Utilizing Mobility Tracking to Identify Hotspots for Contagious Disease Spread A Case Study of UNITEN Students Using Google Map Data
Main Article Content
Keywords
Hotspot, HDBSCAN, infection disease, COVID-19
Abstract
A significant global health problem nowadays is the incidence of serious infectious illnesses. An extraordinary humanitarian crisis has been brought on by the current COVID-19 pandemic, which has spread around the world. The spread of new viruses has put established healthcare institutions under tremendous strain and created a number of pressing problems. It is important to predict the future movement and pattern of the illness in order to decrease infectious instances and maximize recovered cases. This research paper aims to utilize mobility tracking as a means to identify hotspots for contagious disease spread. The study focuses on collecting and analyzing mobility data from UNITEN students using Google Map data over a period of two weeks. The paper describes the data collection process, data pre-processing steps, and the application of the HDBSCAN algorithm for hotspot clustering. The results demonstrate the effectiveness of HDBSCAN in identifying hotspots based on the mobility data. The findings highlight the potential of mobility tracking for disease surveillance and provide insights for public health interventions and preventive measures.
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References
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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
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CDC [US Centers for Disease Control and Prevention]. (2018) Managing HIV and Hepatitis C Outbreaks among People Who Inject Drugs - A Guide for State and Local Health Departments. Available from https://www.cdc.gov/hiv/pdf/programresources/guidance/cluster-outbreak/cdc-hiv-hcv-pwid-guide.pdf
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
Devi, S., Nagaraja, K. V., Thanuja, L., Reddy, M. V., & Ramakrishna, S. (2022). Finite element analysis over transmission region of coronavirus in CFD analysis for the respiratory cough droplets, Ain Shams Engineering Journal, 13(6), 101766. https://doi.org/10.1016/j.asej.2022.101766
Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (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
Google. (2020). COVID-19 Community Mobility Reports, 2020. Available from https://www.google.com/covid19/mobility
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
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
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
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
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
Pace, R. K., Barry, R., & Sirmans, C. F. (1998). Spatial Statistics and Real Estate. The Journal of Real Estate Finance and Economics, 17, 5–13. https://doi.org/10.1023/A:1007783811760
Ping, J. L., Green, C. J., Zartman, R. E., & Bronson, K. F. (2004). Exploring spatial dependence of cotton yield using global and local autocorrelation statistics, Field Crops Research, 89(2–3), 219–236. https://doi.org/10.1016/j.fcr.2004.02.009
Porta, M. (Ed.). (2014). A Dictionary of Epidemiology. Oxford University Press.
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
Sardianos, C., Varlamis, I., & Bouras, G. (2018). Extracting User Habits from Google Maps History Logs. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, 2018, 690–697, https://doi.org/10.1109/ASONAM.2018.8508442
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 of the United States of America, 117(52), 32883–32890. https://doi.org/10.1073/pnas.2012326117
Setti, L., Passarini, F., De Gennaro, G., Barbieri, P., Perrone, M. G., Borelli, M., Palmisani, J., Di Gilio, A., Piscitelli, P., & Miani, A. (2020). Airborne Transmission Route of COVID-19: Why 2 Meters/6 Feet of Inter-Personal Distance Could Not Be Enough. International Journal of Environmental Research and Public Health, 17(8), 2932. https://doi.org/10.3390/ijerph17082932
Si, Y. L., Debba, P., Skidmore, A. K., Toxopeus, A. G., & Li, L. (2008). Spatial and temporal patterns of global H5N1 outbreaks. In ISPRS 2008: Proceedings of the XXI congress: Silk road for information from imagery: the International Society for Photogrammetry and Remote Sensing, 3-11 July, Beijing, China. Comm. II, WG II/1. Beijing: ISPRS, 2008. 69–74. International Society for Photogrammetry and Remote Sensing (ISPRS). http://www.isprs.org/proceedings/XXXVII/congress/2_pdf/1_WG-II-1/12.pdf
Soures, N., Chambers, D., Carmichael, Z., Daram, A., Shah, D. P., Clark, K., Potter, L., & Kudithipudu, 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
Tsai, P. J., Lin, M. L., Chu, C. M., & Perng, C. H. (2009). Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006. BMC Public Health, 9, 464. https://doi.org/10.1186/1471-2458-9-464
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
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
Yeshiwondim, A. K., Gopal, S., Hailemariam, A. T., Dengela, D. O., & Patel, H. P. (2009). Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia. International Journal of Health Geographics, 8, 5. https://doi.org/10.1186/1476-072X-8-5
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
Zarei, M., Rahimi, K., Hassanzadeh, K., Abdi, M., Hosseini, V., Fathi, A., & Kakaei, K. (2021). From the environment to the cells: An overview on pivotal factors which affect spreading and infection in COVID-19 pandemic. Environmental Research, 201, 111555. https://doi.org/10.1016/j.envres.2021.111555
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Sep 30, 2023
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How to Cite
Yaw, M. W., Krishnan, P. S., Chen, C. P., & Tiong, S. K. (2023). Utilizing Mobility Tracking to Identify Hotspots for Contagious Disease Spread: A Case Study of UNITEN Students Using Google Map Data. Journal of Telecommunications and the Digital Economy, 11(3), 163–179. https://doi.org/10.18080/jtde.v11n3.775
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Telecommunications
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Author Biographies
Yaw Mei Wyin, Universiti Tenaga Nasional, Kajang, Malaysia
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
Prajindra Sankar Krishnan, Universiti Tenaga Nasional, Kajang, Malaysia
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
Chen Chai Phing, Universiti Tenaga Nasional, Kajang, Malaysia
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
Tiong Sieh Kiong, Universiti Tenaga Nasional, Kajang, Malaysia
Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia
