title: Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centre for Infectious Diseases, Division of Pathway Medicine, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Nicola A
authors.initials: NA
authors.lastname: Wardrop
authors.forename: Peter M
authors.initials: PM
authors.lastname: Atkinson
authors.forename: Peter W
authors.initials: PW
authors.lastname: Gething
authors.forename: Eric M
authors.initials: EM
authors.lastname: Fèvre
authors.forename: Kim
authors.initials: K
authors.lastname: Picozzi
authors.forename: Abbas S L
authors.initials: AS
authors.lastname: Kakembo
authors.forename: Susan C
authors.initials: SC
authors.lastname: Welburn
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002980:Climate; D004777:Environment; D005843:Geography; D006801:Humans; D058751:Livestock; D015233:Models, Statistical; D015995:Prevalence; D014353:Trypanosomiasis, African / Q000453:epidemiology*; D014454:Uganda / Q000453:epidemiology