COVID-19 RISK ASSESSMENT IN PUBLIC TRANSPORT USING AMBIENT SENSOR DATA AND WIRELESS COMMUNICATIONS
Covid-19 causes one of the most alarming global health and economic crises in modern times. Countries around the world establish different preventing measures to stop or control Covid-19 spread. The goal of this paper is to present methods for the evaluation of indoor air quality in public transport to assess the risk of contracting Covid-19. The first part of the paper involves investigating the relationship between Covid-19 and various factors affecting indoor air quality. The focus of this paper relies on exploring existing methods to estimate the number of occupants in public transport. It is known that increased occupancy rate increases the possibility of contamination as well as indoor carbon dioxide concentration. Wireless data collection schemes will be defined that can collect data from public transportation. Collected data are envisioned to be stored in the cloud for data analytics. We will present novel methods to analyze the collected data by considering the historical data and estimate the virus contagion risk level for each public transportation vehicle in service. The methodology is expected to be applicable for other airborne diseases as well. Real-time risk levels of public transportation vehicles will be available through a mobile application so that people can choose their mode of transportation accordingly.
Ahmed, N., Ghose, A., Agrawal, A.K., Bhaumik, C., Chandel, V. & Kumar, A. 2015. Smartevactrak: A people counting and coarse-level localization solution for efficient evacuation of large buildings. In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 372-377. IEEE.
Aziz, K. E., Merad, D., Fertil, B., & Thome, N. 2011. Pedestrian head detection and tracking using skeleton graph for people counting in crowded environments. Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011, pp. 516-519.
Baker, M. G., Thornley, C. N., Mills, C., Roberts, S., Perera, S., Peters, J., ... & Wilson, N. 2010. Transmission of pandemic A/H1N1 2009 influenza on passenger aircraft: retrospective cohort study. Bmj, 340, c2424.
Chan, A. B., Liang, Z. S. J., & Vasconcelos, N. 2008. Privacy preserving crowd monitoring: Counting people without people models or tracking. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR.
Cho, S. Y., Chow, T. W. S., & Leung, C. T. 1999. A neural-based crowd estimation by hybrid global learning algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(4), pp. 535-541.
Choi, J. W., Yim, D. H. & Cho, S. H. 2017. People counting based on an IR-UWB radar sensor. IEEE Sensors Journal, 17(17), pp. 5717-5727.
Chow, T. W. S., Yam, J. Y. F., & Cho, S. Y. 1999. Fast training algorithm for feedforward neural networks: Application to crowd estimation at underground stations. Artificial Intelligence in Engineering, 13(3), pp. 301-307.
Del Pizzo, L., Foggia, P., Greco, A., Percannella, G., & Vento, M. 2016. Counting people by RGB or depth overhead cameras. Pattern Recognition Letters, 81, pp. 41-50.
Fattorini, D., & Regoli, F. 2020. Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environmental Pollution, p. 114732.
Fears, A. C., Klimstra, W. B., Duprex, P., Hartman, A., Weaver, S. C., Plante, K. S., Mirchandani, D., Plante, J. A., Aguilar, P. V., Fernández, D., & Nalca, A. 2020. Persistence of severe acute respiratory syndrome coronavirus 2 in aerosol suspensions. Emerging infectious diseases, 26(9), p. 2168.
Gorbalenya, A. E., Baker, S. C., Baric, R. S., de Groot, R. J., Drosten, C., Gulyaeva, A. A., Haagmans, B. L., Lauber, C., Leontovich, A. M., Neuman, B. W., & Penzar, D. 2020. The species severe acute respiratory syndrome related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 5: pp. 536-544.
Haritaoglu, I., Harwood, D., & Davis, L. S. 2000. W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), pp. 809-830.
Handte, M., Iqbal, M. U., Wagner, S., Apolinarski, W., Marrón, P. J., Navarro, E. M. M., Martinez, S., Barthelemy, S. I., & Fernández, M. G. 2014. Crowd Density Estimation for Public Transport Vehicles. In EDBT/ICDT Workshops, pp. 315-322.
