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

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dc.contributor.author Amadi, Jacinter A.
dc.contributor.author Olago, Daniel O.
dc.contributor.author Ong’amo, George O.
dc.contributor.author Oriaso, Silas O
dc.contributor.author Nanyingi, Mark
dc.contributor.author Nyamongo, Isaac K
dc.contributor.author Estambale, Benson B. A.
dc.date.accessioned 2022-04-11T13:06:41Z
dc.date.available 2022-04-11T13:06:41Z
dc.date.issued 2018-07-05
dc.identifier.citation Amadi JA, Olago DO, Ong’amo GO, Oriaso SO, Nanyingi M, Nyamongo IK, et al. (2018) Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya. PLoS ONE 13(7): e0199357. https://doi.org/ 10.1371/journal.pone.0199357 en_US
dc.identifier.issn e0199357
dc.identifier.uri https://doi.org/10.1371/journal.pone.0199357
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/559
dc.description Research Article en_US
dc.description.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. en_US
dc.description.sponsorship The study received financial support from the WHO’s Special Programme for Research and Training in Tropical Diseases (TDR) through a grant agreement with the International Development Research Centre of Canada (106905- 00). The funded Project No. B20278 was a collaborative research between the University of Nairobi and Jaramogi Oginga Odinga University of Science and Technology, Kenya en_US
dc.language.iso en en_US
dc.publisher PLoS ONE en_US
dc.subject implications for malaria risk en_US
dc.subject Sensitivity of vegetation en_US
dc.subject climate variability en_US
dc.title Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya en_US
dc.type Article en_US

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