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

IF 3.5 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Guanyong Ou , Yuxuan Tang , Jiexiang Liu , Yabin Hao , Zhi Chen , Ting Huang , Shaxi Li , Shiyu Niu , Yun Peng , Jiaqi Feng , Hongwei Tu , Yang Yang , Han Zhang , Yingxia Liu
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引用次数: 0

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.

机器人和人工智能驱动的废水自动监测,用于猴痘疫情的主动预测
自2022年5月以来,人类历史上爆发了规模最大、地域传播范围最广的猴痘疫情,为此,我们创新性地开发了一种用于疫情追踪的在线自动污水病毒富集和浓缩机器人。结合人工智能(AI)模型,我们的研究旨在根据污水中猴痘病毒(MPXV)的浓度来估计痘病例。我们的研究发现,废水中的猴痘病毒浓度与临床确诊的猴痘感染病例数量之间存在着令人信服的联系,而我们的人工智能预测模型能够非常精确地预测病例,捕捉到了数据变化的 87%,从而进一步证实了这一发现。不过,值得注意的是,这种高精度预测可能与数据采集频率相对较高以及医院本身相对非流动的隔离环境有关。总之,这项研究标志着我们在跟踪和应对麻痘爆发的能力方面向前迈出了重要一步。通过利用创新技术进行疾病监测和预测,它有可能彻底改变公共卫生监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biosafety and Health
Biosafety and Health Medicine-Infectious Diseases
CiteScore
7.60
自引率
0.00%
发文量
116
审稿时长
66 days
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