Modeling the seasonality of Anopheles gambiae s.s. biting rates in a South Benin sanitary zone.

Background: Efficient malaria vector control requires knowledge of spatio-temporal vector dynamics. We have classified village groups according to the biting rate profiles of both Anopheles coluzzii and An. gambiae, the major malaria vectors in these villages.

Methods: Mosquitoes were captured by human bait in 28 South Benin villages during 2009. Both An. coluzzii and An. gambiae counts in each village were standardized to focus on changes in the vector biting rate over time. Latent class trajectory modeling, allowing for random intercept at the 'village' level, was adjusted to standardized values.

Results: The villages could be classified into two groups with distinct vector biting rate profiles (continuous/transient). This classification helped creating a map of vector biting rates in the area. The biting rate profiles were found to be significantly correlated with mean rainfall, altitude, average number of larval sites, and average normalized difference vegetation index.

Conclusions: In highly malaria-prone regions, knowledge of vector biting rate profiles is important to improve vector control interventions. A similar methodology may be applied to study the biting rate profiles of other vector-borne infections.