IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v425y2020ics0304380020300818.html
   My bibliography  Save this article

An individual-based model to assess the spatial and individual heterogeneity of Brucella melitensis transmission in Alpine ibex

Author

Listed:
  • Lambert, Sébastien
  • Gilot-Fromont, Emmanuelle
  • Toïgo, Carole
  • Marchand, Pascal
  • Petit, Elodie
  • Garin-Bastuji, Bruno
  • Gauthier, Dominique
  • Gaillard, Jean-Michel
  • Rossi, Sophie
  • Thébault, Anne

Abstract

Heterogeneity of infectious disease transmission can be generated by individual differences in the frequency of contacts with susceptible individuals, in the ability to transmit the infectious agent or in the duration of infection, and by spatial variation in the distribution, density or movements of hosts. Identifying spatial and individual heterogeneity can help improving management strategies to eradicate or mitigate infectious diseases, by targeting the individuals or areas that are responsible for most transmissions. Individual-based models allow quantifying the respective role of these sources of heterogeneity by integrating potential mechanisms that generate heterogeneity and then by tracking transmissions caused by each infected individual. In this study, we provide an individual-based model of endemic brucellosis Brucella melitensis transmission in the population of Alpine ibex (Capra ibex) of the Bargy massif (France) by taking advantage of detailed information available on ibex population dynamics, behaviour, and habitat use, and on epidemiological surveys. This host-pathogen system is expected to be subject of both individual and spatial heterogeneity. We first estimated the transmission probabilities, hitherto unknown, of the two main transmission routes of the infection (i.e., exposure to infectious births/abortions and venereal transmission). Then, we quantified heterogeneity at both individual and spatial levels. We found that both transmission routes are not negligible to explain the data, and that there is a high amount of heterogeneity of the host-pathogen system at the individual level, with females generating around 90% of the new cases of brucellosis infection. Males transmit infection at a lesser extent but still play a non-negligible role because they move between subpopulations and thereby create opportunities for spreading the infection spatially by venereal transmission. Two particular socio-spatial units are hotspots of transmission, and act as sources of transmission for the other units. These results may have important implications for disease management strategies.

Suggested Citation

  • Lambert, Sébastien & Gilot-Fromont, Emmanuelle & Toïgo, Carole & Marchand, Pascal & Petit, Elodie & Garin-Bastuji, Bruno & Gauthier, Dominique & Gaillard, Jean-Michel & Rossi, Sophie & Thébault, Anne, 2020. "An individual-based model to assess the spatial and individual heterogeneity of Brucella melitensis transmission in Alpine ibex," Ecological Modelling, Elsevier, vol. 425(C).
  • Handle: RePEc:eee:ecomod:v:425:y:2020:i:c:s0304380020300818
    DOI: 10.1016/j.ecolmodel.2020.109009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020300818
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109009?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. D. T. Haydon & D. A. Randall & L. Matthews & D. L. Knobel & L. A. Tallents & M. B. Gravenor & S. D. Williams & J. P. Pollinger & S. Cleaveland & M. E. J. Woolhouse & C. Sillero-Zubiri & J. Marino & D., 2006. "Low-coverage vaccination strategies for the conservation of endangered species," Nature, Nature, vol. 443(7112), pages 692-695, October.
    2. Ellen Brooks-Pollock & Gareth O. Roberts & Matt J. Keeling, 2014. "A dynamic model of bovine tuberculosis spread and control in Great Britain," Nature, Nature, vol. 511(7508), pages 228-231, July.
    3. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    4. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    5. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    6. Alison P. Galvani & Robert M. May, 2005. "Dimensions of superspreading," Nature, Nature, vol. 438(7066), pages 293-295, November.
    7. Eli P. Fenichel & Richard D. Horan, 2007. "Gender-Based Harvesting in Wildlife Disease Management," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(4), pages 904-920.
    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. Russell, Robin E. & Walsh, Daniel P. & Samuel, Michael D. & Grunnill, Martin D. & Rocke, Tonie E., 2021. "Space matters: host spatial structure and the dynamics of plague transmission," Ecological Modelling, Elsevier, vol. 443(C).

    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. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    2. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    3. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    4. Moshe B Hoshen & Anthony H Burton & Themis J V Bowcock, 2007. "Simulating disease transmission dynamics at a multi-scale level," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 26-34.
    5. 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.
    6. Lingcai Kong & Jinfeng Wang & Weiguo Han & Zhidong Cao, 2016. "Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model," IJERPH, MDPI, vol. 13(3), pages 1-13, February.
    7. Ellen Brooks-Pollock & Leon Danon & Hester Korthals Altes & Jennifer A Davidson & Andrew M T Pollock & Dick van Soolingen & Colin Campbell & Maeve K Lalor, 2020. "A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-14, March.
    8. Jonas I Liechti & Gabriel E Leventhal & Sebastian Bonhoeffer, 2017. "Host population structure impedes reversion to drug sensitivity after discontinuation of treatment," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-19, August.
    9. Mark D Jankowski & Christopher J Williams & Jeanne M Fair & Jennifer C Owen, 2013. "Birds Shed RNA-Viruses According to the Pareto Principle," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    10. Yunhwan Kim & Hohyung Ryu & Sunmi Lee, 2018. "Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea," IJERPH, MDPI, vol. 15(11), pages 1-17, October.
    11. Wang, Jia-Zeng & Peng, Wei-Hua, 2020. "Fluctuations for the outbreak prevalence of the SIR epidemics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    12. Yeongseon Park & Michael A. Martin & Katia Koelle, 2023. "Epidemiological inference for emerging viruses using segregating sites," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    13. Calvin Pozderac & Brian Skinner, 2021. "Superspreading of SARS-CoV-2 in the USA," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-10, March.
    14. Lilith K Whittles & Peter J White & Xavier Didelot, 2019. "A dynamic power-law sexual network model of gonorrhoea outbreaks," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-20, March.
    15. Seoyun Choe & Hee-Sung Kim & Sunmi Lee, 2020. "Exploration of Superspreading Events in 2015 MERS-CoV Outbreak in Korea by Branching Process Models," IJERPH, MDPI, vol. 17(17), pages 1-14, August.
    16. C. E. Dangerfield & A. E. Whalley & N. Hanley & C. A. Gilligan, 2018. "What a Difference a Stochastic Process Makes: Epidemiological-Based Real Options Models of Optimal Treatment of Disease," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(3), pages 691-711, July.
    17. Meggan E Craft & Hawthorne L Beyer & Daniel T Haydon, 2013. "Estimating the Probability of a Major Outbreak from the Timing of Early Cases: An Indeterminate Problem?," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-7, March.
    18. Elizabeth Hunter & Brian Mac Namee & John Kelleher, 2018. "An open-data-driven agent-based model to simulate infectious disease outbreaks," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-35, December.
    19. Liu, Yu & Wang, Bai & Wu, Bin & Shang, Suiming & Zhang, Yunlei & Shi, Chuan, 2016. "Characterizing super-spreading in microblog: An epidemic-based information propagation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 202-218.
    20. Daouia, Abdelaati & Stupfler, Gilles & Usseglio-Carleve, Antoine, 2022. "Extreme value modelling of SARS-CoV-2 community transmission using discrete Generalised Pareto distributions," TSE Working Papers 22-1323, Toulouse School of Economics (TSE), revised 09 Mar 2023.

    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:ecomod:v:425:y:2020:i:c:s0304380020300818. 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.journals.elsevier.com/ecological-modelling .

    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.