Hnat, T. W., Griffiths, E., Dawson, R., & Whitehouse, K. 2012. Doorjamb: unobtrusive room-level tracking of people in homes using doorway sensors. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, pp. 309-322.
Howerton, J. M., & Schenck, B.L. 2020. The Deployment of a LoRaWAN-Based IoT Air Quality Sensor Network for Public Good. In 2020 Systems and Information Engineering Design Symposium (SIEDS), pp. 1-6. IEEE.
Jain, S., & Madamopoulos, N. 2016. Ahorrar: Indoor occupancy counting to enable smart energy efficient office buildings. In 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom), pp. 469-476. IEEE.
Jin, M., Bekiaris-Liberis, N., Weekly, K., Spanos, C., & Bayen, A. 2015. Sensing by proxy: Occupancy detection based on indoor CO2 concentration. UBICOMM 2015, 14.
Kamal, M., Aljohani, A., & Alanazi, E. 2020. IoT meets COVID-19: Status, Challenges, and Opportunities. arXiv preprint arXiv:2007.12268.
Kettnaker, V., & Zabih, R. 1999. Counting people from multiple cameras. International Conference on Multimedia Computing and Systems -Proceedings, 2, pp. 267-271.
Kouyoumdjieva, S. T., Danielis, P., & Karlsson, G. 2019. Survey of non-image based approaches for counting people. IEEE Communications Surveys & Tutorials.
Kostakos, V., Camacho, T., & Mantero, C. 2010. Wireless detection of end-to-end passenger trips on public transport buses. In 13th International IEEE Conference on Intelligent Transportation Systems, pp. 1795-1800. IEEE.
Li, H., Chan, E. C., Guo, X., Xiao, J., Wu, K., & Ni, L. M. 2015. Wi-counter: smartphone-based people counter using crowdsourced wi-fi signal data. IEEE Transactions on Human-Machine Systems, 45(4), pp. 442-452.
Li, H., Lu, H., Chen, X., Chen, G., Chen, K., & Shou, L. 2016. Vita: A versatile toolkit for generating indoor mobility data for real-world buildings. Proceedings of the VLDB Endowment, 9(13), pp.1453-1456.
Li, Y., Qian, H., Hang, J., Chen, X., Hong, L., Liang, P., Li, J., Xiao, S., Wei, J., Liu, L., & Kang, M., 2020. Evidence for probable aerosol transmission of SARS-CoV-2 in a poorly ventilated restaurant. medRxiv.
Liu, L., Li, Y., Nielsen, P.V., Wei, J., & Jensen, R. L. 2017. Short‐range airborne transmission of expiratory droplets between two people. Indoor Air, 27(2), pp. 452-462.
Morawska, L., Tang, J. W., Bahnfleth, W., Bluyssen, P. M., Boerstra, A., Buonanno, G., Cao, J., Dancer, S., Floto, A., Franchimon, F., & Haworth, C. 2020. How can airborne transmission of COVID-19 indoors be minimised?. Environment international, 142, p. 105832.
Nakatsuka, M., Iwatani, H., & Katto, J. 2008. A study on passive crowd density estimation using wireless sensors. In The 4th Intl. Conf. on Mobile Computing and Ubiquitous Networking (ICMU 2008).
Nasir, Z. A., Campos, L. C., Christie, N. & Colbeck, I. 2016. Airborne biological hazards and urban transport infrastructure: current challenges and future directions. Environmental Science and Pollution Research, 23(15), pp. 15757-15766.
Ndiaye, M., Oyewobi, S. S., Abu-Mahfouz, A. M., Hancke, G. P., Kurien, A. M., & Djouani, K. 2020. IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution. IEEE Access, 8, pp. 186821-186839.
Nitti, M., Pinna, F., Pintor, L., Pilloni, V., & Barabino, B. 2020. iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System. Energies, 13(6), p.1446.
Niu, R., & Varshney, P. K. 2006. Target location estimation in sensor networks with quantized data. IEEE Transactions on Signal Processing, 54(12), pp. 4519-4528.
