Automated robot and artificial intelligence-powered wastewater surveillance for proactive mpox outbreak prediction

Ou, Guanyong; Tang, Yuxuan; Liu, Jiexiang; Hao, Yabin; Chen, Zhi; Huang, Ting; Li, Shaxi; Niu, Shiyu; Peng, Yun; Feng, Jiaqi; Tu, Hongwei; Yang, Yang; Zhang, Han; Liu, Yingxia

doi:10.1016/j.bsheal.2024.07.002

Biosafety and Health

2024

DOI: 10.1016/j.bsheal.2024.07.002

|View full text |Cite

Automated robot and artificial intelligence-powered wastewater surveillance for proactive mpox outbreak prediction

Guanyong Ou,

Yuxuan Tang,

Jiexiang Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Priyanka,

Gowrisankar,

Banerjee

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

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.

show abstract

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

Priyanka,

Gowrisankar,

Banerjee

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Automated robot and artificial intelligence-powered wastewater surveillance for proactive mpox outbreak prediction

Cited by 1 publication

References 39 publications

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

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

Contact Info

Product

Resources

About