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. 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

Phong V V Le  1   2 , Praveen Kumar  1   3 , Marilyn O Ruiz  4 , Charles Mbogo  5 , Ephantus J Muturi  6
Affiliations

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

Phong V V Le et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schematic of SLIM model for predicting malaria dynamics under climate change.
SLIM consists of two time-continuous space-discrete models (S-ELPAs and S-SEIR) that considered the nonlinear relationship between temperature and Plasmodium development inside Anopheles vectors for estimating the EIP. SLIM is linked with an ecohydrologic modeling framework (Dhara) to incorporate the acclimatory responses of vegetation on soil moisture and breeding habitat of vectors under climate change. Solid arrows represent direct impacts of temperature increase on malaria. Dash arrows represent the indirect impacts of ecohydrologic acclimation under climate change on malaria.
Fig 2
Fig 2. Map of study area in Kilifi county, Kenya.
(a) Mean annual precipitation gradient map of Africa showing Kenya in red region. (b) Distribution of P. falciparum incidence rate in Kenya. Areas that have no data are shown in white. The Kilifi county (bottom right, black polygon) is one of the regions of highest malaria incidence in Kenya. Data is obtained from the Malaria Atlas Project [54]. (c) Map of population density distribution in Kilifi county, Kenya (Data is adapted from [56]). Simulations are conducted for the area of 440 km2 indicated by the red rectangle. (d) Map of tree cover in Kilifi, Kenya. Data is obtained from the World Resource Institute (http://www.wri.org).
Fig 3
Fig 3. Domain of simulations in the study area.
(a) Variation in topographic elevation (source: ASTER DEM). (b) Map of distribution of human population in the study area (source: [56]). The gray background represents hillshaded topography.
Fig 4
Fig 4. Estimation of power-law scaling of topographic depression.
(a) Map of topographic depression (red polygons) identified from digital elevation model using TDI model [64]. The gray background represents hillshaded topography. (b) Scaling law relationship of topographic depressions at different ponding levels. Lines are fitted to the distributions using least square linear regression. R2, α, β represent the coefficient of determination, intercept, and slope, respectively, for each curve.
Fig 5
Fig 5. Comparison of malaria incidence rate modeled by SLIM and observed data.
Vertical line represents ± standard deviation. Malaria incidence data are collected in 3 elementary schools in the area and from the Malaria Atlas Project. The high uncertainty of observed incidence in Oct 2008 comes from the variability and small sample size of the data collection.
Fig 6
Fig 6. Mean annual evapotranspiration of the study region obtained from model simulations for each climate scenario.
Box plots display 25th, 50th, and 75th percentiles. Color squares represent modeled data, and black dots represent the mean value of annual ET.
Fig 7
Fig 7. Key meteorological forcing data and variations of mosquito populations in scenario S0).
(a) Daily precipitation; (b) Mean daily air temperature; (c) Population dynamics of mosquitoes in three aquatic phases (egg E, larval L, and pupal P) in the S-ELPAs model; and (d) Population dynamics of mosquitoes in three adult stages (host seeking Ah, resting Ar, and oviposition site searching Ao) in the S-ELPAs model. Atotal represents the sum of adult mosquitoes in all phases.
Fig 8
Fig 8. Key meteorological forcing data and the variations of malaria in scenario S0.
(a) Daily precipitation and (b) Mean daily air temperature (both are the same as in Fig 6); (c) Variation of exposed (Eh) and infectious (Ih) host populations modeled in the S-SEIR model; and (d) Variation of vector populations (susceptible Sv, exposed Ev, and infectious Iv) modeled in the S-SEIR model. Nv represents the total adult vectors. The S-SEIR represents different states of adult vectors shown in S-ELPAs.
Fig 9
Fig 9. Comparison of exposed (Eh) and infected (Ih) malaria cases under climate change scenarios.
Left column: One-to-one comparison between cases under present (S0—current) and elevated [CO2] (S1—future) conditions. Middle column: One-to-one comparison between cases under present (S0—current) and elevated [CO2] (S2—future) conditions. Right column: One-to-one comparison between cases under present (S0—current) and elevated [CO2] (S3—future) conditions. The inset boxplots show the difference between cases in current and future conditions. Top row shows the values of exposed cases, bottom row shows the values of infected cases. Black dots represent the mean values.
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Grants and funding

PVVL and PK was funded by National Science Foundation (https://www.nsf.gov) grants CBET1209402, ACI 1261582, ACI 1429699, EAR 1331906, and EAR 1417444. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.