Evidence map›Paper›PMID 26030468›Full record

ArticleInternational journal of environmental research and public health2015

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

Céline Christiansen-Jucht, Kamil Erguler, Chee Yan Shek, María-Gloria Basáñez, Paul E Parham

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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  6. Age influences the thermal suitability ofProceedings. Biological sciences · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Céline Christiansen-JuchtDepartment 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.
Kamil ErgulerEnergy, Environment, and Water Research Center, The Cyprus Institute, Nicosia 2121, Cyprus. k.erguler@cyi.ac.cy.
Chee Yan ShekDepartment of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London W2 1PG, UK. chee.shek08@imperial.ac.uk.
María-Gloria BasáñezDepartment of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London St Mary's Campus, Norfolk Place, London W2 1PG, UK. m.basanez@imperial.ac.uk.
Paul E ParhamDepartment of Public Health and Policy, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 3GL, UK. Paul.Parham@liverpool.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Models, TheoreticalTemperatureAfrica South of the SaharaAge FactorsAnimalsAnophelesClimate ChangeHumansInsect VectorsLongitudinal StudiesMalariaPopulation DynamicsAnopheles gambiae s.s.climate changemalariamathematical modellingmosquito population dynamicssenescencevector-borne diseases

Identifiers

PMID26030468
PMCPMC4483682

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.