How Human Mobility Models Can Help to Deal with COVID-19

[EN] One of the key factors for the spreading of human infections, such as the COVID-19, is human mobility. There is a huge background of human mobility models developed with the aim of evaluating the performance of mobile computer networks, such as cellular networks, opportunistic networks, etc. In this paper, we propose the use of these models for evaluating the temporal and spatial risk of transmission of the COVID-19 disease. First, we study both pure synthetic model and simulated models based on pedestrian simulators, generated for real urban scenarios such as a square and a subway station. In order to evaluate the risk, two different risks of exposure are defined. The results show that we can obtain not only the temporal risk but also a heat map with the exposure risk in the evaluated scenario. This is particularly interesting for public spaces, where health authorities could make effective risk management plans to reduce the risk of transmission.Hernández-Orallo, E.; Armero-Martínez, A. (2021). How Human Mobility Models Can Help to Deal with COVID-19. Electronics. 10(1):1-24. https://doi.org/10.3390/electronics1001003312410

A SIR-based model for contact-based messaging applications supported by permanent infrastructure

[EN] In this paper, we focus on the study of coupled systems of ordinary differential equations (ODEs) describing the diffusion of messages between mobile devices. Communications in mobile opportunistic networks take place upon the establishment of ephemeral contacts among mobile nodes using direct communication. SIR (Sane, Infected, Recovered) models permit to represent the diffusion of messages using an epidemiological based approach. The question we analyse in this work is whether the coexistence of a fixed infrastructure can improve the diffusion of messages and thus justify the additional costs. We analyse this case from the point of view of dynamical systems, finding and characterising the admissible equilibrium of this scenario. We show that a centralised diffusion is not efficient when people density reaches a sufficient value. This result supports the interest in developing opportunistic networks for occasionally crowded places to avoid the cost of additional infrastructure.This work was partially supported by Ministerio de Economia y Competitividad, Spain (Grants TEC2014-52690-R, MTM2016-75963-P & BCAM Severo Ochoa excellence accreditation SEV-2013-0323), Generalitat Valenciana, Spain (Grants AICO/2015/108, ACOMP/2015/005, GVA/2018/110), by the Basque Government through the BERC 2014-2017.Conejero, JA.; Hernández-Orallo, E.; Manzoni, P.; Murillo-Arcila, M. (2019). A SIR-based model for contact-based messaging applications supported by permanent infrastructure. Discrete and Continuous Dynamical Systems. Series S. 12(4-5):735-746. https://doi.org/10.3934/dcdss.2019048735746124-

Data Transmissions using Hub Nodes in Vehicular Social Networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Vehicular Social Networks (VSNs) consist of groups of individuals (i.e., people) who may share common interests, preferences and needs in the context of temporal spatial proximity on roads. In this environment, the impact of human social factors, such as mobility, willingness to cooperate and personal preferences, on vehicular connectivity is taken under consideration, thus extending the concept of Vehicular Ad-hoc Networks. In VSNs, vehicles are classified based on their social degree, a vehicle considered to be a ¿social¿ one if it accesses the vehicular social network and posts messages with a frequency higher than a given threshold. Therefore, to speed up the data dissemination process within a vehicular social network, a packet should be forwarded to those vehicles showing high social activity. In a previous paper, we introduced a new probabilistic-based broadcasting scheme called SCARF (SoCial-Aware Reliable Forwarding Technique for Vehicular Communications), and we analytically demonstrated its effectiveness in packet transmission reduction while guaranteeing network dissemination. In this paper, we assess SCARF in more realistic scenarios with real traffic traces, and we compare it with other similar techniques. We show that SCARF outperforms other approaches in terms of delivery ratio, while guaranteeing acceptable time delay values and average number of forwardings.Vegni, AM.; Souza, C.; Loscrí, V.; Hernández-Orallo, E.; Manzoni, P. (2020). Data Transmissions using Hub Nodes in Vehicular Social Networks. IEEE Transactions on Mobile Computing. 19(7):1570-1585. https://doi.org/10.1109/TMC.2019.2928803S1570158519

FALCON: A New Approach for the Evaluation of Opportunistic Networks

[EN] Evaluating the performance of opportunistic networks with a high number of nodes is a challenging problem. Analytical models cannot provide a realistic evaluation of these networks, and simulations can be very time-consuming, sometimes requiring even weeks only to provide the results of a single scenario. In this paper, we present a fast evaluation model called FALCON (Fast Analysis, using a Lattice Cell model, of Opportunistic Networks) that is computationally very efficient and precise. The model is based on discretising space and time in order to reduce the computation complexity, and we formalised it as a discrete dynamic system that can be quickly solved. We describe some validation experiments showing that the precision of the obtained results is equivalent to the ones obtained with standard simulation approaches. The experiments also show that computation time is reduced by two orders of magnitude (from hours to seconds), allowing for a faster evaluation of opportunistic networks. Finally, we show that the FALCON model is easy to adapt and expand to consider different scenarios and protocols.This work was partially supported by Ministerio de Economia y Competitividad, Spain, grant TEC2014-52690-R.Hernández-Orallo, E.; Cano, J.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2018). FALCON: A New Approach for the Evaluation of Opportunistic Networks. Ad Hoc Networks. 81:109-121. https://doi.org/10.1016/j.adhoc.2018.07.004S1091218

