title: Spatial generalised linear mixed models based on distances.
abstract: Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time.
authors.affiliation: Department of Statistics, Faculty of Sciences, National University of Colombia, Bogotá, Colombia oomelom@unal.edu.co.
authors.forename: Oscar O
authors.initials: OO
authors.lastname: Melo
authors.affiliation: Department of Mathematics, University Jaume I, Castellon, Spain.
authors.forename: Jorge
authors.initials: J
authors.lastname: Mateu
authors.affiliation: Faculty of Engineering, District University Francisco José de Caldas, Bogotá, Colombia.
authors.forename: Carlos E
authors.initials: CE
authors.lastname: Melo
mesh_terms: D000818:Animals; D002163:Cameroon / Q000453:epidemiology; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008117:Loa; D008118:Loiasis / Q000453:epidemiology / Q000469:parasitology; D008390:Markov Chains; D009010:Monte Carlo Method; D015995:Prevalence; D012306:Risk
keywords: Markov chain Monte Carlo; distance-based methods; epidemiological study; spatial generalised linear mixed models; spatial interpolation