Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Public Library of Science
2010
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/61886 |
| _version_ | 1855532738372698112 |
|---|---|
| author | Wardrop, N.A. Atkinson, P.M. Gething, P.W. Fèvre, Eric M. Picozzi, K. Kakembo, A.S. |
| author_browse | Atkinson, P.M. Fèvre, Eric M. Gething, P.W. Kakembo, A.S. Picozzi, K. Wardrop, N.A. |
| author_facet | Wardrop, N.A. Atkinson, P.M. Gething, P.W. Fèvre, Eric M. Picozzi, K. Kakembo, A.S. |
| author_sort | Wardrop, N.A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace61886 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2010 |
| publishDateRange | 2010 |
| publishDateSort | 2010 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace618862024-05-23T19:41:36Z Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis Wardrop, N.A. Atkinson, P.M. Gething, P.W. Fèvre, Eric M. Picozzi, K. Kakembo, A.S. trypanosomiasis animal diseases livestock zoonoses 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. 2010-12-21 2015-03-25T09:16:24Z 2015-03-25T09:16:24Z Journal Article https://hdl.handle.net/10568/61886 en Open Access Public Library of Science Wardrop, N.A., Atkinson, P.M., Gething, P.W., Fevre, E.M., Picozzi, K. and Kakembo, A.S. 2010. Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis. PLoS Neglected Tropical Diseases 4(12): e914. |
| spellingShingle | trypanosomiasis animal diseases livestock zoonoses Wardrop, N.A. Atkinson, P.M. Gething, P.W. Fèvre, Eric M. Picozzi, K. Kakembo, A.S. Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis |
| title | Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis |
| title_full | Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis |
| title_fullStr | Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis |
| title_full_unstemmed | Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis |
| title_short | Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis |
| title_sort | bayesian geostatistical analysis and prediction of rhodesian human african trypanosomiasis |
| topic | trypanosomiasis animal diseases livestock zoonoses |
| url | https://hdl.handle.net/10568/61886 |
| work_keys_str_mv | AT wardropna bayesiangeostatisticalanalysisandpredictionofrhodesianhumanafricantrypanosomiasis AT atkinsonpm bayesiangeostatisticalanalysisandpredictionofrhodesianhumanafricantrypanosomiasis AT gethingpw bayesiangeostatisticalanalysisandpredictionofrhodesianhumanafricantrypanosomiasis AT fevreericm bayesiangeostatisticalanalysisandpredictionofrhodesianhumanafricantrypanosomiasis AT picozzik bayesiangeostatisticalanalysisandpredictionofrhodesianhumanafricantrypanosomiasis AT kakemboas bayesiangeostatisticalanalysisandpredictionofrhodesianhumanafricantrypanosomiasis |