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Article ; Online: Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University.

Laxton, Megan Ruth / Nightingale, Glenna / Lindgren, Finn / Sivakumaran, Arjuna / Othieno, Richard

2023 Volume 18, Issue 11, Page(s) e0291348

Abstract: The impact of the COVID-19 pandemic on University students has been a topic of fiery debate and of public health research. This study demonstrates the use of a combination of spatiotemporal epidemiological models to describe the trends in COVID-19 ... ...

Abstract	The impact of the COVID-19 pandemic on University students has been a topic of fiery debate and of public health research. This study demonstrates the use of a combination of spatiotemporal epidemiological models to describe the trends in COVID-19 positive cases on spatial, temporal and spatiotemporal scales. In addition, this study proposes new epidemiological metrics to describe the connectivity between observed positivity; an analogous metric to the R number in conventional epidemiology. The proposed indices, Rspatial, Rspatiotemporal and Rscaling will aim to improve the characterisation of the spread of infectious disease beyond that of the COVID-19 framework and as a result inform relevant public health policy. Apart from demonstrating the application of the novel epidemiological indices, the key findings in this study are: firstly, there were some Intermediate Zones in Edinburgh with noticeably high levels of COVID-19 positivity, and that the first outbreak during the study period was observed in Dalry and Fountainbridge. Secondly, the estimation of the distance over which the COVID-19 counts at the halls of residence are spatially correlated (or related to each other) was found to be 0.19km (0.13km to 0.27km) and is denoted by the index, Rspatial. This estimate is useful for public health policy in this setting, especially with contact tracing. Thirdly, the study indicates that the association between the surrounding community level of COVID-19 positivity (Intermediate Zones in Edinburgh) and that of the University of Edinburgh's halls of residence was not statistically significant. Fourthly, this study reveals that relatively high levels of COVID-19 positivity were observed for halls for which higher COVID-19 fines were issued (Spearman's correlation coefficient = 0.34), and separately, for halls which were non-ensuite relatively to those which were not (Spearman's correlation coefficient = 0.16). Finally, Intermediate Zones with the highest positivity were associated with student residences that experienced relatively high COVID-19 positivity (Spearman's correlation coefficient = 0.27).
MeSH term(s)	Humans ; COVID-19/epidemiology ; Universities ; Pandemics ; Public Health ; Students
Language	English
Publishing date	2023-11-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0291348
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Joint model for longitudinal and spatio-temporal survival data

Medina-Olivares, Victor / Lindgren, Finn / Calabrese, Raffaella / Crook, Jonathan

2023

Abstract: In credit risk analysis, survival models with fixed and time-varying covariates are widely used to predict a borrower's time-to-event. When the time-varying drivers are endogenous, modelling jointly the evolution of the survival time and the endogenous ... ...

Abstract	In credit risk analysis, survival models with fixed and time-varying covariates are widely used to predict a borrower's time-to-event. When the time-varying drivers are endogenous, modelling jointly the evolution of the survival time and the endogenous covariates is the most appropriate approach, also known as the joint model for longitudinal and survival data. In addition to the temporal component, credit risk models can be enhanced when including borrowers' geographical information by considering spatial clustering and its variation over time. We propose the Spatio-Temporal Joint Model (STJM) to capture spatial and temporal effects and their interaction. This Bayesian hierarchical joint model reckons the survival effect of unobserved heterogeneity among borrowers located in the same region at a particular time. To estimate the STJM model for large datasets, we consider the Integrated Nested Laplace Approximation (INLA) methodology. We apply the STJM to predict the time to full prepayment on a large dataset of 57,258 US mortgage borrowers with more than 2.5 million observations. Empirical results indicate that including spatial effects consistently improves the performance of the joint model. However, the gains are less definitive when we additionally include spatio-temporal interactions.
Keywords	Quantitative Finance - Risk Management ; Computer Science - Computational Engineering ; Finance ; and Science ; Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	330
Publishing date	2023-11-07
Publishing country	us
Document type	Book ; Online
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Article ; Online: Optimisation of the core subset for the APY approximation of genomic relationships

Pocrnic, Ivan / Lindgren, Finn / Tolhurst, Daniel / Herring, William O. / Gorjanc, Gregor

Genet Sel Evol. 2022 Dec., v. 54, no. 1 p.76-76

2022

Abstract: BACKGROUND: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed ... ...

