Journal ArticleDOI
Log Gaussian Cox processes and spatially aggregated disease incidence data.
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The continuous model proposed here uses a data augmentation step to sample from the posterior distribution of the exact locations at each step of an Markov chain Monte Carlo algorithm, and models the exactly locations with an log-Gaussian Cox process.Abstract:
This article presents a methodology for modeling aggregated disease incidence data with the spatially continuous log-Gaussian Cox process. Statistical models for spatially aggregated disease incidence data usually assign the same relative risk to all individuals in the same reporting region (census areas or postal regions). A further assumption that the relative risks in two regions are independent given their neighbor's risks (the Markov assumption) makes the commonly used Besag-York-Mollie model computationally simple. The continuous model proposed here uses a data augmentation step to sample from the posterior distribution of the exact locations at each step of an Markov chain Monte Carlo algorithm, and models the exact locations with an log-Gaussian Cox process. A simulation study shows the log-Gaussian Cox process model consistently outperforming the Besag-York-Mollie model. The method is illustrated by making inference on the spatial distribution of syphilis risk in North Carolina. The effect of several known social risk factors are estimated, and areas with risk well in excess of that expected given these risk factors are identified.read more
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Journal ArticleDOI
Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000–17: a spatial and temporal modelling study
Katherine E. Battle,Tim C.D. Lucas,Michele Nguyen,Rosalind E. Howes,Anita Nandi,Katherine A. Twohig,Daniel A. Pfeffer,Ewan Cameron,Puja C Rao,Daniel C Casey,Harry S. Gibson,Jennifer Rozier,Ursula Dalrymple,Suzanne H. Keddie,Emma L. Collins,Joseph R Harris,Carlos A. Guerra,Michael P Thorn,Donal Bisanzio,Donal Bisanzio,Nancy Fullman,Chantal Huynh,Xie Rachel Kulikoff,Michael Kutz,Alan D. Lopez,Ali H. Mokdad,Mohsen Naghavi,Grant Nguyen,Katya Anne Shackelford,Theo Vos,Haidong Wang,Stephen S Lim,Christopher J L Murray,Ric N. Price,Ric N. Price,J. Kevin Baird,David L. Smith,Samir Bhatt,Daniel J. Weiss,Simon I. Hay,Peter W. Gething +40 more
TL;DR: These results support global monitoring systems and can inform the optimisation of diagnosis and treatment where P vivax has most impact, and form the malaria estimates for the Global Burden of Disease 2017 study.
Journal ArticleDOI
Spatial and spatio-temporal Log-Gaussian Cox processes:extending the geostatistical paradigm
TL;DR: This paper first describes the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data, and discusses inference, with a particular focus on the computational challenges of likelihood-based inference.
Journal ArticleDOI
A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)
TL;DR: In this paper, a toolbox for fitting complex models to realistic spatial point pattern data is presented, where models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates are considered.
Journal ArticleDOI
Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R
TL;DR: A suite of R functions provides an extensible framework for inferring covariate effects as well as the parameters of the latent field in log-Gaussian Cox processes and presents methods for Bayesian inference in two further classes of model based on the log- Gaussian Cox process.
Journal ArticleDOI
Locally adaptive spatial smoothing using conditional auto-regressive models
Duncan Lee,Richard Mitchell +1 more
TL;DR: The methodology proposed is an extension to the class of conditional auto‐regressive priors, which allow them to capture such localized spatial correlation and to identify step changes, and takes the form of an iterative algorithm.
References
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