IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v161y2022icp186-199.html
   My bibliography  Save this article

Enhancing Covid-19 virus spread modeling using an activity travel model

Author

Listed:
  • Nguyen, Tri K.
  • Hoang, Nam H.
  • Currie, Graham
  • Vu, Hai L.

Abstract

Coronavirus 2019 (COVID-19) and its variants are still spreading rapidly with deadly consequences and profound impacts on the global health and world economy. Without a suitable vaccine, mobility restriction has been the most effective method so far to prevent its spreading and avoid overwhelming the heath system of the affected country. The compartmental model SIR (or Susceptible, Infected, and Recovered) is the most popular mathematical model used to predict the course of the COVID-19 pandemic in order to plan the control actions and mobility restrictions against its spreading. A major limitation of this model in relation to modeling the spreading of COVID-19, and the mobility limitation strategy, is that the SIR model does not include mobility or take into account changes in mobility within its structure. This paper develops and tests a new hybrid SIR model; SIR-M which is integrated with an urban activity travel model to explore how it might improve the prediction of pandemic course and the testing of mobility limitation strategies in managing virus spread. The paper describes the enhanced methodology and tests a range of mobility limitation strategies on virus spread outcomes. Implications for policy and research futures are suggested.

Suggested Citation

  • Nguyen, Tri K. & Hoang, Nam H. & Currie, Graham & Vu, Hai L., 2022. "Enhancing Covid-19 virus spread modeling using an activity travel model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 186-199.
    • Handle: RePEc:eee:transa:v:161:y:2022:i:c:p:186-199
    DOI: 10.1016/j.tra.2022.05.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856422001173
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2022.05.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    2. Sheryl L. Chang & Nathan Harding & Cameron Zachreson & Oliver M. Cliff & Mikhail Prokopenko, 2020. "Modelling transmission and control of the COVID-19 pandemic in Australia," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    3. Serina Chang & Emma Pierson & Pang Wei Koh & Jaline Gerardin & Beth Redbird & David Grusky & Jure Leskovec, 2021. "Mobility network models of COVID-19 explain inequities and inform reopening," Nature, Nature, vol. 589(7840), pages 82-87, January.
    4. Roorda, Matthew J. & Miller, Eric J. & Habib, Khandker M.N., 2008. "Validation of TASHA: A 24-h activity scheduling microsimulation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(2), pages 360-375, February.
    Full references (including those not matched with items on IDEAS)

    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. Pongou, Roland & Tchuente, Guy & Tondji, Jean-Baptiste, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," GLO Discussion Paper Series 957, Global Labor Organization (GLO).
    2. Daniel K Sewell & Aaron Miller & for the CDC MInD-Healthcare Program, 2020. "Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.
    3. Nathan H. Schumaker & Sydney M. Watkins, 2021. "Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA," Land, MDPI, vol. 10(4), pages 1-13, April.
    4. Gregory L Watson & Di Xiong & Lu Zhang & Joseph A Zoller & John Shamshoian & Phillip Sundin & Teresa Bufford & Anne W Rimoin & Marc A Suchard & Christina M Ramirez, 2021. "Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
    5. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    6. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2021. "Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States," Papers 2110.10230, arXiv.org.
    7. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2023. "Optimal interventions in networks during a pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 847-883, April.
    8. Shailesh Bharati & Rahul Batra, 2021. "How Misuse of Statistics Can Spread Misinformation: A Study of Misrepresentation of COVID-19 Data," Papers 2102.07198, arXiv.org.
    9. Eugenio Valdano & Davide Colombi & Chiara Poletto & Vittoria Colizza, 2023. "Epidemic graph diagrams as analytics for epidemic control in the data-rich era," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. František Božek & Irena Tušer, 2021. "Measures for Ensuring Sustainability during the Current Spreading of Coronaviruses in the Czech Republic," Sustainability, MDPI, vol. 13(12), pages 1-22, June.
    11. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    12. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    13. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    14. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    15. X. Angela Yao & Andrew Crooks & Bin Jiang & Jukka Krisp & Xintao Liu & Haosheng Huang, 2023. "An overview of urban analytical approaches to combating the Covid-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1133-1143, June.
    16. Pau Fonseca i Casas & Joan Garcia i Subirana & Víctor García i Carrasco & Xavier Pi i Palomés, 2021. "SARS-CoV-2 Spread Forecast Dynamic Model Validation through Digital Twin Approach, Catalonia Case Study," Mathematics, MDPI, vol. 9(14), pages 1-17, July.
    17. Yasmin, Farhana & Morency, Catherine & Roorda, Matthew J., 2015. "Assessment of spatial transferability of an activity-based model, TASHA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 200-213.
    18. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    19. Till Baldenius & Nicolas Koch & Hannah Klauber & Nadja Klein, 2023. "Heat increases experienced racial segregation in the United States," Papers 2306.13772, arXiv.org.
    20. Yoon, Seo Youn & Ravulaparthy, Srinath K. & Goulias, Konstadinos G., 2014. "Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 68(C), pages 3-17.

    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:eee:transa:v:161:y:2022:i:c:p:186-199. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.