Thesis icon

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...

Expand abstract

Actions


Access Document


Files:

Authors


More by this author
Division:
MSD
Role:
Author

Contributors

Role:
Supervisor
Role:
Supervisor
Role:
Supervisor
More from this funder
Funding agency for:
Shearer, F
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
UUID:
uuid:cfe8ffa9-453b-4e10-9009-e387a39db6de
Deposit date:
2018-05-25

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP