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

Christiansen-Jucht C, Erguler K, Shek CY, Basáñez MG, Parham PE - Int J Environ Res Public Health (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 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. celine.jucht@imperial.ac.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.

No MeSH data available.


Related in: MedlinePlus

Flow diagram representing the structure of Model 2.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4483682&req=5

ijerph-12-05975-f002: Flow diagram representing the structure of Model 2.

Mentions: The second model (Figure 2 and Equation (2)) takes into account age-dependent mortality affecting larvae via a gamma distribution with parameters αL and βL by subdividing the larval stage into αL subclasses (the number of sub-classes is determined as defined below) [72,73]. The rate at which larvae progress through the subclasses is set as αLμL, where μL is equal to 1/αLβL. Upon hatching, eggs will enter the first larval subclass (L1), in which they either progress to the next subclass (L2) at temperature-dependent rate 7 μL, progress to pupae at temperature-dependent rate σL, or die due to overcrowding at density-dependent rate μK. This process continues as they progress through all subsequent subclasses.


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

Christiansen-Jucht C, Erguler K, Shek CY, Basáñez MG, Parham PE - Int J Environ Res Public Health (2015)

Flow diagram representing the structure of Model 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4483682&req=5

ijerph-12-05975-f002: Flow diagram representing the structure of Model 2.
Mentions: The second model (Figure 2 and Equation (2)) takes into account age-dependent mortality affecting larvae via a gamma distribution with parameters αL and βL by subdividing the larval stage into αL subclasses (the number of sub-classes is determined as defined below) [72,73]. The rate at which larvae progress through the subclasses is set as αLμL, where μL is equal to 1/αLβL. Upon hatching, eggs will enter the first larval subclass (L1), in which they either progress to the next subclass (L2) at temperature-dependent rate 7 μL, progress to pupae at temperature-dependent rate σL, or die due to overcrowding at density-dependent rate μK. This process continues as they progress through all subsequent subclasses.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 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. celine.jucht@imperial.ac.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.

No MeSH data available.


Related in: MedlinePlus