IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v1y1999i2d10.1023_a1010061321051.html
   My bibliography  Save this article

Branching Approximation for the Collective Epidemic Model

Author

Listed:
  • Claude Lefe`vre

    (Universite´ Libre de Bruxelles)

  • Sergey Utev

    (La Trobe University)

Abstract

A new approach is developed that allows us to establish and analyze a branching-type approximation for the collective epidemic model. Firstly, a necessary and sufficient condition is obtained for the vague convergence of the final size of the epidemic to the total progeny in an appropriate branching model, as the initial number of susceptibles tends to infinity. Then, an upper bound for the L 1 distance between the statistics under study is derived, showing inter alia that the approximation may hold even when the initial number of infectives is arbitrarily large. The results are illustrated with several particular models of special interest.

Suggested Citation

  • Claude Lefe`vre & Sergey Utev, 1999. "Branching Approximation for the Collective Epidemic Model," Methodology and Computing in Applied Probability, Springer, vol. 1(2), pages 211-228, September.
  • Handle: RePEc:spr:metcap:v:1:y:1999:i:2:d:10.1023_a:1010061321051
    DOI: 10.1023/A:1010061321051
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1010061321051
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1023/A:1010061321051?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. Ball, Frank & Donnelly, Peter, 1995. "Strong approximations for epidemic models," Stochastic Processes and their Applications, Elsevier, vol. 55(1), pages 1-21, January.
    2. Lefèvre, Claude & Utev, Sergei, 1997. "Mixed Poisson approximation in the collective epidemic model," Stochastic Processes and their Applications, Elsevier, vol. 69(2), pages 217-246, September.
    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. Donatien Hainaut, 2020. "An Actuarial Approach for Modeling Pandemic Risk," Risks, MDPI, vol. 9(1), pages 1-28, December.
    2. Hainaut, Donatien, 2020. "An actuarial approach for modeling pandemic risk," LIDAM Discussion Papers ISBA 2020025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Barbour, A. D. & Utev, Sergey, 2004. "Approximating the Reed-Frost epidemic process," Stochastic Processes and their Applications, Elsevier, vol. 113(2), pages 173-197, October.

    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. Simon, Matthieu, 2020. "SIR epidemics with stochastic infectious periods," Stochastic Processes and their Applications, Elsevier, vol. 130(7), pages 4252-4274.
    2. Barbour, A. D. & Utev, Sergey, 2004. "Approximating the Reed-Frost epidemic process," Stochastic Processes and their Applications, Elsevier, vol. 113(2), pages 173-197, October.
    3. Claude Lefèvre & Sergey Utev, 1998. "On Order-Preserving Properties of Probability Metrics," Journal of Theoretical Probability, Springer, vol. 11(4), pages 907-920, October.
    4. Denuit, Michel & Lefevre, Claude & Utev, Sergey, 2002. "Measuring the impact of dependence between claims occurrences," Insurance: Mathematics and Economics, Elsevier, vol. 30(1), pages 1-19, February.
    5. Villela, Daniel A.M., 2016. "Analysis of the vectorial capacity of vector-borne diseases using moment-generating functions," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 1-8.
    6. Johannes Müler & Volker Hösel, 2007. "Estimating the Tracing Probability from Contact History at the Onset of an Epidemic," Mathematical Population Studies, Taylor & Francis Journals, vol. 14(4), pages 211-236, November.
    7. Terrazas-Santamaria Diana & Mendoza-Palacios Saul & Berasaluce-Iza Julen, 2023. "An Alternative Approach to Frequency of Patent Technology Codes: The Case of Renewable Energy Generation," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-14, January.
    8. Ball, Frank & Neal, Peter, 2003. "The great circle epidemic model," Stochastic Processes and their Applications, Elsevier, vol. 107(2), pages 233-268, October.
    9. Demiris, Nikolaos & Kypraios, Theodore & Smith, L. Vanessa, 2012. "On the epidemic of financial crises," MPRA Paper 46693, University Library of Munich, Germany.
    10. Chen, Zezhun & Dassios, Angelos & Kuan, Valerie & Lim, Jia Wei & Qu, Yan & Surya, Budhi & Zhao, Hongbiao, 2021. "A two-phase dynamic contagion model for COVID-19," LSE Research Online Documents on Economics 105064, London School of Economics and Political Science, LSE Library.
    11. Clancy, Damian & O'Neill, Philip, 1998. "Approximation of epidemics by inhomogeneous birth-and-death processes," Stochastic Processes and their Applications, Elsevier, vol. 73(2), pages 233-245, March.
    12. Wierman, John C. & Marchette, David J., 2004. "Modeling computer virus prevalence with a susceptible-infected-susceptible model with reintroduction," Computational Statistics & Data Analysis, Elsevier, vol. 45(1), pages 3-23, February.

    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:spr:metcap:v:1:y:1999:i:2:d:10.1023_a:1010061321051. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.