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

Jacinter A Amadi,George O Ong’Amo,Silas O Oriaso,Benson B A Estambale,Isaac K Nyamongo,Daniel O Olago,Mark Nanyingi,Jeffrey Shaman

doi:10.1371/journal.pone.0199357

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.

Highlights

Despite current gains in malaria reduction, the disease continues to have devastating health and livelihood impacts especially in sub-Saharan Africa where 88% of global malaria cases and deaths occur [1]
75% of the Kenyan population live in malaria prone zones, mostly in epidemic and seasonal transmission zones [3]
High Normalized Difference Vegetation Index (NDVI) was recorded in the highland zone and low NDVI in the lowland zone

Summary

Introduction

Despite current gains in malaria reduction, the disease continues to have devastating health and livelihood impacts especially in sub-Saharan Africa where 88% of global malaria cases and deaths occur [1]. In Kenya, malaria accounts for about 18% of outpatient consultations and 6% of hospital admissions [2]. 75% of the Kenyan population live in malaria prone zones, mostly in epidemic and seasonal transmission zones [3]. Epidemic transmission is influenced by a complex of interacting factors including vector, parasite, climate, environmental and socioeconomic factors [4]. Malaria prevention and control efforts in Kenya are biased towards lake endemic and highland epidemic zones while seasonal transmission zones remain largely neglected [5]. Malaria is among the most prevalent diseases in seasonal transmission zones such as Baringo County [3]

Objectives

Methods

Results

Discussion

Conclusion