| 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. | |
| dc.date.accessioned | 2018-07-06T07:47:39Z | |
| dc.date.available | 2018-07-06T07:47:39Z | |
| dc.date.issued | 2018-07-05 | |
| dc.identifier.uri | http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0199357 | |
| dc.identifier.uri | https://doi.org/10.1371/journal.pone.0199357 | |
| dc.identifier.uri | http://ir.jooust.ac.ke:8080/xmlui/handle/123456789/1359 | |
| dc.description | https://doi.org/10.1371/journal.pone.0199357 | 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.language.iso | en | en_US |
| dc.publisher | PLOS ONE | 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 |
