IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v11y2019i4p92-d220787.html
   My bibliography  Save this article

Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

Author

Listed:
  • Jürgen Hackl

    (Institute of Construction and Infrastructure Management, ETH Zurich, 8093 Zurich, Switzerland)

  • Thibaut Dubernet

    (Institute for Transport Planning and Systems, ETH Zurich, 8093 Zurich, Switzerland)

Abstract

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

Suggested Citation

  • Jürgen Hackl & Thibaut Dubernet, 2019. "Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models," Future Internet, MDPI, vol. 11(4), pages 1-14, April.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:4:p:92-:d:220787
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/11/4/92/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/11/4/92/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paolo Bajardi & Chiara Poletto & Jose J Ramasco & Michele Tizzoni & Vittoria Colizza & Alessandro Vespignani, 2011. "Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    2. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lu, Zhong-Wen & Xu, Yuan-Hao & Chen, Jie & Hu, Mao-Bin, 2023. "Investigation of traffic-driven epidemic spreading by taxi trip data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    2. Calum MacRury & Nykyta Polituchyi & Paweł Prałat & Kinga Siuta & Przemysław Szufel, 2024. "Optimizing transport frequency in multi-layered urban transportation networks for pandemic prevention," Public Transport, Springer, vol. 16(2), pages 381-418, June.
    3. Fatima-Zohra Younsi & Djamila Hamdadou, 2021. "Dynamic Contact Network Simulation Model Based on Multi-Agent Systems," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-21, October.
    4. Abderrahim Zannou & Abdelhak Boulaalam & El Habib Nfaoui, 2020. "SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process," Future Internet, MDPI, vol. 13(1), pages 1-23, December.
    5. Seyyed-Mahdi Hosseini-Motlagh & Mohammad Reza Ghatreh Samani & Behnam Karimi, 2023. "Resilient and social health service network design to reduce the effect of COVID-19 outbreak," Annals of Operations Research, Springer, vol. 328(1), pages 903-975, September.
    6. Patrick Urrutia & David Wren & Chrysafis Vogiatzis & Ruriko Yoshida, 2022. "SARS-CoV-2 Dissemination Using a Network of the US Counties," SN Operations Research Forum, Springer, vol. 3(2), pages 1-23, June.
    7. Zeng, Jia-Ying & Lu, Ping & Wei, Ying & Chen, Xin & Lin, Kai-Biao, 2023. "Deep reinforcement learning based medical supplies dispatching model for major infectious diseases: Case study of COVID-19," Operations Research Perspectives, Elsevier, vol. 11(C).
    8. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Homaei, Shamim, 2023. "Design of control strategies to help prevent the spread of COVID-19 pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 219-238.
    9. Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michał Wielechowski & Katarzyna Czech & Łukasz Grzęda, 2020. "Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic," Economies, MDPI, vol. 8(4), pages 1-24, September.
    2. Burris, Courtney & Nikolaev, Alexander & Zhong, Shiran & Bian, Ling, 2021. "Network effects in influenza spread: The impact of mobility and socio-economic factors," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    3. Xiaoyan Mu & Anthony Gar-On Yeh & Xiaohu Zhang, 2021. "The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year," Environment and Planning B, , vol. 48(7), pages 1955-1971, September.
    4. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    5. Saturnino Luz & Masood Masoodian, 2022. "Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    6. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    7. Hakan Yilmazkuday, 2021. "Welfare costs of COVID‐19: Evidence from US counties," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 826-848, September.
    8. repec:plo:pone00:0070578 is not listed on IDEAS
    9. Michele Coscia & Ricardo Hausmann, 2015. "Evidence That Calls-Based and Mobility Networks Are Isomorphic," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.
    10. Richard C. Larson, 2007. "Simple Models of Influenza Progression Within a Heterogeneous Population," Operations Research, INFORMS, vol. 55(3), pages 399-412, June.
    11. Askitas, Nikos & Tatsiramos, Konstantinos & Verheyden, Bertrand, 2020. "Lockdown Strategies, Mobility Patterns and COVID-19," IZA Discussion Papers 13293, Institute of Labor Economics (IZA).
    12. Wang, Wenjun & Pan, Lin & Yuan, Ning & Zhang, Sen & Liu, Dong, 2015. "A comparative analysis of intra-city human mobility by taxi," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 134-147.
    13. Hyeyoung Kim & Ningchuan Xiao & Mark Moritz & Rebecca Garabed & Laura W. Pomeroy, 2016. "Simulating the Transmission of Foot-And-Mouth Disease Among Mobile Herds in the Far North Region, Cameroon," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(2), pages 1-6.
    14. Mattia Mazzoli & Riccardo Gallotti & Filippo Privitera & Pere Colet & José J. Ramasco, 2023. "Spatial immunization to abate disease spreading in transportation hubs," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    15. Thombre, Anurag & Agarwal, Amit, 2021. "A paradigm shift in urban mobility: Policy insights from travel before and after COVID-19 to seize the opportunity," Transport Policy, Elsevier, vol. 110(C), pages 335-353.
    16. Yeran Sun & Hongchao Fan & Ming Li & Alexander Zipf, 2016. "Identifying the city center using human travel flows generated from location-based social networking data," Environment and Planning B, , vol. 43(3), pages 480-498, May.
    17. Gregory Price & Eric van Holm, 2021. "The Effect of Social Distancing on the Early Spread of the Novel Coronavirus," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2331-2340, September.
    18. Kuo-Ying Wang, 2014. "How Change of Public Transportation Usage Reveals Fear of the SARS Virus in a City," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    19. Yuan Li Liu & Kai Zhu & Qi Yao Chen & Jing Li & Jin Cai & Tian He & He Ping Liao, 2021. "Impact of the COVID-19 Pandemic on Farm Households’ Vulnerability to Multidimensional Poverty in Rural China," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    20. Hakan Yilmazkuday, 2020. "COVID-19 Spread and Inter-County Travel: Daily Evidence from the U.S," Working Papers 2007, Florida International University, Department of Economics.
    21. Fang, Hanming & Wang, Long & Yang, Yang, 2020. "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China," Journal of Public Economics, Elsevier, vol. 191(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:11:y:2019:i:4:p:92-:d:220787. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.