COVIDSensing: Social Sensing strategy for the management of the COVID-19 crisis

[EN] The management of the COVID-19 pandemic has been shown to be critical for reducing its dramatic effects. Social sensing can analyse user-contributed data posted daily in social-media services, where participants are seen as Social Sensors. Individually, social sensors may provide noisy information. However, collectively, such opinion holders constitute a large critical mass dispersed everywhere and with an immediate capacity for information transfer. The main goal of this article is to present a novel methodological tool based on social sensing, called COVIDSensing. In particular, this application serves to provide actionable information in real time for the management of the socio-economic and health crisis caused by COVID-19. This tool dynamically identifies socio-economic problems of general interest through the analysis of people¿s opinions on social networks. Moreover, it tracks and predicts the evolution of the COVID-19 pandemic based on epidemiological figures together with the social perceptions towards the disease. This article presents the case study of Spain to illustrate the tool.This work is derived from R&D project RTI2018-096384-B-I00, as well as the Ramon y Cajal Grant RYC2018-025580-I, funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe, by the Spanish Agencia Estatal de Investigación (grant number PID2020- 112827GB-I00/ AEI/10.13039/501100011033), and by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Proyectos AICO/2020, Spain, under Grant AICO/2020/302.Sepúlveda, A.; Periñán-Pascual, C.; Muñoz, A.; Martínez-España, R.; Hernández-Orallo, E.; Cecilia-Canales, JM. (2021). COVIDSensing: Social Sensing strategy for the management of the COVID-19 crisis. Electronics. 10(24):1-17. https://doi.org/10.3390/electronics10243157S117102

Reconciling Big Data and Thick Data to Advance the New Urban Science and Smart City Governance

Amid growing enthusiasm for a ”new urban science” and ”smart city” approaches to urban management, ”big data” is expected to create radical new opportunities for urban research and practice. Meanwhile, anthropologists, sociologists, and human geographers, among others, generate highly contextualized and nuanced data, sometimes referred to as ‘thick data,’ that can potentially complement, refine and calibrate big data analytics while generating new interpretations of the city through diverse forms of reasoning. While researchers in a range of fields have begun to consider such questions, scholars of urban affairs have not yet engaged in these discussions. The article explores how ethnographic research could be reconciled with big data-driven inquiry into urban phenomena. We orient our critical reflections around an illustrative example: road safety in Mexico City. We argue that big and thick data can be reconciled in and through three stages of the research process: research formulation, data collection and analysis, and research output and knowledge representation

Social Media Text Processing and Semantic Analysis for Smart Cities

With the rise of Social Media, people obtain and share information almost instantly on a 24/7 basis. Many research areas have tried to gain valuable insights from these large volumes of freely available user generated content. With the goal of extracting knowledge from social media streams that might be useful in the context of intelligent transportation systems and smart cities, we designed and developed a framework that provides functionalities for parallel collection of geo-located tweets from multiple pre-defined bounding boxes (cities or regions), including filtering of non-complying tweets, text pre-processing for Portuguese and English language, topic modeling, and transportation-specific text classifiers, as well as, aggregation and data visualization. We performed an exploratory data analysis of geo-located tweets in 5 different cities: Rio de Janeiro, S\~ao Paulo, New York City, London and Melbourne, comprising a total of more than 43 million tweets in a period of 3 months. Furthermore, we performed a large scale topic modelling comparison between Rio de Janeiro and S\~ao Paulo. Interestingly, most of the topics are shared between both cities which despite being in the same country are considered very different regarding population, economy and lifestyle. We take advantage of recent developments in word embeddings and train such representations from the collections of geo-located tweets. We then use a combination of bag-of-embeddings and traditional bag-of-words to train travel-related classifiers in both Portuguese and English to filter travel-related content from non-related. We created specific gold-standard data to perform empirical evaluation of the resulting classifiers. Results are in line with research work in other application areas by showing the robustness of using word embeddings to learn word similarities that bag-of-words is not able to capture

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. 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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. 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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

Social Media Enabled Human Sensing for Smart Cities

Smart city initiatives rely on real-time measurements and data collected by a large number of heterogenous physical sensors deployed throughout a city. Physical sensors, however, are fraught with interoperability, dependability, management and political challenges. Furthermore, these sensors are unable to sense the opinions and emotional reactions of citizens that invariably impact smart city initiatives. Yet everyday, millions of dwellers and visitors of a city share their observations, thoughts, feelings andexperiences, or in other words, their perceptions about theircity through social media updates. This paper reasons why “human sensors”, namely, citizens that share information about their surroundings via social media can supplement, complement, or even replace the information measured by physical sensors. We present a methodology based on probabilistic language modeling to extract and visualize such perceptions that may be relevant to smart cities from social media updates. Using more than six million geo-tagged tweets collected over regions that feature widely varying geographical, social, cultural and political characteristics and tweet densities, we illustrate the potential of social media enabled human sensing to address diverse smart city challenges

Social Media Enabled Human Sensing for Smart Cities

Smart city initiatives rely on real-time measurements and data collected by a large number of heterogenous physical sensors deployed throughout a city. Physical sensors, however, are fraught with interoperability, dependability, management and political challenges. Furthermore, these sensors are unable to sense the opinions and emotional reactions of citizens that invariably impact smart city initiatives. Yet everyday, millions of dwellers and visitors of a city share their observations, thoughts, feelings andexperiences, or in other words, their perceptions about theircity through social media updates. This paper reasons why “human sensors”, namely, citizens that share information about their surroundings via social media can supplement, complement, or even replace the information measured by physical sensors. We present a methodology based on probabilistic language modeling to extract and visualize such perceptions that may be relevant to smart cities from social media updates. Using more than six million geo-tagged tweets collected over regions that feature widely varying geographical, social, cultural and political characteristics and tweet densities, we illustrate the potential of social media enabled human sensing to address diverse smart city challenges