Abstract
We modeled surface wetness at high resolution, using a dynamic hydrology model, to predict flood and swamp water mosquito abundances. Historical meteorologic data, as well as topographic, soil, and vegetation data, were used to model surface wetness and identify potential fresh and swamp water breeding habitats in two northern New Jersey watersheds. Surface wetness was positively associated with the subsequent abundance of the dominant floodwater mosquito species, Aedes vexans, and the swamp water species, Anopheles walkeri. The subsequent abundance of Culex pipiens, a species that breeds in polluted, eutrophic waters, was negatively correlated with local modeled surface wetness. These associations permit real-time monitoring and forecasting of these floodwater and nonfloodwater species at high spatial and temporal resolution. These predictions will enable public health agencies to institute control measures before the mosquitoes emerge as adults, when their role as transmitters of disease comes into play.
Highlights
I n their efforts to control mosquitoes and mosquito-borne diseases, public health officials would benefit if they could identify the locations of mosquito populations
This study demonstrated the potential application of dynamic hydrology models in epidemiologic monitoring; the model was coarse in both temporal and spatial resolution and lacked the means for assessing the spatial distribution of wetness across the land surface
We present an example of how flood and swamp water mosquito abundance can be predicted in real time at high spatial resolution through application of a more detailed dynamic hydrology model [10]
Summary
I n their efforts to control mosquitoes and mosquito-borne diseases, public health officials would benefit if they could identify the locations of mosquito populations. In an effort to circumvent this shortcoming, researchers have attempted to account for fluctuations in mosquito populations through the monitoring of environmental conditions Many such studies have associated the measured abundance of vectors or vector-borne disease incidence with satellite imaging [1,2,3,4,5,6,7,8]. We present an example of how flood and swamp water mosquito abundance can be predicted in real time at high spatial resolution through application of a more detailed dynamic hydrology model [10] This model accounts for topographic variability and its control over soil moisture heterogeneity and Columbia University, Palisades, New York, USA runoff within a watershed. The model tracks the expansion and contraction of lowland saturated zones within this spatial framework and generates a picture of the variable wetness of the land surface as it changes in time
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