Kemeny-based testing for COVID-19

Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is: who should be test...

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Bibliographic Details
Published in:PloS one 2020-11, Vol.15 (11), p.e0242401
Main Authors: Yilmaz, Serife, Dudkina, Ekaterina, Bin, Michelangelo, Crisostomi, Emanuele, Ferraro, Pietro, Murray-Smith, Roderick, Parisini, Thomas, Stone, Lewi, Shorten, Robert
Format: Article
Language:English
Subjects:
Algorithms
Betacoronavirus
Biology and Life Sciences
Clinical Laboratory Techniques
Community structure
Computer and Information Sciences
Contact
Contact Tracing
Coronavirus Infections - diagnosis
Coronavirus Infections - transmission
Coronaviruses
COVID-19
COVID-19 Testing
Design
Design engineering
Diagnosis
Disease transmission
Ecology and Environmental Sciences
Engineering and Technology
Epidemics
Epidemiology
Humans
Immunization
Markov analysis
Markov processes
Medical statistics
Medicine and Health Sciences
Methods
Models, Theoretical
Pandemics
Physical Sciences
Pneumonia, Viral - diagnosis
Pneumonia, Viral - transmission
Probability
Research and Analysis Methods
SARS-CoV-2
Smartphones
Spreaders
Statistics
Ultrasonic testing
Viral diseases
Viruses
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Summary:Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is: who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our 'Kemeny indicator' is the value of the Kemeny constant in the new graph that is obtained when a node is removed from the original graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage. Extensive simulations provide promising results in early identification and in blocking the possible 'super-spreaders' links that transmit disease between different communities.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0242401