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.

BackgroundThe decline in global malaria cases is attributed to intensified utilization of primary vector control interventions and artemisinin-based combination therapies (ACTs). These strategies are inadequate in many rural areas, thus adopting locally appropriate integrated malaria control strategies is imperative in these heterogeneous settings. This study aimed at investigating trends and local knowledge on malaria and to develop a framework for malaria control for communities in Baringo, Kenya.MethodsClinical malaria cases obtained from four health facilities in the riverine and lowland zones were used to analyse malaria trends for the 2005–2014 period. A mixed method approach integrating eight focus group discussions, 12 key informant interviews, 300 survey questionnaires and two stakeholders’ consultative forums were used to assess local knowledge on malaria risk and develop a framework for malaria reduction.ResultsMalaria cases increased significantly during the 2005–2014 period (tau = 0.352; p < 0.001) in the riverine zone. March, April, May, June and October showed significant increases compared to other months. Misconceptions about the cause and mode of malaria transmission existed. Gender-segregated outdoor occupation such as social drinking, farm activities, herding, and circumcision events increased the risk of mosquito bites. A positive relationship occurred between education level and opinion on exposure to malaria risk after dusk (χ2 = 2.70, p < 0.05). There was over-reliance on bed nets, yet only 68% (204/300) of respondents owned at least one net. Complementary malaria control measures were under-utilized, with 90% of respondents denying having used either sprays, repellents or burnt cow dung or plant leaves over the last one year before the study was conducted. Baraza, radios, and mobile phone messages were identified as effective media for malaria information exchange. Supplementary strategies identified included unblocking canals, clearing Prosopis bushes, and use of community volunteers and school clubs to promote social behaviour change.ConclusionsThe knowledge gap on malaria transmission should be addressed to minimize the impacts and enhance uptake of appropriate malaria management mechanisms. Implementing community-based framework can support significant reductions in malaria prevalence by minimizing both indoor and outdoor malaria transmissions.

Anopheles gambiae s.l. (Diptera: Culicidae) is responsible for the transmission of the devastating Plasmodium falciparum (Haemosporida: Plasmodiidae) strain of malaria in Africa. This study investigated the relationship between climate and environmental conditions and An. gambiae s.l. larvae abundance and modelled the larval distribution of this species in Baringo County, Kenya. Mosquito larvae were collected using a 350-mL dipper and a pipette once per month from December 2015 to December 2016. A random forest algorithm was used to generate vegetation cover classes. A negative binomial regression was used to model the association between remotely sensed climate (rainfall and temperature) and environmental (vegetation cover, vegetation health, topographic wetness and slope) factors and An. gambiae s.l. for December 2015. Anopheles gambiae s.l. was significantly more frequent in the riverine zone (P < 0.05, r = 0.59) compared with the lowland zone. Rainfall (b = 6.22, P < 0.001), slope (b = - 4.81, P = 0.012) and vegetation health (b = - 5.60, P = 0.038) significantly influenced the distribution of An. gambiae s.l. larvae. High An. gambiae s.l. abundance was associated with cropland and wetland environments. Effective malaria control will require zone-specific interventions such as a focused dry season vector control strategy in the riverine zone.