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

Indoor vehicles geolocalization using LoRaWAN

[EN] One of the main drawbacks of Global Navigation Satellite Sytems (GNSS) is that they do not work indoors. When inside, there is often no direct line from the satellite signals to the device and the ultra high frequency (UHF) used is blocked by thick, solid materials such as brick, metal, stone or wood. In this paper, we describe a solution based on the Long Range Wide Area Network (LoRaWAN) technology to geolocalise vehicles indoors. Through estimation of the behaviour of a LoRaWAN channel and using trilateration, the localisation of a vehicle can be obtained within a 20¿30 m range. Indoor geolocation for Intelligent Transporation Systems (ITS) can be used to locate vehicles of any type in underground parkings, keep a platoon of trucks in formation or create geo-fences, that is, sending an alert if an object moves outside a defined area, like a bicycle being stolen. Routing of heavy vehicles within an industrial setting is another possibility.This work was partially supported by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00.Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Hernández-Orallo, E. (2019). Indoor vehicles geolocalization using LoRaWAN. Future Internet. 11(6):1-15. https://doi.org/10.3390/fi11060124S11511

Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19

[EN] Detecting and controlling the diffusion of infectious diseases such as COVID-19 is crucial to managing epidemics. One common measure taken to contain or reduce diffusion is to detect infected individuals and trace their prior contacts so as to then selectively isolate any individuals likely to have been infected. These prior contacts can be traced using mobile devices such as smartphones or smartwatches, which can continuously collect the location and contacts of their owners by using their embedded localisation and communications technologies, such as GPS, Cellular networks, Wi-Fi, and Bluetooth. This paper evaluates the effectiveness of these technologies and determines the impact of contact tracing precision on the spread and control of infectious diseases. To this end, we have created an epidemic model that we used to evaluate the efficiency and cost (number of people quarantined) of the measures to be taken, depending on the smartphone contact tracing technologies used. Our results show that in order to be effective for the COVID-19 disease, the contact tracing technology must be precise, contacts must be traced quickly, and a significant percentage of the population must use the smartphone contact tracing application. These strict requirements make smartphone-based contact tracing rather ineffective at containing the spread of the infection during the first outbreak of the virus. However, considering a second wave, where a portion of the population will have gained immunity, or in combination with some other more lenient measures, smartphone-based contact tracing could be extremely useful.This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096384-B-I00.Hernández-Orallo, E.; Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J. (2020). Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19. IEEE Access. 8:99083-99097. https://doi.org/10.1109/ACCESS.2020.2998042S9908399097

A Representative and Accurate Characterization of Inter-contact Times in Mobile Opportunistic Networks

© ACM 2013. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MSWiM '13 Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems; http://dx.doi.org/10.1145/2507924.2507936.A representative characterisation of inter-contact times between nodes is essential for the performance evaluation of mobile networks. The most common characterization of inter-contact times is based on the study of the aggregate distribution of contacts between individual pairs of nodes. The problem with this aggregate distribution is that it is not always representative of the individual pair distributions, especially in the short term and when the number of nodes in the network is high. Thus, deriving results from this characterisation, can lead to inaccurate performance evaluations results. In this paper, we propose and evaluate two new methods for characterising the inter-contact times distribution in mobile networks. We prove that these characterizations have a higher representativeness, thereby improving the accuracy of the derived performance results. For evaluating the precision of the different characterizations we use the epidemic routing protocol, which has an analytical performance expression that is based on a contact rate λ. We derive from each of the characterizations the corresponding λ values. Then, we compare the results obtained using the analytical expression with simulation results using both synthetic and real contact traces. It is shown that the new characterization methods are very accurate, even for reduced contact traces and high number of nodes.This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01.Hernández Orallo, E.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). A Representative and Accurate Characterization of Inter-contact Times in Mobile Opportunistic Networks. ACM. https://doi.org/10.1145/2507924.2507936

Does AI Qualify for the Job?: A Bidirectional Model Mapping Labour and AI Intensities

[EN] In this paper we present a setting for examining the relation between the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labour and AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples.This material is based upon work supported by the EU (FEDER), and the Spanish MINECO under grant RTI2018-094403-B-C3, the Generalitat Valenciana PROMETEO/2019/098. F. Martínez-Plumed was also supported by INCIBE (Ayudas para la excelencia de los equipos de investigación avanzada en ciberseguridad), the European Commission (JRC) HUMAINT project (CT-EX2018D335821-101), and UPV (PAID-06-18). J. H-Orallo is also funded by an FLI grant RFP2-152.Martínez-Plumed, F.; Tolan, S.; Pesole, A.; Hernández-Orallo, J.; Fernández-Macías, E.; Gómez, E. (2020). Does AI Qualify for the Job?: A Bidirectional Model Mapping Labour and AI Intensities. Association for Computing Machinery (ACM). 94-100. https://doi.org/10.1145/3375627.3375831S9410