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

International Journal of Environmental Research and Public Health

Céline Christiansen-Jucht³  Kamil Erguler²  Chee Yan Shek³  Mar໚-Gloria Basá༞z³  Paul E. Parham¹ 

[1] Department of Public Health and Policy, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 3GL, UK; E-Mail:;Energy, Environment, and Water Research Center, The Cyprus Institute, Nicosia 2121, Cyprus; E-Mail:;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; E-Mails:

关键词: mathematical modelling;    climate change;    mosquito population dynamics;    Anopheles gambiae s.s.;    senescence;    malaria;    vector-borne diseases;   

DOI  :  10.3390/ijerph120605975

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.