Cited by 85Malaria Research and ControlMosquito-borne diseases and controlMathematical and Theoretical Epidemiology and Ecology Models

Modeling the role of environmental variables on the population dynamics of the malaria vector Anopheles gambiae sensu stricto

Open access
Parham, Paul E, Pople, Diane, Christiansen-Jucht, Céline, Lindsay, Steve, Hinsley, Wes, Michael, Edwin·2012·Malaria Journal

Abstract

BACKGROUND: The impact of weather and climate on malaria transmission has attracted considerable attention in recent years, yet uncertainties around future disease trends under climate change remain. Mathematical models provide powerful tools for addressing such questions and understanding the implications for interventions and eradication strategies, but these require realistic modeling of the vector population dynamics and its response to environmental variables. METHODS: Published and unpublished field and experimental data are used to develop new formulations for modeling the relationships between key aspects of vector ecology and environmental variables. These relationships are integrated within a validated deterministic model of Anopheles gambiae s.s. population dynamics to provide a valuable tool for understanding vector response to biotic and abiotic variables. RESULTS: A novel, parsimonious framework for assessing the effects of rainfall, cloudiness, wind speed, desiccation, temperature, relative humidity and density-dependence on vector abundance is developed, allowing ease of construction, analysis, and integration into malaria transmission models. Model validation shows good agreement with longitudinal vector abundance data from Tanzania, suggesting that recent malaria reductions in certain areas of Africa could be due to changing environmental conditions affecting vector populations. CONCLUSIONS: Mathematical models provide a powerful, explanatory means of understanding the role of environmental variables on mosquito populations and hence for predicting future malaria transmission under global change. The framework developed provides a valuable advance in this respect, but also highlights key research gaps that need to be resolved if we are to better understand future malaria risk in vulnerable communities.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/1475-2875-11-271

Cites

54 of 54 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Most attempts to represent surface hydrology in malaria transmission modeling resort to a simple conceptual water balance model to determine the availability of water for larval habitats.

    “Most resort to a simple conceptual water balance model to determine the availability of water for larval habitats (Asare, Tompkins, Amekudzi, & Ermert, 2016; Montosi et al., 2012; Parham et al., 2012; Patz et al., 1998), while only a few have proposed more sophisticated hydrologic models that further consider canopy processes and subsurface flows (Bomblies et al., 2008; Le et al., 2018).”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Martens P Health and climate change: modeling the impacts of global warming and ozone depletion 1998 Earthscan, Oxford, UK 176

Cited by

Sorted by citations

Authors
Parham, P. E., Pople, D., Christiansen-Jucht, C., Lindsay, S., Hinsley, W., & Michael, E.
Affiliations
  • Imperial College London (GB)
  • London School of Hygiene & Tropical Medicine (GB)
  • University of Notre Dame (US)
Corresponding author
Parham, Paul E
Venue
Malaria Journal
Published
2012-08-09
Volume
11
Issue
1
Pages
271-271
ISSN
1475-2875 (ISSN-L)
OpenAlex
W2108999275
Cited by
85
References
54
Citations by year
2026: 3 · 2025: 11 · 2024: 7 · 2023: 3 · 2022: 2 · 2021: 5 · 2020: 5 · 2019: 6 · 2018: 11 · 2017: 5 · 2016: 5 · 2015: 5 · 2014: 6 · 2013: 9 · 2012: 2
FWCI
6.80
Type
journal article
Language
en
Open access
Locations
  • Malaria Journal (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Durham Research Online (Durham University) (acceptedVersion, other-oa)
  • Durham Research Online (Durham University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • University of Notre Dame
  • Imperial College London
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Malaria Research and Control
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
Keywords
Anopheles gambiae, malaria vectors, vector population dynamics, environmental variables, rainfall, cloud cover, wind speed, desiccation, temperature, relative humidity, density dependence, vector abundance, malaria transmission, climate change, Tanzania
MeSH terms
Africa, Animals, Anopheles, Climate, Disease Vectors, Models, Theoretical, Population Dynamics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review