IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v12y2015i6p5975-6005d50295.html
   My bibliography  Save this article

Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival

Author

Listed:
  • Céline Christiansen-Jucht

    (Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London W2 1PG, UK)

  • Kamil Erguler

    (Energy, Environment, and Water Research Center, The Cyprus Institute, Nicosia 2121, Cyprus)

  • Chee Yan Shek

    (Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London W2 1PG, UK)

  • María-Gloria Basáñez

    (Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London W2 1PG, UK)

  • Paul E. Parham

    (Department of Public Health and Policy, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 3GL, UK)

Abstract

Climate change and global warming are emerging as important threats to human health, particularly through the potential increase in vector- and water-borne diseases. Environmental variables are known to affect substantially the population dynamics and abundance of the poikilothermic vectors of disease, but the exact extent of this sensitivity is not well established. Focusing on malaria and its main vector in Africa, Anopheles gambiae sensu stricto , we present a set of novel mathematical models of climate-driven mosquito population dynamics motivated by experimental data suggesting that in An. gambiae , mortality is temperature and age dependent. We compared the performance of these models to that of a “standard” model ignoring age dependence. We used a longitudinal dataset of vector abundance over 36 months in sub-Saharan Africa for comparison between models that incorporate age dependence and one that does not, and observe that age-dependent models consistently fitted the data better than the reference model. This highlights that including age dependence in the vector component of mosquito-borne disease models may be important to predict more reliably disease transmission dynamics. Further data and studies are needed to enable improved fitting, leading to more accurate and informative model predictions for the An. gambiae malaria vector as well as for other disease vectors.

Suggested Citation

  • Céline Christiansen-Jucht & Kamil Erguler & Chee Yan Shek & María-Gloria Basáñez & Paul E. Parham, 2015. "Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival," IJERPH, MDPI, vol. 12(6), pages 1-31, May.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:6:p:5975-6005:d:50295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/12/6/5975/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/12/6/5975/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas S. Churcher & Jean-François Trape & Anna Cohuet, 2015. "Human-to-mosquito transmission efficiency increases as malaria is controlled," Nature Communications, Nature, vol. 6(1), pages 1-8, May.
    2. Joke Bilcke & Philippe Beutels & Marc Brisson & Mark Jit, 2011. "Accounting for Methodological, Structural, and Parameter Uncertainty in Decision-Analytic Models," Medical Decision Making, , vol. 31(4), pages 675-692, July.
    3. Helen J Wearing & Pejman Rohani & Matt J Keeling, 2005. "Appropriate Models for the Management of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 2(7), pages 1-1, July.
    4. Annelise Tran & Grégory L'Ambert & Guillaume Lacour & Romain Benoît & Marie Demarchi & Myriam Cros & Priscilla Cailly & Mélaine Aubry-Kientz & Thomas Balenghien & Pauline Ezanno, 2013. "A Rainfall- and Temperature-Driven Abundance Model for Aedes albopictus Populations," IJERPH, MDPI, vol. 10(5), pages 1-22, April.
    5. Guillaume Chabot-Couture & Karima Nigmatulina & Philip Eckhoff, 2014. "An Environmental Data Set for Vector-Borne Disease Modeling and Epidemiology," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-17, April.
    6. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    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. Ngwa, Gideon A. & Teboh-Ewungkem, Miranda I. & Njongwe, James A., 2023. "Continuous-time predator-prey-like systems used to investigate the question: Can human consciousness help eliminate temporary mosquito breeding sites from around human habitats?," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 437-469.

    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. Lahrouz, A. & El Mahjour, H. & Settati, A. & Bernoussi, A., 2018. "Dynamics and optimal control of a non-linear epidemic model with relapse and cure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 299-317.
    2. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    3. Hongwei Shao & Tao Peng & Zhiwei Ji & Jing Su & Xiaobo Zhou, 2013. "Systematically Studying Kinase Inhibitor Induced Signaling Network Signatures by Integrating Both Therapeutic and Side Effects," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-16, December.
    4. Zheng, Zhoumin & Xu, Nuo & Khan, Mohsin & Pedersen, Michael & Abdalgader, Tarteel & Zhang, Lai, 2024. "Nonlinear impacts of climate change on dengue transmission in mainland China: Underlying mechanisms and future projection," Ecological Modelling, Elsevier, vol. 492(C).
    5. Richard C. Larson, 2007. "Simple Models of Influenza Progression Within a Heterogeneous Population," Operations Research, INFORMS, vol. 55(3), pages 399-412, June.
    6. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2016. "Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-18, April.
    7. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
    8. Andrew White & Malachi Tolman & Howard D Thames & Hubert Rodney Withers & Kathy A Mason & Mark K Transtrum, 2016. "The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-26, December.
    9. Beniamino Caputo & Mattia Manica & Federico Filipponi & Marta Blangiardo & Pietro Cobre & Luca Delucchi & Carlo Maria De Marco & Luca Iesu & Paola Morano & Valeria Petrella & Marco Salvemini & Cesare , 2020. "ZanzaMapp: A Scalable Citizen Science Tool to Monitor Perception of Mosquito Abundance and Nuisance in Italy and Beyond," IJERPH, MDPI, vol. 17(21), pages 1-19, October.
    10. Victoria Chebotaeva & Paula A. Vasquez, 2023. "Erlang-Distributed SEIR Epidemic Models with Cross-Diffusion," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    11. Elba Raimúndez & Simone Keller & Gwen Zwingenberger & Karolin Ebert & Sabine Hug & Fabian J Theis & Dieter Maier & Birgit Luber & Jan Hasenauer, 2020. "Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-21, March.
    12. Daniele Proverbio & Françoise Kemp & Stefano Magni & Andreas Husch & Atte Aalto & Laurent Mombaerts & Alexander Skupin & Jorge Gonçalves & Jose Ameijeiras-Alonso & Christophe Ley, 2021. "Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    13. Joseph D Taylor & Samuel Winnall & Alain Nogaret, 2020. "Estimation of neuron parameters from imperfect observations," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-22, July.
    14. Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    15. Andrija S Grustam & Nasuh Buyukkaramikli & Ron Koymans & Hubertus J M Vrijhoef & Johan L Severens, 2019. "Value of information analysis in telehealth for chronic heart failure management," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
    16. Xinxian Shao & Andrew Mugler & Justin Kim & Ha Jun Jeong & Bruce R Levin & Ilya Nemenman, 2017. "Growth of bacteria in 3-d colonies," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-19, July.
    17. Carbone, Giuseppe & De Vincenzo, Ilario, 2022. "A general theory for infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    18. Tim Goedemé & Karel Van den Bosch & Lina Salanauskaite & Gerlinde Verbist, 2013. "Testing the Statistical Significance of Microsimulation Results: A Plea," International Journal of Microsimulation, International Microsimulation Association, vol. 6(3), pages 50-77.
    19. Anthony Newall & Mark Jit & Philippe Beutels, 2012. "Economic Evaluations of Childhood Influenza Vaccination," PharmacoEconomics, Springer, vol. 30(8), pages 647-660, August.
    20. Peace, Angela & O’Regan, Suzanne M. & Spatz, Jennifer A. & Reilly, Patrick N. & Hill, Rachel D. & Carter, E. Davis & Wilkes, Rebecca P. & Waltzek, Thomas B. & Miller, Debra L. & Gray, Matthew J., 2019. "A highly invasive chimeric ranavirus can decimate tadpole populations rapidly through multiple transmission pathways," Ecological Modelling, Elsevier, vol. 410(C), pages 1-1.

    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:jijerp:v:12:y:2015:i:6:p:5975-6005:d:50295. 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.