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Public transportation network scan for rapid surveillance

Public transportation network scan for rapid surveillance As people move around using public transportation networks such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decision in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work in the emergency situation where computational time is essentially important. We propose a new scan statistics approach for the public transportation network, called PTNS (Public Transportation Network Scan). PTNS utilizes the available network structure to construct potential candidates of clusters, and thus it can work well especially in situations where public transportation is the main medium of the infection spread. Further, it is designed for rapid surveillance. Lastly, PTNS is generalized to detect space-time clusters by customizing the iteration for potential clusters creation. Using the simulation data generated with a real railway network, we showed that, PTNS outperformed the conventional methods, including Circular- and Flex-scan approaches, in terms of the detection performance, while the computational time is feasible. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Public transportation network scan for rapid surveillance

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Public transportation network scan for rapid surveillance

Abstract

As people move around using public transportation networks such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decision in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work in the emergency situation where computational time is...
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Publisher
Taylor & Francis
Copyright
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2022.2069458
Publisher site
See Article on Publisher Site

Abstract

As people move around using public transportation networks such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decision in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work in the emergency situation where computational time is essentially important. We propose a new scan statistics approach for the public transportation network, called PTNS (Public Transportation Network Scan). PTNS utilizes the available network structure to construct potential candidates of clusters, and thus it can work well especially in situations where public transportation is the main medium of the infection spread. Further, it is designed for rapid surveillance. Lastly, PTNS is generalized to detect space-time clusters by customizing the iteration for potential clusters creation. Using the simulation data generated with a real railway network, we showed that, PTNS outperformed the conventional methods, including Circular- and Flex-scan approaches, in terms of the detection performance, while the computational time is feasible.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jun 1, 2022

Keywords: Scan statistics; Spatio-Temporal analysis; rapid surveillance; public Transportation; covid-19

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