Format

Send to

Choose Destination
PLoS One. 2019 Feb 6;14(2):e0211258. doi: 10.1371/journal.pone.0211258. eCollection 2019.

Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya.

Author information

1
Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801, United States of America.
2
Faculty of Hydrology Meteorology and Oceanography, Vietnam National University, Hanoi, Vietnam.
3
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, United States of America.
4
Department of Pathobiology, University of Illinois, Urbana, IL 61802, United States of America.
5
Malaria Public Health Research, Kenya Medical Research Institute - Wellcome Trust Research Programme, Nairobi, Kenya.
6
Crop Bioprotection Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Peoria, IL 61604, United States of America.

Abstract

BACKGROUND:

The transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. This study investigates the combined, i.e. direct and indirect, impacts of climate change on the dynamics of malaria through modifications in: (i) the sporogonic cycle of Plasmodium induced by air temperature increase, and (ii) the life cycle of Anopheles vector triggered by changes in natural breeding habitat arising from the altered moisture dynamics resulting from acclimation responses of vegetation under climate change. The study is performed for a rural region in Kilifi county, Kenya.

METHODS AND FINDINGS:

We use a stochastic lattice-based malaria (SLIM) model to make predictions of changes in Anopheles vector abundance, the life cycle of Plasmodium parasites, and thus malaria transmission under projected climate change in the study region. SLIM incorporates a nonlinear temperature-dependence of malaria parasite development to estimate the extrinsic incubation period of Plasmodium. It is also linked with a spatially distributed eco-hydrologic modeling framework to capture the impacts of climate change on soil moisture dynamics, which served as a key determinant for the formation and persistence of mosquito larval habitats on the land surface. Malaria incidence data collected from 2008 to 2013 is used for SLIM model validation. Projections of climate change and human population for the region are used to run the models for prediction scenarios. Under elevated atmospheric CO2 concentration ([CO2]) only, modeled results reveal wetter soil moisture in the root zone due to the suppression of transpiration from vegetation acclimation, which increases the abundance of Anopheles vectors and the risk of malaria. When air temperature increases are also considered along with elevated [CO2], the life cycle of Anopheles vector and the extrinsic incubation period of Plasmodium parasites are shortened nonlinearly. However, the reduction of soil moisture resulting from higher evapotranspiration due to air temperature increase also reduces the larval habitats of the vector. Our findings show the complicated role of vegetation acclimation under elevated [CO2] on malaria dynamics and indicate an indirect but ignored impact of air temperature increase on malaria transmission through reduction in larval habitats and vector density.

CONCLUSIONS:

Vegetation acclimation triggered by elevated [CO2] under climate change increases the risk of malaria. In addition, air temperature increase under climate change has opposing effects on mosquito larval habitats and the life cycles of both Anopheles vectors and Plasmodium parasites. The indirect impacts of temperature change on soil moisture dynamics are significant and should be weighed together with the direct effects of temperature change on the life cycles of mosquitoes and parasites for future malaria prediction and control.

PMID:
30726279
PMCID:
PMC6364917
DOI:
10.1371/journal.pone.0211258
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center