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

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  • 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
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    References listed on IDEAS

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