title: Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence.
abstract: Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.
  
  
  
  We performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.
  
  
  
  None of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.
authors.affiliation: Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, AE, Enschede, The Netherlands.
authors.forename: Andrea L
authors.identifier: 0000-0003-4500-1712
authors.initials: AL
authors.lastname: Araujo Navas
authors.affiliation: Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, AE, Enschede, The Netherlands.
authors.forename: Nicholas A S
authors.initials: NA
authors.lastname: Hamm
authors.affiliation: UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton QLD, Australia.|Child Health and Environment Program, Child Health Research Centre, The University of Queensland, South Brisbane QLD, Australia.
authors.forename: Ricardo J
authors.initials: RJ
authors.lastname: Soares Magalhães
authors.affiliation: Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, AE, Enschede, The Netherlands.
authors.forename: Alfred
authors.initials: A
authors.lastname: Stein
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D006302:Health Services Research; D006373:Helminthiasis / Q000453:epidemiology* / Q000469:parasitology / Q000635:transmission; D006376:Helminths / Q000235:genetics / Q000302:isolation & purification; D006801:Humans; D015233:Models, Statistical; D012547:Schistosoma / Q000235:genetics / Q000302:isolation & purification; D012552:Schistosomiasis / Q000453:epidemiology* / Q000469:parasitology / Q000635:transmission; D012987:Soil / Q000469:parasitology*
chemical_list: D012987:Soil