Pan, S., Mirshekari, M., Zhang, P., & Noh, H. Y., 2016. Occupant traffic estimation through structural vibration sensing. In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016., 9803, p. 980306. International Society for Optics and Photonics.
Pestre, V., Morel, B., Encrenaz, N., Brunon, A., Lucht, F., Pozzetto, B., & Berthelot, P. 2012. Transmission by super-spreading event of pandemic A/H1N1 2009 influenza during road and train travel. Scandinavian journal of infectious diseases, 44(3), pp. 225-227.
Qian, H., & Zheng, X. 2018. Ventilation control for airborne transmission of human exhaled bio-aerosols in buildings. Journal of thoracic disease, 10(Suppl 19), p. S2295.
Riediker, M., & Tsai, D. H. 2020. Estimation of SARS-CoV-2 emissions from non-symptomatic cases. medRxiv.
Regazzoni, C. S., & Tesei, A. 1996. Distributed data fusion for real-time crowding estimation. Signal Processing, 53(1), pp. 47-63.
Sacchi, C., Gera, G., Marcenaro, L., & Regazzoni, C. S. 2001. Advanced image-processing tools for counting people in tourist site-monitoring applications. Signal Processing, 81(5), pp. 1017-1040.
Saeed, N., Bader, A., Al-Naffouri, T. Y., & Alouini, M. S. 2020. When Wireless Communication Faces COVID-19: Combating the Pandemic and Saving the Economy. arXiv preprint arXiv:2005.06637.
Santarpia, J. L., Rivera, D. N., Herrera, V., Morwitzer, M. J., Creager, H., Santarpia, G. W., Crown, K. K., Brett-Major, D., Schnaubelt, E., Broadhurst, M. J., & Lawler, J. V. 2020. Transmission potential of SARS-CoV-2 in viral shedding observed at the University of Nebraska Medical Center. MedRxIV.
Schlögl, T., Wachmann, B., Kropatsch, W., & Bischof, H. 1832. Evaluation of People Counting Systems. Image Processing, August.
Shen, Y., Li, C., Dong, H., Wang, Z., Martinez, L., Sun, Z., Handel, A., Chen, Z., Chen, E., Ebell, M., & Wang, F. 2020. Airborne transmission of COVID-19: epidemiologic evidence from two outbreak investigations.
Shih, O., & Rowe, A. 2015. Occupancy estimation using ultrasonic chirps. In Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, pp. 149-158.
Tang, X., Xiao, B., & Li, K. 2018. Indoor crowd density estimation through mobile smartphone wi-fi probes. IEEE transactions on systems, man, and cybernetics: systems.
Tatem, A. J., Rogers, D. J., & Hay, S. I. 2006. Global transport networks and infectious disease spread. Advances in parasitology, 62, pp. 293-343.
Tirachini, A., & Cats, O. 2020. COVID-19 and public transportation: Current assessment, prospects, and research needs. Journal of Public Transportation, 22(1), p.1.
Vasco Dantas dos Reis, J. 2014. Image Descriptors for Counting People with Uncalibrated Cameras.
Van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., & Lloyd-Smith, J. O. 2020. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England Journal of Medicine, 382(16), pp.1564-1567.
Wielechowski, M., Czech, K., & Grzęda, Ł. 2020. Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic. Economies, 8(4), p.78.
Xiao, S., Li, Y., Sung, M., Wei, J., & Yang, Z. 2018. A study of the probable transmission routes of MERS‐CoV during the first hospital outbreak in the Republic of Korea. Indoor Air, 28(1), pp. 51-63.
Xu, C., Firner, B., Moore, R. S., Zhang, Y., Trappe, W., Howard, R., Zhang, F., & An, N. 2013. SCPL: Indoor device-free multi-subject counting and localization using radio signal strength. In Proceedings of the 12th international conference on Information processing in sensor networks, pp. 79-90.
Yu, I. T., Li, Y., Wong, T. W., Tam, W., Chan, A. T., Lee, J. H., Leung, D. Y., & Ho, T. 2004. Evidence of airborne transmission of the severe acute respiratory syndrome virus. New England Journal of Medicine, 350(17), pp. 1731-1739.