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

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作者
Ou Guanyong [1 ]
Tang Yuxuan [2 ]
Liu Jiexiang [1 ,3 ]
Hao Yabin [1 ,4 ]
Chen Zhi [2 ]
Huang Ting [1 ]
Li Shaxi [1 ]
Niu Shiyu [1 ]
Peng Yun [1 ]
Feng Jiaqi [1 ]
Tu Hongwei [5 ]
Yang Yang [1 ]
Zhang Han [2 ]
Liu Yingxia [1 ]
机构
[1] Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, the Third People’s Hospital of Shenzhen, Second Hospital Affiliated to Southern University of Science an
[2] International Collaborative Laboratory ofD, Materials for Optoelectronics Science and Technology of Ministry of Education, Institute of Microscale Optoelectronics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
[3] Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China
[4] Shenzhen Metasensing Tech Limited Company, Shenzhen, China
[5] Guangdong Provincial Centre for Diseases Control and Prevention, Guangzhou,
关键词
Automated online sewage virus enrichment robot; Artificial intelligence (AI) model; Early warning system; Mpox; Monkeypox virus (MPXV);
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摘要
In the wake of the largest-ever recorded outbreak of mpox in terms of magnitude and geographical spread in human history since May 2022, we innovatively developed an automated online sewage virus enrichment and concentration robot for disease tracking. Coupled with an artificial intelligence (AI) model, our research aims to estimate mpox cases based on the concentration of the monkeypox virus (MPXV) in wastewater. Our research has revealed a compelling link between the levels of MPXV in wastewater and the number of clinically confirmed mpox infections, a finding that is reinforced by the ability of our AI prediction model to forecast cases with remarkable precision, capturing 87 % of the data’s variability. However, it is worth noting that this high precision in predictions may be related to the relatively high frequency of data acquisition and the relatively non-mobile isolated environment of the hospital itself. In conclusion, this study represents a significant step forward in our ability to track and respond to mpox outbreaks. It has the potential to revolutionize public health surveillance by utilizing innovative technologies for disease surveillance and prediction.
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