Using machine learning to understand microgeographic determinants of the Zika vector, Aedes aegypti.

Using machine learning to understand microgeographic determinants of the Zika vector, Aedes aegypti.

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There are limited data on why the 2016 Zika outbreak in Miami-Dade County, Florida was confined to certain neighborhoods.In this research, Aedes aegypti, the primary vector of Zika virus, are studied to examine neighborhood-level differences in their population dynamics and underlying processes.Weekly mosquito data were acquired from the Miami-Dade County Mosquito Control Division from 2016 to 2020 from 172 makita rf1101 traps deployed around Miami-Dade County.

Using random forest, a machine learning method, predictive models of spatiotemporal dynamics of Ae.aegypti in response to meteorological conditions and neighborhood-specific socio-demographic and physical characteristics, such as land-use and land-cover type and income level, were created.The study area was divided into two groups: areas affected by local transmission of Zika during the 2016 outbreak and unaffected areas.

Ae.aegypti populations in areas affected by Zika were more strongly influenced by 14- and 21-day lagged weather conditions.In the unaffected areas, mosquito populations were more strongly influenced by land-use and day-of-collection weather conditions.

There are neighborhood-scale differences in Ae.aegypti population dynamics.These differences in turn influence vector-borne disease diffusion in a region.

These results have implications for vector control experts to lead neighborhood-specific vector control strategies and for epidemiologists to guide vector-borne disease risk preparations, especially for containing the spread of vector-borne disease in jouer soft focus hydrate & setting powder response to ongoing climate change.