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EpiDesktop—A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios

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Advances and New Trends in Environmental Informatics (ENVIROINFO 2021)

Part of the book series: Progress in IS ((PROIS))

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Abstract

Human mobility has been recognized as one of the critical factors determining the spread of contagious diseases, such as SARS-CoV-2, a highly contagious and elusive virus. This virus disrupts the normal lives of more than half of the global population in one way or another, claiming the lives of millions. In such cases, mobility should be managed via the imposition of certain policies. This proposed study presents a newly developed spatial platform aimed at simulating and mapping the spread of infectious diseases and mobility patterns under different scenarios based on different epidemiological models. In addition to the "business as usual" scenario, other response scenarios can be defined to reflect real-world situations, taking into consideration various parameters, including the daily rise in infections and deaths, among others. The developed system provides insights to decision-makers about strategies to be implemented and measures for controlling the spread of the virus.

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Notes

  1. 1.

    https://github.com/DotSpatial/DotSpatial.

References

  1. Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track COVID-19 in real time. Lancet. Infect. Dis. 20, 533–534 (2020). https://doi.org/10.1016/S1473-3099(20)30120-1

    Article  Google Scholar 

  2. World Bank: Population, total | Data, https://data.worldbank.org/indicator/SP.POP.TOTL. Accessed 28 April 2021

  3. Population Reference Bureau: 2020 World Population Data Sheet (2020)

    Google Scholar 

  4. Hadjidemetriou, G.M., Sasidharan, M., Kouyialis, G., Parlikad, A.K.: The impact of government measures and human mobility trend on COVID-19 related deaths in the UK. Transp. Res. Interdisc. Perspect. 6, 100167 (2020).https://doi.org/10.1016/j.trip.2020.100167

  5. Zhou, Y., Xu, R., Hu, D., Yue, Y., Li, Q., Xia, J.: Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data. The Lancet Digital Health. 2, e417–e424 (2020). https://doi.org/10.1016/S2589-7500(20)30165-5

    Article  Google Scholar 

  6. Chu, S., Gao, S., Sundaram, D.: A flexible multi-criteria spatial decision support architecture: design and evaluation by healthcare test cases. J. Inf. Technol. Healthc. 3, 5–20 (2005)

    Google Scholar 

  7. Afzal, S., Maciejewski, R., Ebert, D.S.: Visual analytics decision support environment for epidemic modeling and response evaluation. In: VAST 2011—IEEE Conference on Visual Analytics Science and Technology 2011, Proceedings (2011). https://doi.org/10.1109/VAST.2011.6102457

  8. Broeck, W.V.D., Gioannini, C., Gonçalves, B., Quaggiotto, M., Colizza, V., Vespignani, A.: The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect. Dis. 11, (2011). https://doi.org/10.1186/1471-2334-11-37

  9. Maciejewski, R., Livengood, P., Rudolph, S., Collins, T.F., Ebert, D.S., Brigantic, R.T., Corley, C.D., Muller, G.A., Sanders, S.W.: A pandemic influenza modeling and visualization tool. J. Vis. Lang. Comput. 22, 268–278 (2011). https://doi.org/10.1016/j.jvlc.2011.04.002

    Article  Google Scholar 

  10. Bryan, C., Wu, X., Mniszewski, S., Ma, K.-L.: Integrating predictive analytics into a spatiotemporal epidemic simulation. In: VAST 2015— IEEE Conference on Visual Analytics Science and Technology, Proceedings (2015). https://doi.org/10.1109/VAST.2015.7347626

  11. Ali, M.A., Ahsan, Z., Amin, M., Latif, S., Ayyaz, A., Ayyaz, M.N.: ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan. Public Health. 134, 72–85 (2016). https://doi.org/10.1016/j.puhe.2016.01.006

    Article  Google Scholar 

  12. Yañez, A., Duggan, J., Hayes, C., Jilani, M., Connolly, M.: PandemCap: Decision support tool for epidemic management. In: VAHC 2017— IEEE Workshop on Visual Analytics in Healthcare, Proceedings (2018). https://doi.org/10.1109/VAHC.2017.8387497

  13. Lipeng, J., Xuedong, Z., Jianqin, Z., Zhijie, X., Shaocun, D.: Design and Development of a Visualization System for COVID-19 Simulation Based on WebGIS. In: 2020 International Conference on Public Health and Data Science (ICPHDS), pp. 278–282 (2020). https://doi.org/10.1109/ICPHDS51617.2020.00061

