Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications

[EN] One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective.This work was partially supported by the "Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018", Spain, under Grant RTI2018-096384-B-I00.Hernández-Orallo, E.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2020). Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications. Applied Sciences. 10(20):1-19. https://doi.org/10.3390/app10207113S1191020Li, R., Rivers, C., Tan, Q., Murray, M. B., Toner, E., & Lipsitch, M. (2020). The demand for inpatient and ICU beds for COVID-19 in the US: lessons from Chinese cities. doi:10.1101/2020.03.09.20033241(2020). Contact Transmission of COVID-19 in South Korea: Novel Investigation Techniques for Tracing Contacts. Osong Public Health and Research Perspectives, 11(1), 60-63. doi:10.24171/j.phrp.2020.11.1.09Eames, K. T. D., & Keeling, M. J. (2003). Contact tracing and disease control. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1533), 2565-2571. doi:10.1098/rspb.2003.2554Salathé, M. (2018). Digital epidemiology: what is it, and where is it going? Life Sciences, Society and Policy, 14(1). doi:10.1186/s40504-017-0065-7The FluPhone Studyhttps://www.fluphone.orgSafe Pathshttp://safepaths.mit.eduPan-European Privacy-Preserving Proximity Tracing (PEPP-PT)https://www.pepp-pt.orgPelusi, L., Passarella, A., & Conti, M. (2006). Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Communications Magazine, 44(11), 134-141. doi:10.1109/mcom.2006.248176Zhang, X., Neglia, G., Kurose, J., & Towsley, D. (2007). Performance modeling of epidemic routing. Computer Networks, 51(10), 2867-2891. doi:10.1016/j.comnet.2006.11.028Helgason, O., Kouyoumdjieva, S. T., & Karlsson, G. (2014). Opportunistic Communication and Human Mobility. IEEE Transactions on Mobile Computing, 13(7), 1597-1610. doi:10.1109/tmc.2013.160Chancay-Garcia, L., Hernandez-Orallo, E., Manzoni, P., Calafate, C. T., & Cano, J.-C. (2018). Evaluating and Enhancing Information Dissemination in Urban Areas of Interest Using Opportunistic Networks. IEEE Access, 6, 32514-32531. doi:10.1109/access.2018.2846201Dede, J., Forster, A., Hernandez-Orallo, E., Herrera-Tapia, J., Kuladinithi, K., Kuppusamy, V., … Vatandas, Z. (2018). Simulating Opportunistic Networks: Survey and Future Directions. IEEE Communications Surveys & Tutorials, 20(2), 1547-1573. doi:10.1109/comst.2017.2782182Hernández-Orallo, E., Murillo-Arcila, M., Calafate, C. T., Cano, J. C., Conejero, J. A., & Manzoni, P. (2016). Analytical evaluation of the performance of contact-Based messaging applications. Computer Networks, 111, 45-54. doi:10.1016/j.comnet.2016.07.006Hernandez-Orallo, E., Olmos, M. D. S., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2015). CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes. IEEE Transactions on Mobile Computing, 14(6), 1162-1175. doi:10.1109/tmc.2014.2343627Hernandez-Orallo, E., Manzoni, P., Calafate, C. T., & Cano, J.-C. (2020). Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19. IEEE Access, 8, 99083-99097. doi:10.1109/access.2020.2998042Christaki, E. (2015). New technologies in predicting, preventing and controlling emerging infectious diseases. Virulence, 6(6), 558-565. doi:10.1080/21505594.2015.1040975Cecilia, J. M., Cano, J., Hernández‐Orallo, E., Calafate, C. T., & Manzoni, P. (2020). Mobile crowdsensing approaches to address the COVID‐19 pandemic in Spain. IET Smart Cities, 2(2), 58-63. doi:10.1049/iet-smc.2020.0037Hernández-Orallo, E., Borrego, C., Manzoni, P., Marquez-Barja, J. M., Cano, J. C., & Calafate, C. T. (2020). Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing. Pervasive and Mobile Computing, 67, 101201. doi:10.1016/j.pmcj.2020.101201Doran, D., Severin, K., Gokhale, S., & Dagnino, A. (2015). Social media enabled human sensing for smart cities. AI Communications, 29(1), 57-75. doi:10.3233/aic-150683Salathe, M., Kazandjieva, M., Lee, J. W., Levis, P., Feldman, M. W., & Jones, J. H. (2010). A high-resolution human contact network for infectious disease transmission. Proceedings of the National Academy of Sciences, 107(51), 22020-22025. doi:10.1073/pnas.1009094108Fraser, C., Riley, S., Anderson, R. M., & Ferguson, N. M. (2004). Factors that make an infectious disease outbreak controllable. Proceedings of the National Academy of Sciences, 101(16), 6146-6151. doi:10.1073/pnas.0307506101Klinkenberg, D., Fraser, C., & Heesterbeek, H. (2006). The Effectiveness of Contact Tracing in Emerging Epidemics. PLoS ONE, 1(1), e12. doi:10.1371/journal.pone.0000012Kwok, K. O., Tang, A., Wei, V. W. I., Park, W. H., Yeoh, E. K., & Riley, S. (2019). Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. Computational and Structural Biotechnology Journal, 17, 186-194. doi:10.1016/j.csbj.2019.01.003Müller, J., Kretzschmar, M., & Dietz, K. (2000). Contact tracing in stochastic and deterministic epidemic models. Mathematical Biosciences, 164(1), 39-64. doi:10.1016/s0025-5564(99)00061-9Huerta, R., & Tsimring, L. S. (2002). Contact tracing and epidemics control in social networks. Physical Review E, 66(5). doi:10.1103/physreve.66.056115Lipsitch, M., Cohen, T., Cooper, B., Robins, J. M., Ma, S., James, L., … Murray, M. (2003). Transmission Dynamics and Control of Severe Acute Respiratory Syndrome. Science, 300(5627), 1966-1970. doi:10.1126/science.1086616Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., Jarvis, C. I., Russell, T. W., … van Zandvoort, K. (2020). Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health, 8(4), e488-e496. doi:10.1016/s2214-109x(20)30074-7Farrahi, K., Emonet, R., & Cebrian, M. (2014). Epidemic Contact Tracing via Communication Traces. PLoS ONE, 9(5), e95133. doi:10.1371/journal.pone.0095133Yang, H.-X., Wang, W.-X., Lai, Y.-C., & Wang, B.-H. (2012). Traffic-driven epidemic spreading on networks of mobile agents. EPL (Europhysics Letters), 98(6), 68003. doi:10.1209/0295-5075/98/68003Anglemyer, A., Moore, T. H., Parker, L., Chambers, T., Grady, A., Chiu, K., … Bero, L. (2020). Digital contact tracing technologies in epidemics: a rapid review. Cochrane Database of Systematic Reviews, 2020(8). doi:10.1002/14651858.cd013699Braithwaite, I., Callender, T., Bullock, M., & Aldridge, R. W. (2020). Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19. The Lancet Digital Health, 2(11), e607-e621. doi:10.1016/s2589-7500(20)30184-9Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., … Fraser, C. (2020). Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491). doi:10.1126/science.abb6936Cencetti, G., Santin, G., Longa, A., Pigani, E., Barrat, A., Cattuto, C., … Lepri, B. (2020). Digital proximity tracing on empirical contact networks for pandemic control. doi:10.1101/2020.05.29.20115915Kretzschmar, M. E., Rozhnova, G., Bootsma, M., van Boven, M., van de Wijgert, J., & Bonten, M. (2020). Time is of the essence: impact of delays on effectiveness of contact tracing for COVID-19, a modelling study. doi:10.1101/2020.05.09.20096289Lambert, A. (2020). A mathematically rigorous assessment of the efficiency of quarantining and contact tracing in curbing the COVID-19 epidemic. doi:10.1101/2020.05.04.20091009Sattler, F., Ma, J., Wagner, P., Neumann, D., Wenzel, M., Schäfer, R., … Wiegand, T. (2020). Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements. npj Digital Medicine, 3(1). doi:10.1038/s41746-020-00340-0Coronavirus: How to Do Testing and Contact Tracinghttps://medium.com/@tomaspueyoLi, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020). Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science, 368(6490), 489-493. doi:10.1126/science.abb322

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