Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data
Main Article Content
Keywords
contact networks, human mobility simulation, epidemic control policy
Abstract
According to mobility data that records mobility traffic using location trackers on mobile phones, the COVID-19 epidemic and the adoption of social distance policies have drastically altered people’s visiting patterns. However, rather than the volume of visitors, the transmission is controlled by the frequency and length of concurrent occupation at particular places. Therefore, it is essential to comprehend how people interact in various settings in order to focus legislation, guide contact tracking, and educate prevention initiatives. This study suggests an effective method for reducing the virus’s propagation among university students enrolled on-campus by creating a self-developed Google History Location Extractor and Indicator software based on actual data on people’s movements. The platform enables academics and policymakers to model the results of human mobility and the epidemic condition under various epidemic control measures and assess the potential for future advancements in the epidemic’s spread. It provides tools for identifying prospective contacts, analyzing individual infection risks, and reviewing the success of campus regulations. By more precisely focusing on probable virus carriers during the screening process, the suggested multi-functional platform makes it easier to decide on epidemic control measures, ultimately helping to manage and avoid future outbreaks.
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References
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Muller, S. A., Balmer, M., Neumann, A., & Nagel, K. (2020). Mobility traces and spreading of COVID-19. medRxiv preprint. https://doi.org/10.1101/2020.03.27.20045302
Pan, Y., Darzi, A., Kabiri, A., Zhao, G., Luo, W., Xiong, C., Zhang, L. (2020). Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States. Scientific Reports, 10(1):1–9. https://doi.org/10.1038/s41598-020-77751-2
Parshani, R., Carmi, S., & Havlin, S. (2010). Epidemic threshold for the susceptible-infectious-susceptible model on random networks. Physical Review Letters, 104(25), 258701. https://doi.org/10.1103/PhysRevLett.104.258701
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Ruktanonchai, N. W., Ruktanonchai, C. W., Floyd, J. R. & Tatem, A. J. (2018). Using Google Location History data to quantify fine-scale human mobility. International Journal of Health Geographics, 17, 28. https://doi.org/10.1186/s12942-018-0150-z
Schlosser, F., Maier, B. F., Jack, O., Hinrichs, D., Zachariae, A., & Brockmann, D. (2020). COVID-19 lockdown induces disease-mitigating structural changes in mobility networks. Proceedings of the National Academy of Sciences (USA), 117(52), 32883–32890. https://doi.org/10.1073/pnas.2012326117
Soures, N., Chambers, D., Carmichael, Z., Daram, A., Shah, D. P., Clark, K., Potter, L., & Kudithipudi, D. (2020). SIRNet: Understanding social distancing measures with hybrid neural network model for COVID-19 infectious spread. arXiv preprint, 200410376. https://doi.org/10.48550/arXiv.2004.10376
Venkatramanan, S., Sadilek, A., Fadikar, A., Barrett, C. L., Biggerstaff, M., Chen, J., Dotiwalla, X., Eastham, P., Gipson, B., Higdon, D., Kucuktunc, O., Lieber, A., Lewis, B. L., Reynolds, Z., Vullikanti, A. K., Wang, L., & Marathe, M. (2021). Forecasting influenza activity using machine-learned mobility map. Nature Communications, 12, 726. https://doi.org/10.1038/s41467-021-21018-5
Wang, Y., Wang, Y., Chen, Y., & Qin, Q. (2020). Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. Journal of Medical Virology, 92(6), 568–576. https://doi.org/10.1002/jmv.25748
Weill, J. A., Stigler, M., Deschenes, O., & Springborn, M. R. (2020). Social distancing responses to COVID-19 emergency declarations strongly differentiated by income. Proceedings of the National Academy of Sciences, 117(33), 19658–19660. https://doi.org/10.1073/pnas.2009412117
Wu, J. T., Leung, K., & Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet, 395(10225), 689–697. https://doi.org/10.1016/S0140-6736(20)30260-9
Yi, H., Ng, S. T., Farwin, A., Pei Ting Low, A., Chang, C. M., & Lim, J. (2021). Health equity considerations in COVID-19: geospatial network analysis of the COVID-19 outbreak in the migrant population in Singapore. Journal of Travel Medicine, 28(2), taaa159. https://doi.org/10.1093/jtm/taaa159
