Bayesian Geostatistical Analysis and Prediction of Rhodesian Human African Trypanosomiasis
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Date
2010
Authors
Wardrop, Nicola A.
Atkinson, Peter M.
Gething, Peter W.
Fèvre, Eric M.
Picozzi, Kim
Kakembo, Abbas S. L.
Welburn, Susan C.
Journal Title
Journal ISSN
Volume Title
Publisher
PLoS
Abstract
The persistent spread of Rhodesian human African trypanosomiasis (HAT) in Uganda in recent years has
increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the
evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial
distribution of the disease.
One recent study used simple logistic regression methods to explore the relationship between prevalence of
Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of
Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock.
Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model
parameters and undertake predictive mapping for risk of high HAT prevalence in future.
Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian
framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters
we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and
significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock
movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects.
Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of
untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial
distribution of Rhodesian HAT and the linkages between the disease’s distribution and minimum land surface temperature
have also been confirmed via the application of these methods.
Predictive mapping indicates an increased risk of high HAT prevalence in the future in areas surrounding
livestock markets, demonstrating the importance of livestock trading for continuing disease spread. Adherence to
government policy to treat livestock at the point of sale is essential to prevent the spread of sleeping sickness in Uganda.
Description
Keywords
Bayesian Geostatistical Analysis, Rhodesian Human African Trypanosomiasis, Prediction
Citation
Wardrop NA, Atkinson PM, Gething PW, Fe`vre EM, Picozzi K, et al. (2010) Bayesian Geostatistical Analysis and Prediction of Rhodesian Human African Trypanosomiasis. PLoS Negl Trop Dis 4(12): e914. doi:10.1371/journal.pntd.0000914