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Title: Using data mining to estimate patterns of contagion-risk interactions in an intercity public road transport system
Authors: Cristóbal, Teresa
Quesada-Arencibia, Alexis 
de Blasio, Gabriele S. 
Padrón, Gabino 
Alayón, Francisco 
García, Carmelo R. 
UNESCO Clasification: 120304 Inteligencia artificial
3327 Tecnología de los sistemas de transporte
Keywords: Close contact patterns
Data mining
Epidemics, et al
Issue Date: 2022
Project: Proyecto COVID19-03
Journal: IEEE Access 
Abstract: The COVID-19 pandemic has had very negative effects on public transport systems. These effects have compromised the role they should play as enablers of social equity and environmentally sustainable mobility and have caused serious economic losses for public transport operators. For this reason, in the context of pandemics, meaningful epidemiological information gathered in the specific framework of these systems is of great interest. This article presents the findings of an investigation into the risk of transmission of a respiratory infectious disease in an intercity road transport system that carries millions of passengers annually. To achieve this objective, a data mining methodology was used to generate the data required to ascertain the level of risk. Using this methodology, the occupancy of vehicle seats by passengers was simulated using two different strategies. The first is an empirical approach to the behaviour of passengers when occupying a free seat and the second attempts to minimise the risk of contagion. For each of these strategies, the interactions with risk of infection between passengers were estimated, the patterns of these interactions on the different routes of the transport system were obtained using k-means clustering technique, and the impact of the strategies was analysed.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2022.3206838
Source: IEEE Access [ISSN 2169-3536], v. 10, (Septiembre 2022)
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