Afzal, S., Maciejewski, R., & Ebert, D. S. (2011). Visual analytics decision support environment for epidemic modeling and response evaluation. 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), Providence, RI, USA, pp. 191–200. https://doi.org/10.1109/VAST.2011.6102457
Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., & Leskovec, J. (2021). Mobility network models of COVID-19 explain inequities and inform reopening. Nature, 589, 82–87. https://doi.org/10.1038/s41586-020-2923-3
Chiang, W. H., Liu, X., & Mohler, G. (2022). Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates. International Journal of Forecasting, 38(2), 505–520. https://doi.org/10.1016/j.ijforecast.2021.07.001
Dave, D., McNichols, D., & Sabia, J. J. (2021). The Contagion Externality of a Superspreading Event: The Sturgis Motorcycle Rally and COVID-19. Southern Economic Journal, 87(3), 769–807. https://doi.org/10.1002/soej.12475
Dunne, C., Muller, M. J., Perra, N., & Martino, M. (2015). VoroGraph: Visualization Tools for Epidemic Analysis. Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 255–258. https://doi.org/10.1145/2702613.2725459
Guo, D. (2007). Visual analytics of spatial interaction patterns for pandemic decision support. International Journal of Geographical Information Science, 21(8), 859–877, https://doi.org/10.1080/13658810701349037
Khalel, A. M. H. (2010). Position Location Techniques in Wireless Communication Systems (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4796
Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., and Andrea Rinaldo. (2020). Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proceedings of the National Academy of Sciences, USA, 117(19), 10484–10491. https://doi.org/10.1073/pnas.2004978117
Ghayvat, H., Awais, M., Gope, P., Pandya, S., & Majumdar, S. (2021). Recognizing Suspect and Predicting the Spread of Contagion Based on Mobile Phone Location Data (COUNTERACT): A system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and artificial intelligence. Sustainable Cities and Society, 69, 102798. https://doi.org/10.1016/j.scs.2021.102798
Google. (2020). Google COVID-19 Community Mobility Reports, 2020. Available from https://www.google.com/covid19/mobility
Gupta, R., Bedi, M., Goyal, P., Wadhera, S., & Verma, V. (2020). Analysis of COVID-19 tracking tool in India: Case study of Aarogya Setu mobile application. Digital Government: Research and Practice, 1(4), 28. https://doi.org/10.1145/3416088
Kantor, J. (2021). 3.9 modeling and control of a campus outbreak of Coronavirus Covid-19. Retrieved March 13, 2023, from https://jckantor.github.io/CBE30338/03.09-COVID-19.html
Kiang, M. V., Santillana, M., Chen, J. T., Onnela, J.-P., Krieger, N., Engø-Monsen, K., Ekapirat, N., Areechokchai, D., Prempree, P., Maude, R. J., & Buckee, C. O. (2021). Incorporating human mobility data improves forecasts of Dengue fever in Thailand. Scientific Reports, 11, 923. https://doi.org/10.1038/s41598-020-79438-0
Klise, K., Beyeler, W., Finley, P., & Makvandi, M. (2021). Analysis of mobility data to build contact networks for COVID-19. PLoS One, 16(4), e0249726. https://doi.org/10.1371/journal.pone.0249726
Kraemer, M. U. G., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., Open COVID-19 Data Working Group, du Plessis, L., Faria, N. R., Li, R., Hanage, W. P., Brownstein, J. S., Layan, M., Vespignani, A., Tian, H., Dye, C., Pybus, O. G., & Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493–497. https://doi.org/10.1126/science.abb4218
Kumar, N., Oke, J. & Nahmias-Biran, Bh. (2021). Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas. Scientific Reports, 11, 22665. https://doi.org/10.1038/s41598-021-01522-w
Lasry, A., Kidder, D., Hast, M., Poovey, J., Sunshine, G., Winglee. K., et al. (2020). Timing of community mitigation and changes in reported COVID-19 and community mobility–four US metropolitan areas, February 26–April 1, 2020. Morbidity and Mortality Weekly Report, 69(15), 451–457. https://doi.org/10.15585/mmwr.mm6915e2
Maheshwari, P., & Albert, R. (2020). Network model and analysis of the spread of Covid-19 with social distancing. Applied Network Science, 5, 100. https://doi.org/10.1007/s41109-020-00344-5
