Graduation Year

2016

Degree

M.S.P.H.

Degree Name

MS in Public Health (M.S.P.H.)

Degree Granting Department

Global Health

Major Professor

Thomas Unnasch, Ph.D.

Committee Member

Benjamin Jacob, Ph.D.

Committee Member

Hesborn Wao, Ph.D.

Keywords

Anthropogenic boreholes, Dry-seasonal rivers, Interpolating a spectral signature, Remote sensing model, Tropical savanna climate

Abstract

A lack of surveillance systems is an impediment to public health intervention for perennial vector-borne disease transmission in northern tropical savanna region of Kenya. The population in this area are mostly poor nomadic pastoralists with little acquired functional immunity to Plasmodium falciparum, due to infrequent challenges with the parasite. A common characteristic in tropical savanna climatic zone is the availability of riverbeds that have anthropogenic boreholes that provide malaria vector mosquitoes, such as Anopheles gambiae s.l and Anopheles funestus, with aquatic refuge habitats for proliferation and endemic transmission to proximity human households during the dry-season. Unfortunately, currently there have been no entomological investigations employing field or remotely sensed data that can characterize and model anthropogenic borehole habitats focusing on the dry-land ecology of immature Anopheles mosquitoes in sub-Sahara Africa. The goal of this investigation was three-fold: (I) to employ WorldView-3 (0.31 meter spatial resolution) visible and near infra-red waveband sensor data to image sub-Saharan land cover associated with vector-borne disease transmission; (II) to remotely identify anthropogenic boreholes in three riverbeds that were surveyed to determine whether they provide malaria vectors with refuge habitat and maintain their population during the dry season in Chemolingot, Kenya, and (III) to obtain a radiometric/spectral signature model representing boreholes from the remotely-sensed data. The signature model was then interpolated to predict unknown locations of boreholes with the same spectral signature in Nginyang Riverbed, Kenya. Ground validation studies were subsequently conducted to assess model’s precision based on sensitivity and specificity tests.

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