Abstract
The rapid spread of COVID-19 has demonstrated the need for accurate information to contain its diffusion. Technological solutions are a complement that can help citizens to be informed about the risk in their environment. Although measures such as contact traceability have been successful in some countries, their use raises society’s resistance. This paper proposes a variation of the consensus processes in directed networks to create a risk map of a determined area. The process shares information with trusted contacts: people we would notify in the case of being infected. When the process converges, each participant would have obtained the risk map for the selected zone.
A consensus simulation has been introduced in an SEIR model to evaluate how having available a risk map could affect the virus’s propagation. The scenario chosen is La Gomera Island: a region where the Spanish government has tested its contact tracing app (RadarCOVID). The paper also compares both strategies joint and separately: contact tracing to detect potential infections, and risk maps to avoid movements into conflictive areas. Contact tracing apps could work with 40% of participants instead of 60%. On the other hand, the elaboration of risk maps could work with just a 20% of active installations. Nevertheless, the effect is to delay the propagation instead of reducing the contagion. With both strategies actives, we significantly reduce infected peoples with a relatively low participant number.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Apple, Google: Exposure notifications: using technology to help public health authorities fight covid’19, 01 June 2020. https://www.google.com/covid19/exposurenotifications/
Apple, Google: Privacy-preserving contact tracing, 01 June 2020. https://www.apple.com/covid19/contacttracing
Castelluccia, C., Bielova, N., Boutet, A., Cunche, M., Lauradoux, C., Le Métayer, D., Roca, V.: ROBERT: ROBust and privacy-presERving proximity Tracing, May 2020. https://hal.inria.fr/hal-02611265/file/ROBERT-v1.1.pdf
Chan, J., Foster, D., Gollakota, S., Horvitz, E., Jaeger, J., Kakade, S., Kohno, T., Langford, J., Larson, J., Sharma, P., Singanamalla, S., Sunshine, J., Tessaro, S.: Pact: privacy sensitive protocols and mechanisms for mobile contact tracing (2020). arXiv:2004.03544 [cs.CR]
Dominguez-Garcia, A.D., Hadjicostis, C.N.: Distributed matrix scaling and application to average consensus in directed graphs. IEEE Trans. Autom. Control 58(3), 667–681 (2013)
González, M., Hidalgo, C., Barabási, A.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)
Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)
O’Neill, P.H., Ryan-Mosley, T., Johnson, B.: A flood of coronavirus apps are tracking us. Now it’s time to keep track of them, 07 May 2020. https://www.technologyreview.com/2020/05/07/1000961/launching-mittr-covid-tracing-tracker/
Pan-European privacy-preserving proximity tracing, 01 June 2020. https://www.pepp-pt.org/
Rivest, R.L., Weitzner, D.J., Ivers, L.C., Soibelman, I., Zissman, M.A.: Pact: private automated contact tracing, 01 June 2020. pact.mit.edu
Hinch, R., Probert, W., Nurtay, A., Kendall, M., Wymant, C., Hall, M., Lythgoe, K., Cruz, A.B., Zhao, L., Stewart, A., Ferretti, L., Parker, M., Meroueh, A., Mathias, B., Stevenson, S., Montero, D., Warren, J., Mather, N.K., Finkelstein, A., Abeler-Dörner, L., Bonsall, D., Fraser, C.: Effective Configurations of a Digital Contact Tracing App: A report to NHSX. University of Oxford, 16 April 2020
Simko, L., Calo, R., Roesner, F., Kohno, T.: Covid-19 contact tracing and privacy: studying opinion and preferences (2020). arXiv:2005.06056 [cs.CR]
The European Data Protection Board: Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak, 01 June 2020. https://edpb.europa.eu/our-work-tools/our-documents/usmernenia/guidelines-042020-use-location-data-and-contact-tracing_en
Troncoso, C., Payer, M., Hubaux, J.P., Salath, M., Larus, J., Bugnion, E., Lueks, W., Stadler, T., Pyrgelis, A., Antonioli, D., Barman, L., Chatel, S., Paterson, K., apkun, S., Basin, D., Beutel, J., Jackson, D., Preneel, B., Smart, N., Singelee, D., Abidin, A., Guerses, S., Veale, M., Cremers, C., Binns, R., Cattuto, C.: Decentralized privacy-preserving proximity tracing, April 2020. https://github.com/DP-3T/documents/blob/master/DP3T
Vinuesa, R., Theodorou, A., Battaglini, M., Dignum, V.: A socio-technical framework for digital contact tracing. Results Eng. 8, 100163 (2020)
WeTrace: Privacy-preserving bluetooth covid-19 contract tracing application, 01 June 2020. https://wetrace.ch/
Yang, Z., Zeng, Z., Wang, K., Wong, S.S., Liang, W., Zanin, M., Liu, P., Cao, X., Gao, Z., Mai, Z., Liang, J., Liu, X., Li, S., Li, Y., Ye, F., Guan, W., Yang, Y., Li, F., Luo, S., Xie, Y., Liu, B., Wang, Z., Zhang, S., Wang, Y., Zhong, N., He, J.: Modified SEIR and AI prediction of the epidemics trend of covid-19 in china under public health interventions. J. Thorac. Dis. 12(3) (2020)
Acknowledgements
This research was supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215, and by the Spanish Ministry of Science, Innovation and Universities (MICIU) under Contract No. PGC2018-093854-B-I00b and RTI2018-095390-B-C32.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rebollo, M., Benito, R.M., Losada, J.C., Galeano, J. (2021). Using Distributed Risk Maps by Consensus as a Complement to Contact Tracing Apps. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-65347-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65346-0
Online ISBN: 978-3-030-65347-7
eBook Packages: EngineeringEngineering (R0)