Mpox outbreak: Time series analysis with multifractal and deep learning network

• Published in: Chaos (Woodbury, N.Y.) Vol. 34; no. 10
• Main Authors: Priyanka, T M C, Gowrisankar, A, Banerjee, Santo
• Format: Journal Article
• Language: English
• Published: United States 01-10-2024
• Subjects: Deep Learning; Disease Outbreaks; Fractals; Humans; Neural Networks, Computer
• ISSN: 1089-7682
• DOI: 10.1063/5.0236082

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.