Spatially explicit analyses of anopheline mosquitoes indoor resting density: implications for malaria control

Background

The question of sampling and spatial aggregation of malaria vectors is central to vector control efforts and estimates of transmission. Spatial patterns of anopheline populations are complex because mosquitoes'' habitats and behaviors are strongly heterogeneous. Analyses of spatially referenced counts provide a powerful approach to delineate complex distribution patterns, and contributions of these methods in the study and control of malaria vectors must be carefully evaluated.

Methodology/Principal Findings

We used correlograms, directional variograms, Local Indicators of Spatial Association (LISA) and the Spatial Analysis by Distance IndicEs (SADIE) to examine spatial patterns of Indoor Resting Densities (IRD) in two dominant malaria vectors sampled with a 5×5 km grid over a 2500 km² area in the forest domain of Cameroon. SADIE analyses revealed that the distribution of Anopheles gambiae was different from regular or random, whereas there was no evidence of spatial pattern in Anopheles funestus (Ia = 1.644, Pa<0.05 and Ia = 1.464, Pa>0.05, respectively). Correlograms and variograms showed significant spatial autocorrelations at small distance lags, and indicated the presence of large clusters of similar values of abundance in An. gambiae while An. funestus was characterized by smaller clusters. The examination of spatial patterns at a finer spatial scale with SADIE and LISA identified several patches of higher than average IRD (hot spots) and clusters of lower than average IRD (cold spots) for the two species. Significant changes occurred in the overall spatial pattern, spatial trends and clusters when IRDs were aggregated at the house level rather than the locality level. All spatial analyses unveiled scale-dependent patterns that could not be identified by traditional aggregation indices.

Conclusions/Significance

Our study illustrates the importance of spatial analyses in unraveling the complex spatial patterns of malaria vectors, and highlights the potential contributions of these methods in malaria control.