McMinn, S., & Talbot, R. (2020). Mobile Phone Data Show More Americans Are Leaving Their Homes, Despite Orders. NPR, The Coronavirus Crisis. Available from https://www.npr.org/2020/05/01/849161820/mobile-phone-data-showmore-americans-are-leaving-their-homes-despite-orders
Muller, S. A., Balmer, M., Neumann, A., & Nagel, K. (2020). Mobility traces and spreading of COVID-19. medRxiv preprint. https://doi.org/10.1101/2020.03.27.20045302
Pan, Y., Darzi, A., Kabiri, A., Zhao, G., Luo, W., Xiong, C., Zhang, L. (2020). Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States. Scientific Reports, 10(1):1–9. https://doi.org/10.1038/s41598-020-77751-2
Parshani, R., Carmi, S., & Havlin, S. (2010). Epidemic threshold for the susceptible-infectious-susceptible model on random networks. Physical Review Letters, 104(25), 258701. https://doi.org/10.1103/PhysRevLett.104.258701
Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. E., Davies, N., Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Jit, M., & Klepac, P. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. Lancet Public Health, 5(5), E261–E270. https://doi.org/10.1016/S2468-2667(20)30073-6
Rechtin, M., Feldman, V., Klare, S., Riddle, N., & Sharma, R. (2020). Modeling and Simulation of COVID-19 Pandemic for Cincinnati Tri-State Area. arXiv preprint: 200606021. https://doi.org/10.48550/arXiv.2006.06021
Ruktanonchai, N. W., Ruktanonchai, C. W., Floyd, J. R. & Tatem, A. J. (2018). Using Google Location History data to quantify fine-scale human mobility. International Journal of Health Geographics, 17, 28. https://doi.org/10.1186/s12942-018-0150-z
Schlosser, F., Maier, B. F., Jack, O., Hinrichs, D., Zachariae, A., & Brockmann, D. (2020). COVID-19 lockdown induces disease-mitigating structural changes in mobility networks. Proceedings of the National Academy of Sciences (USA), 117(52), 32883–32890. https://doi.org/10.1073/pnas.2012326117
Soures, N., Chambers, D., Carmichael, Z., Daram, A., Shah, D. P., Clark, K., Potter, L., & Kudithipudi, D. (2020). SIRNet: Understanding social distancing measures with hybrid neural network model for COVID-19 infectious spread. arXiv preprint, 200410376. https://doi.org/10.48550/arXiv.2004.10376
Venkatramanan, S., Sadilek, A., Fadikar, A., Barrett, C. L., Biggerstaff, M., Chen, J., Dotiwalla, X., Eastham, P., Gipson, B., Higdon, D., Kucuktunc, O., Lieber, A., Lewis, B. L., Reynolds, Z., Vullikanti, A. K., Wang, L., & Marathe, M. (2021). Forecasting influenza activity using machine-learned mobility map. Nature Communications, 12, 726. https://doi.org/10.1038/s41467-021-21018-5
Wang, Y., Wang, Y., Chen, Y., & Qin, Q. (2020). Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. Journal of Medical Virology, 92(6), 568–576. https://doi.org/10.1002/jmv.25748
Weill, J. A., Stigler, M., Deschenes, O., & Springborn, M. R. (2020). Social distancing responses to COVID-19 emergency declarations strongly differentiated by income. Proceedings of the National Academy of Sciences, 117(33), 19658–19660. https://doi.org/10.1073/pnas.2009412117
Wu, J. T., Leung, K., & Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet, 395(10225), 689–697. https://doi.org/10.1016/S0140-6736(20)30260-9
Yi, H., Ng, S. T., Farwin, A., Pei Ting Low, A., Chang, C. M., & Lim, J. (2021). Health equity considerations in COVID-19: geospatial network analysis of the COVID-19 outbreak in the migrant population in Singapore. Journal of Travel Medicine, 28(2), taaa159. https://doi.org/10.1093/jtm/taaa159
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Published
Sep 30, 2023
Article Details
How to Cite
Yaw, M. W., Krishnan, P. S., Chen, C. P., & Tiong , S. K. (2023). Big Data Analytics in Tracking COVID-19 Spread Utilizing Google Location Data. Journal of Telecommunications and the Digital Economy, 11(3), 143–162. https://doi.org/10.18080/jtde.v11n3.771
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Telecommunications
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Author Biographies
Yaw Mei Wyin, UNITEN
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
Prajindra Sankar Krishnan, UNITEN
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
Chen Chai Phing, UNITEN
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
Tiong Sieh Kiong, UNITEN
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
