Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty
成果类型：
Article
署名作者：
Baboun, Jose; Beaudry, Isabelle S.; Castro, Luis M.; Gutierrez, Felipe; Jara, Alejandro; Rubioa, Benjamin; Verschaea, Jose
署名单位：
Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile; Mount Holyoke College; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile
刊物名称：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN/ISSBN：
0027-11178
DOI：
10.1073/pnas.2316616121
发表日期：
2024-04-02
关键词：
摘要：
Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.