Automated Robot and Artificial Intelligence-Powered Wastewater Surveillance for Proactive Mpox Outbreak Prediction

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

BIOSAFETY AND HEALTH（2024）

National Clinical Research Center for Infectious Disease | International Collaborative Laboratory of 2D | Shenzhen Metasensing Tech Ltd Co | Guangdong Prov Ctr Dis Control & Prevent

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Abstract

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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Key words

Automated online sewage virus enrichment robot,Artificial intelligence (AI) model,Early warning system,Mpox,Monkeypox virus (MPXV)

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
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