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基于全球飞机的废水监测网络的优化与性能分析

Optimization and performance analytics of global aircraft-based wastewater surveillance networks.

作者信息

St-Onge Guillaume, Davis Jessica T, Hébert-Dufresne Laurent, Allard Antoine, Urbinati Alessandra, Scarpino Samuel V, Chinazzi Matteo, Vespignani Alessandro

机构信息

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115, USA.

The Roux Institute, Northeastern University, Portland, ME 04101, USA.

出版信息

medRxiv. 2024 Aug 4:2024.08.02.24311418. doi: 10.1101/2024.08.02.24311418.

Abstract

Aircraft wastewater surveillance has been proposed as a novel approach to monitor the global spread of pathogens. Here we develop a computational framework to provide actionable information for designing and estimating the effectiveness of global aircraft-based wastewater surveillance networks (WWSNs). We study respiratory diseases of varying transmission potentials and find that networks of 10 to 20 strategically placed wastewater sentinel sites can provide timely situational awareness and function effectively as an early warning system. The model identifies potential blind spots and suggests optimization strategies to increase WWSNs effectiveness while minimizing resource use. Our findings highlight that increasing the number of sentinel sites beyond a critical threshold does not proportionately improve WWSNs capabilities, stressing the importance of resource optimization. We show through retrospective analyses that WWSNs can significantly shorten the detection time for emerging pathogens. The presented approach offers a realistic analytic framework for the analysis of WWSNs at airports.

摘要

飞机废水监测已被提议作为一种监测病原体全球传播的新方法。在此,我们开发了一个计算框架,为设计和评估全球飞机废水监测网络(WWSN)的有效性提供可操作的信息。我们研究了不同传播潜力的呼吸道疾病,发现由10至20个经过战略布局的废水监测点组成的网络能够提供及时的态势感知,并作为一个早期预警系统有效发挥作用。该模型识别出潜在的盲点,并提出优化策略以提高WWSN的有效性,同时将资源使用降至最低。我们的研究结果表明,超过临界阈值增加监测点数量并不会成比例地提高WWSN的能力,强调了资源优化的重要性。我们通过回顾性分析表明,WWSN可以显著缩短新兴病原体的检测时间。所提出的方法为机场WWSN的分析提供了一个现实的分析框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83fa/11312644/4202a5aa24df/nihpp-2024.08.02.24311418v1-f0001.jpg

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