Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

Abstract

The global increase in vector borne diseases has been linked to climate change. Seasonal vegetation changes are known to influence disease vector population. However, the relationship is more theoretical than quantitatively defined. There is a growing demand for understanding and prediction of climate sensitive vector borne disease risks especially in regions where meteorological data are lacking. This study aimed at analyzing and quantitatively assessing the seasonal and year-to-year association between climatic factors (rainfall and temperature) and vegetation cover, and its implications for malaria risks in Baringo County, Kenya. Remotely sensed temperature, rainfall, and vegetation data for the period 2004–2015 were used. Poisson regression was used to model the association between malaria cases and climatic and environmental factors for the period 2009–2012, this being the period for which all datasets overlapped. A strong positive relationship was observed between the Normalized Difference Vegetation Index (NDVI) and monthly total precipitation. There was a strong negative relationship between NDVI and minimum temperature. The total monthly rainfall (between 94 -181mm), average monthly minimum temperatures (between 16–21°C) and mean monthly NDVI values lower than 0.35 were significantly associated with malaria incidence rates. Results suggests that a combination of climatic and vegetation greenness thresholds need to be met for malaria incidence to be significantly increased in the county. Planning for malaria control can therefore be enhanced by incorporating these factors in malaria risk mapping.

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

“…darlingi breeding [48], while the study conducted by Ssempiira et al that was located in Uganda, observed that the incidence of malaria was increased with day temperature, however very high temperatures above 29 • C resulted in a decline of malaria incidences [37]. This result was concordant with the study of Amadi et al, which was located at Baringo in Kenya and found that average monthly minimum temperatures between 16.2-21 • C (lag 1-month) made favorable conditions for the increase of the malaria risk [84].…”

“…Additionally, precipitation proved to be another significant predictor in the tropical zone highly associated with malaria occurrence [35,37,40,84]. Contrariwise, the study of Kabaria et al that was conducted in Tanzania (tropical region) was the only one study that claimed precipitation performed poorly as a predictor [38].…”

“…According to Stilianakis et al [30] low wind speed was associated with the existence of infected mosquitoes, while high speed reduced the chances for blood meals, and consequently human infections. Overview of studies that utilized EO derived environmental/climatic variables: LST [32,35,[37][38][39][40]43,45,46,[49][50][51][52][63][64][65][66][67]75,[83][84][85], Air temperature [31,41,44,48,53,59,60,76,81,82,86,87], Temperature Index [61], Soil Temperature [30], SST [59,60], SMT [71], SSTA [32], Precipitaiton (Sat) [32,35,40,43,45,50,51,…”

“…Most of the studies in this review took place in tropical, subtropical, temperate and Sub-Saharan climatic zones as shown in Figure 2. Temperature was one of the most influential parameters affecting the malaria occurrence in tropical regions as shown in [35,37,39,45,46,48,84,88] and only one study [38] located in Tanzania, claimed that temperature performed poorly as a predictor. Lag times and degrees of temperature varied between the studies, which could be explained due to the fact that the studies were located in different climatic zones; Diboulo et al that is located in Burkina Faso, and as such is characterized by a Sub-Saharan climate, has found that the density of the vector An.…”

“…Contrariwise, the study of Kabaria et al that was conducted in Tanzania (tropical region) was the only one study that claimed precipitation performed poorly as a predictor [38]. Lag periods ranged significantly in the case of precipitation as well; Sewe et al found out that all three study regions across the Area of Interest (AOI) located in Western Kenya resulted in different lag periods (0 to 12 weeks) [40] and Amadi et al found positive associations between rainfall and malaria at a 2-month lag time [84]. Kanyangarara et al [77] and Midekisa et al [65] took place in humid subtropical climates; Kanyangarara et al [77] found higher malaria risk during the rainy season at a total monthly rainfall between 94-181 mm, while Midekisa et al found positive associations between rainfall and malaria cases at a lag time of one to three months [65].…”