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Thesis

Improving geospatial models of risk for vector-borne, zoonotic diseases

Abstract:

Public health surveillance data are often incomplete, particularly where resources are lacking, but geospatial models can help to fill the gaps by providing estimates where data are sparse. By combining information on locations where diseases have been recorded with geographic data on environmental and socioeconomic covariates known to affect disease transmission using machine-learning models (such as boosted regression trees), niche modelling can generate fine-resolution, evidence-ba...

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Role:
Supervisor
Role:
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Funding agency for:
Freya Shearer
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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