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.
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.
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.
Table 1.
Climate change scenarios for model predictions.
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.
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.
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.
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.
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.
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.