Malaria vector abundance is associated with house structures in Baringo County, Kenya.

Malaria, a major cause of morbidity and mortality, is the most prevalent vector borne disease in Baringo County; a region which has varied house designs in arid and semi-arid areas. This study investigated the association between house structures and indoor-malaria vector abundance in Baringo County. The density of malaria vectors in houses with open eaves was higher than that for houses with closed eaves. Grass thatched roof houses had higher density of malaria vectors than corrugated iron sheet roofs. Similarly, mud walled houses had higher vector density than other wall types. Houses in the riverine zone were significantly associated with malaria vector abundance (p<0.000) possibly due to more varied house structures. In Kamnarok village within riverine zone, a house made of grass thatched roof and mud wall but raised on stilts with domestic animals (sheep/goats) kept at the lower level had lower mosquito density (5.8 per collection) than ordinary houses made of same materials but at ground level (30.5 mosquitoes per collection), suggestive of a change in behavior of mosquito feeding and resting. House modifications such as screening of eaves, improvement of construction material and building stilted houses can be incorporated in the integrated vector management (IVM) strategy to complement insecticide treated bed nets and indoor residual spray to reduce indoor malaria vector density

Insights into malaria transmission among Anopheles funestus mosquitoes, Kenya

Abstract Background Most malaria vectors belong to species complexes. Sibling species often exhibit divergent behaviors dictating the measures that can be deployed effectively in their control. Despite the importance of the Anopheles funestus complex in malaria transmission in sub-Saharan Africa, sibling species have rarely been identified in the past and their vectoring potential remains understudied. Methods We analyzed 1149 wild-caught An. funestus (senso lato) specimens from 21 sites in Kenya, covering the major malaria endemic areas including western, central and coastal areas. Indoor and outdoor collection tools were used targeting host-seeking and resting mosquitoes. The identity of sibling species, infection with malaria Plasmodium parasites, and the host blood meal sources of engorged specimens were analyzed using PCR-based and sequencing methods. Results The most abundant sibling species collected in all study sites were Anopheles funestus (59.8%) and Anopheles rivulorum (32.4%) among the 1062 successfully amplified specimens of the An. funestus complex. Proportionally, An. funestus dominated in indoor collections whilst An. rivulorum dominated in outdoor collections. Other species identified were Anopheles leesoni (4.6%), Anopheles parensis (2.4%), Anopheles vaneedeni (0.1%) and for the first time in Kenya, Anopheles longipalpis C (0.7%). Anopheles funestus had an overall Plasmodium infection rate of 9.7% (62/636), predominantly Plasmodium falciparum (59), with two infected with Plasmodium ovale and one with Plasmodium malariae. There was no difference in the infection rate between indoor and outdoor collections. Out of 344 An. rivulorum, only one carried P. falciparum. We also detected P. falciparum infection in two non-blood-fed An. longipalpis C (2/7) which is the first record for this species in Kenya. The mean human blood indices for An. funestus and An. rivulorum were 68% (93/136) and 64% (45/70), respectively, with feeding tendencies on a broad host range including humans and domestic animals such as cow, goat, sheep, chicken and pig. Conclusions Our findings underscore the importance of active surveillance through application of molecular approaches to unravel novel parasite-vector associations possibly contributed by cryptic species with important implications for effective malaria control and elimination

Malaria vector abundance is associated with house structures in Baringo County, Kenya - Fig 2

<p><b>Examples of house structures that were sampled in Baringo County: Grass-Mud (A), Iron-Iron (B), Iron-Wood (C), Iron-Stone (D), Bororiet (E), Iron-Mud (F). Eave types: Closed Eave Grass-Mud House (J), Closed Eave Iron-Mud House (K), Open Eave Iron-Mud House (L).</b> Order of naming houses: Roof-Wall.</p

Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya.

BackgroundRift Valley fever (RVF) is a vector-borne zoonotic disease that has an impact on human health and animal productivity. Here, we explore the use of vector presence modelling to predict the distribution of RVF vector species under climate change scenario to demonstrate the potential for geographic spread of Rift Valley fever virus (RVFV).ObjectivesTo evaluate the effect of climate change on RVF vector distribution in Baringo County, Kenya, with an aim of developing a risk map for spatial prediction of RVF outbreaks.MethodologyThe study used data on vector presence and ecological niche modelling (MaxEnt) algorithm to predict the effect of climatic change on habitat suitability and the spatial distribution of RVF vectors in Baringo County. Data on species occurrence were obtained from longitudinal sampling of adult mosquitoes and larvae in the study area. We used present (2000) and future (2050) Bioclim climate databases to model the vector distribution.ResultsModel results predicted potential suitable areas with high success rates for Culex quinquefasciatus, Culex univitattus, Mansonia africana, and Mansonia uniformis. Under the present climatic conditions, the lowlands were found to be highly suitable for all the species. Future climatic conditions indicate an increase in the spatial distribution of Cx. quinquefasciatus and M. africana. Model performance was statistically significant.ConclusionSoil types, precipitation in the driest quarter, precipitation seasonality, and isothermality showed the highest predictive potential for the four species

Map of study area showing mosquito sampling points in Baringo County.

<p>Map of study area showing mosquito sampling points in Baringo County.</p

Cumulative malaria vector abundance in different house structures in Baringo County during the 12-month sampling period.

<p>Cumulative malaria vector abundance in different house structures in Baringo County during the 12-month sampling period.</p

Effect of house type on mosquito abundance while controlling for rainfall and temperature in the riverine and lowland zones.

<p>Effect of house type on mosquito abundance while controlling for rainfall and temperature in the riverine and lowland zones.</p