Abstract:
Dengue (DEN) and yellow fever (YF) are re-emerging in East Africa, with contributing drivers
to this trend being unplanned urbanization and increasingly adaptable anthropophilic
Aedes (Stegomyia) vectors. Entomological risk assessment of these diseases remains
scarce for much of East Africa and Kenya even in the dengue fever-prone urban coastal
areas. Focusing on major cities of Kenya, we compared DEN and YF risk in Kilifi County
(DEN-outbreak-prone), and Kisumu and Nairobi Counties (no documented DEN outbreaks).
We surveyed water-holding containers for mosquito immature (larvae/pupae) indoors and
outdoors from selected houses during the long rains, short rains and dry seasons (100
houses/season) in each County from October 2014-June 2016. House index (HI), Breteau
index (BI) and Container index (CI) estimates based on Aedes (Stegomyia) immature infestations
were compared by city and season. Aedes aegypti and Aedes bromeliae were the
main Stegomyia species with significantly more positive houses outdoors (212) than indoors
(88) (n = 900) (χ2 = 60.52, P < 0.0001). Overall, Ae. aegypti estimates of HI (17.3 vs 11.3)
and BI (81.6 vs 87.7) were higher in Kilifi and Kisumu, respectively, than in Nairobi (HI, 0.3;
BI,13). However, CI was highest in Kisumu (33.1), followed by Kilifi (15.1) then Nairobi (5.1).
Aedes bromeliae indices were highest in Kilifi, followed by Kisumu, then Nairobi with HI (4.3,
0.3, 0); BI (21.3, 7, 0.7) and CI (3.3, 3.3, 0.3), at the respective sites. HI and BI for both species
were highest in the long rains, compared to the short rains and dry seasons. We found
strong positive correlations between the BI and CI, and BI and HI for Ae. aegypti, with the
most productive container types being jerricans, drums, used/discarded containers and
tyres. On the basis of established vector index thresholds, our findings suggest low-tomedium
risk levels for urban YF and high DEN risk for Kilifi and Kisumu, whereas for Nairobi YF risk was low while DEN risk levels were low-to-medium. The study provides a baseline
for future vector studies needed to further characterise the observed differential risk patterns
by vector potential evaluation. Identified productive containers should be made the focus of
community-based targeted vector control programs.
