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Ecological Modeling for Public Health: Predicting Hotspots of Human and Vector Contact in Rural Madagascar

dc.contributor.advisor Nunn, Charles
dc.contributor.author Fitzgerald, Ryan
dc.date.accessioned 2019-04-23T18:19:05Z
dc.date.available 2019-04-23T18:19:05Z
dc.date.issued 2019-04-12
dc.identifier.uri https://hdl.handle.net/10161/18381
dc.description.abstract Vector-borne diseases account for almost one-fifth of all infectious disease cases globally, and are a particularly pressing public health issue in low and middle-income countries. In Madagascar, ticks and flea vectors are known to transmit a wide array of pathogens that impact the health of domestic animals and people, most notoriously in the cases of recent plague outbreaks. This study sought to investigate if ecological factors could be used to predict the abundance of disease vectors across landscapes and within the boundaries of a rural village in northeast Madagascar. Using high resolution ecological data from satellite imagery and human land use data collected by portable GPS devices, maps of overlap between ticks and humans were created, and subsequent exposure measurements were calculated for individuals. Within the village, ecological survey data were used to generate geospatial models of flea abundance. The identification of risk hotspots is a crucial public health interest in low-resource settings like rural Madagascar, as preventative resources can be targeted specifically to these areas, lowering the costs of such interventions. Ecological modeling that incorporates human land use data is an innovative approach that shows potential to shift vector-borne disease outbreak infrastructure away from reactionary control measures and instead towards efficient, proactive methods.
dc.language.iso en_US
dc.subject vector, disease, ecology, rural, predict, health
dc.title Ecological Modeling for Public Health: Predicting Hotspots of Human and Vector Contact in Rural Madagascar
dc.type Honors thesis
dc.department Global Health Institute
duke.embargo.months 0


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