.Y.) N.Y.), 2024-10, Vol.34 (10) |
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| container_title | Chaos (Woodbury, N.Y.) |
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| creator | Priyanka, T. M. C. Gowrisankar, A. Banerjee, Santo |
| description | 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. |
| doi_str_mv | 10.1063/5.0236082 |
| format | Article |
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| language | eng |
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| source | MEDLINE; AIP Journals Complete |
| subjects | Deep Learning Disease Outbreaks Fractal analysis Fractals Heterogeneity Humans Mpox (monkeypox) Neural networks Neural Networks, Computer Outbreaks Public health Self-similarity Viral diseases |
| title | Mpox outbreak: Time series analysis with multifractal and deep learning network |
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