Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm

Adrion, 2012, Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase iii study with longitudinal count data as primary endpoint, BMC Med Res Methodol, 12, 1, 10.1186/1471-2288-12-137 Arab, 2008, Hierarchical spatial models, 425 Augustin, 2013, Space-time modelling of blue ling for fisheries stock management, Environmetrics, 24, 109, 10.1002/env.2196 Azahar, 2011, Temporal analysis of basal stem rot disease in oil palm plantations: An analysis on peat soil, Int J Eng Technol, 11, 96 Bauer, 2016, Bayesian penalized spline models for the analysis of spatio-temporal count data, Stat Med, 35, 1848, 10.1002/sim.6785 Berliner, 1996, Hierarchical bayesian time series models, 15 Besag, 1991, Bayesian image restoration, with two applications in spatial statistics, Ann Inst Stat Math, 43, 1, 10.1007/BF00116466 Blangiardo, 2015 Blangiardo, 2013, Spatial and spatio-temporal models with r-inla, Spat Spatio Temp. Epidemiol, 7, 39, 10.1016/j.sste.2013.07.003 Clayton, 1996, Generalized linear mixed models, 275 Cox, 1981, Statistical analysis of time series: some recent developments [with discussion and reply], Scand J Stat, 93 Datta, 2016, Nonseparable dynamic nearest neighbor gaussian process models for large spatio-temporal data with an application to particulate matter analysis, Ann Appl Stat, 10, 1286, 10.1214/16-AOAS931 Durand-Gasselin, 2005, Possible sources of genetic resistance in oil palm (elaeis guineensis jacq.) to basal stem rot caused by Ganoderma boninense–prospects for future breeding, Mycopathologia, 159, 93, 10.1007/s11046-004-4429-1 Frühwirth-Schnatter, 2006, Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling, Biometrika, 827, 10.1093/biomet/93.4.827 Gneiting, 2007, Probabilistic forecasts, calibration and sharpness, J R Stat Soc Ser B Stat Methodol, 69, 243, 10.1111/j.1467-9868.2007.00587.x Held, 2005, A statistical framework for the analysis of multivariate infectious disease surveillance counts, Stat Modell, 5, 187, 10.1191/1471082X05st098oa Keeling, 2008 Knorr-Held, 2000, Bayesian modelling of inseparable space-time variation in disease risk, Stat Med, 19, 2555, 10.1002/1097-0258(20000915/30)19:17/18<2555::AID-SIM587>3.0.CO;2-# Lawson, 2013 Li, 1994, Time series models based on generalized linear models: some further results, Biometrics, 506, 10.2307/2533393 Martino, 2009 Mercière, 2015, Identification and development of new polymorphic microsatellite markers using genome assembly for Ganoderma boninense, causal agent of oil palm basal stem rot disease, Mycol Prog, 14, 103, 10.1007/s11557-015-1123-2 Meyer S, Held L, Höhle M. Spatio-temporal analysis of epidemic phenomena using the r package surveillance. arXiv preprint arXiv:141104162014. Moncalvo, 1995, Phylogenetic relationships in Ganoderma inferred from the internal transcribed spacers and 25s ribosomal dna sequences, Mycologia, 223, 10.2307/3760908 Paul, 2011, Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts, Stat Med, 30, 1118, 10.1002/sim.4177 Pettit, 1990, The conditional predictive ordinate for the normal distribution, J R Stat Soc Ser B Methodol, 175 Pilotti, 2005, Stem rots of oil palm caused by Ganoderma boninense: Pathogen biology and epidemiology, Mycopathologia, 159, 129, 10.1007/s11046-004-4435-3 Rees, 2009, Basal stem rot of oil palm (elaeis guineensis); mode of root infection and lower stem invasion by Ganoderma boninense, Plant Pathol, 58, 982, 10.1111/j.1365-3059.2009.02100.x Richardson, 2006, Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in yorkshire (uk), Stat Methods Med Res, 15, 385, 10.1191/0962280206sm458oa Riebler, 2012, Estimation and extrapolation of time trends in registry data borrowing strength from related populations, Ann Appl Stat, 6, 304, 10.1214/11-AOAS498 Rue, 2005 Rue, 2009, Approximate Bayesian inference for latent gaussian models by using integrated nested laplace approximations, J R Stat Soc Ser B Stat Methodol, 71, 319, 10.1111/j.1467-9868.2008.00700.x Ruiz-Cárdenas, 2012, Direct fitting of dynamic models using integrated nested laplace approximations INLA, Comput Stat Data Anal, 56, 1808, 10.1016/j.csda.2011.10.024 R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015, https://www.R-project.org/. Schrödle, 2012, Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases, Biometrics, 68, 736, 10.1111/j.1541-0420.2011.01717.x Spiegelhalter, 2002, Bayesian measures of model complexity and fit, J R Stat Soc Ser B Stat Methodol, 64, 583, 10.1111/1467-9868.00353 Tisné, 2017, Identification of Ganoderma disease resistance loci using natural field infection of an oil palm multiparental population, G3 Genes Genomes Genet, 7, 1683, 10.1534/g3.117.041764 Wikle, 2010, A general science-based framework for dynamical spatio-temporal models, Test, 19, 417, 10.1007/s11749-010-0209-z Xia, 1998, Spatio-temporal models with errors in covariates: mapping ohio lung cancer mortality, Stat Med, 17, 2025, 10.1002/(SICI)1097-0258(19980930)17:18<2025::AID-SIM865>3.0.CO;2-M Zheng, 2010, Hierarchical dynamic modeling of outbreaks of mountain pine beetle using partial differential equations, Environmetrics, 21, 801, 10.1002/env.1058