  14. Silva, P.C.L., Batista, P.V.C., Lima, H.S., Alves, M.A., Guimarães, F.G., Silva, R.C.P.: COVID-ABS: an agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos, Solitons & Fractals 139, 110088 (2020). https://doi.org/10.1016/j.chaos.2020.110088

  15. Macal, C.M., North, M.J.: Agent-based modeling and simulation. In: Proceedings of the 2009 Winter Simulation Conference (WSC), pp. 86–98 (2009). https://doi.org/10.1109/WSC.2009.5429318

  16. Kashiyama, T., Pang, Y., Sekimoto, Y.: Open PFLOW: creation and evaluation of an open dataset for typical people mass movement in urban areas. Transp. Res. Part C: Emerg. Technol. 85, 249–267 (2017). https://doi.org/10.1016/j.trc.2017.09.016

    Article  Google Scholar 

  17. Sharma, S., Ogunlana, K., Scribner, D., Grynovicki, J.: Modeling human behavior during emergency evacuation using intelligent agents: a multi-agent simulation approach. Inf. Syst. Front. 20, 741–757 (2018). https://doi.org/10.1007/s10796-017-9791-x

    Article  Google Scholar 

  18. Vynnycky, E., White, R.: An Introduction to Infectious Disease Modelling. Oxford University Press, Oxford, New York (2010)

    Google Scholar 

  19. Aggarwal, L., Goswami, P., Sachdeva, S.: Multi-criterion intelligent decision support system for COVID-19. Appl. Soft Comput. 101, 107056 (2021).https://doi.org/10.1016/j.asoc.2020.107056

  20. Sugumaran, R., Degroote, J.: Spatial Decision Support Systems: Principles and Practices. CRC Press, Boca Raton (2010)

    Book  Google Scholar 

  21. Ames, D.P., Horsburgh, J.S., Cao, Y., Kadlec, J., Whiteaker, T., Valentine, D.: HydroDesktop: Web services based software for hydrologic data discovery, download, visualization, and analysis. Environ. Model. Softw. 37, 146–156 (2012). https://doi.org/10.1016/j.envsoft.2012.03.013

  22. Lazoglou, M., Angelides, D.C.: Development of a spatial decision support system for land-use suitability assessment: the case of complex tourism accommodation in Greece. Res. Globalization 2, 100022 (2020).https://doi.org/10.1016/j.resglo.2020.100022

  23. Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., Bosnić, Z.: AgroDSS: a decision support system for agriculture and farming. Comput. Electron. Agric. 161, 260–271 (2019). https://doi.org/10.1016/j.compag.2018.04.001

    Article  Google Scholar 

  24. Sermet, Y., Demir, I., Muste, M.: A serious gaming framework for decision support on hydrological hazards. Sci. Total Environ. 728, 138895 (2020).https://doi.org/10.1016/j.scitotenv.2020.138895

  25. Google LLC: Google COVID-19 Community Mobility Reports. https://www.google.com/covid19/mobility/. Accessed 12 June 2021

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Authors and Affiliations

  1. School of Environment and Society, Tokyo Institute of Technology, 2-12-1-M1-25 Ookayama, Meguro-Ku, Tokyo, 152-8550, Japan

    Ahmed Derdouri & Toshihiro Osaragi

Authors
  1. Ahmed Derdouri
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  2. Toshihiro Osaragi
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Corresponding author

Correspondence to Ahmed Derdouri .

Editor information

Editors and Affiliations

  1. HTW Berlin - Univ of Applied Sciences, Berlin, Berlin, Germany

    Volker Wohlgemuth

  2. Environmental Campus Birkenfeld, Trier University of Applied Sciences, Birkenfeld, Germany

    Stefan Naumann

  3. FB Campus Minden, University of Applied Sciences Bielefeld, Minden, Germany

    Grit Behrens

  4. Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Hans-Knud Arndt

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Derdouri, A., Osaragi, T. (2022). EpiDesktop—A Spatial Decision Support System for Simulating Epidemic Spread and Human Mobility Trends Under Different Scenarios. In: Wohlgemuth, V., Naumann, S., Behrens, G., Arndt, HK. (eds) Advances and New Trends in Environmental Informatics. ENVIROINFO 2021. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-030-88063-7_9

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