Abstract	BACKGROUND: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed to address this challenge, such as the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of the genomic relationship matrix. This partitioning is often done at random. While APY is a good approximation of the full model, random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. METHODS: We derived a novel algorithm for optimising the core subset based on a conditional genomic relationship matrix or a conditional single nucleotide polymorphism (SNP) genotype matrix. We compared the accuracy of genomic predictions with different core subsets for simulated and real pig data sets. The core subsets were constructed (1) at random, (2) based on the diagonal of the genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we visualise the population structure of the genotyped animals with linear Principal Component Analysis and non-linear Uniform Manifold Approximation and Projection. RESULTS: All core subset constructions performed equally well when the number of core animals captured most of the variation in the genomic relationships, both in simulated and real data sets. When the number of core animals was not sufficiently large, there was substantial variability in the results with the random construction but no variability with the conditional construction. Visualisation of the population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. CONCLUSIONS: Our results confirm that the size of the core subset in APY is critical. Furthermore, the results show that the core subset can be optimised with the conditional algorithm that achieves an optimal and repeatable spread of core animals across the domain of genotyped animals.
Keywords	algorithms ; genomics ; genotype ; genotyping ; models ; population structure ; principal component analysis ; single nucleotide polymorphism ; swine
Language	English
Dates of publication	2022-12
Size	p. 76.
Publishing place	BioMed Central
Document type	Article ; Online
ZDB-ID	1005838-2
ISSN	1297-9686 ; 0754-0264 ; 0999-193X
ISSN (online)	1297-9686
ISSN	0754-0264 ; 0999-193X
DOI	10.1186/s12711-022-00767-x
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Article ; Online: Optimisation of the core subset for the APY approximation of genomic relationships.

Pocrnic, Ivan / Lindgren, Finn / Tolhurst, Daniel / Herring, William O / Gorjanc, Gregor

Genetics, selection, evolution : GSE

2022 Volume 54, Issue 1, Page(s) 76

Abstract: Background: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed ... ...

Abstract	Background: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed to address this challenge, such as the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of the genomic relationship matrix. This partitioning is often done at random. While APY is a good approximation of the full model, random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. Methods: We derived a novel algorithm for optimising the core subset based on a conditional genomic relationship matrix or a conditional single nucleotide polymorphism (SNP) genotype matrix. We compared the accuracy of genomic predictions with different core subsets for simulated and real pig data sets. The core subsets were constructed (1) at random, (2) based on the diagonal of the genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we visualise the population structure of the genotyped animals with linear Principal Component Analysis and non-linear Uniform Manifold Approximation and Projection. Results: All core subset constructions performed equally well when the number of core animals captured most of the variation in the genomic relationships, both in simulated and real data sets. When the number of core animals was not sufficiently large, there was substantial variability in the results with the random construction but no variability with the conditional construction. Visualisation of the population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. Conclusions: Our results confirm that the size of the core subset in APY is critical. Furthermore, the results show that the core subset can be optimised with the conditional algorithm that achieves an optimal and repeatable spread of core animals across the domain of genotyped animals.
MeSH term(s)	Swine ; Animals ; Models, Genetic ; Genome ; Genomics/methods ; Genotype ; Algorithms
Language	English
Publishing date	2022-11-22
Publishing country	France
Document type	Journal Article
ZDB-ID	1005838-2
ISSN	1297-9686 ; 0754-0264 ; 0999-193X
ISSN (online)	1297-9686
ISSN	0754-0264 ; 0999-193X
DOI	10.1186/s12711-022-00767-x
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Article ; Online: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.

Ribeiro, Rita / Matthiopoulos, Jason / Lindgren, Finn / Tello, Carlos / Zariquiey, Carlos M / Valderrama, William / Rocke, Tonie E / Streicker, Daniel G

Proceedings. Biological sciences

2023 Volume 290, Issue 2011, Page(s) 20231739

Abstract: Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies ... ...

Abstract	Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies spillover to humans and livestock. We inferred the spatial distribution of vampire bat roosts while accounting for observation effort and environmental effects by fitting a log Gaussian Cox process model to the locations of 563 roosts in three regions of Peru. Our model explained 45% of the variance in the observed roost distribution and identified environmental drivers of roost establishment. When correcting for uneven observation effort, our model estimated a total of 2340 roosts, indicating that undetected roosts (76%) exceed known roosts (24%) by threefold. Predicted hotspots of undetected roosts in rabies-free areas revealed high-risk areas for future viral incursions. Using the predicted roost distribution to inform a spatial model of rabies spillover to livestock identified areas with disproportionate underreporting and indicated a higher rabies burden than previously recognized. We provide a transferrable approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis.
MeSH term(s)	Animals ; Humans ; Rabies/epidemiology ; Rabies/veterinary ; Rabies/prevention & control ; Rabies virus ; Chiroptera ; Zoonoses ; Latin America ; Livestock
Language	English
Publishing date	2023-11-22
Publishing country	England
Document type	Journal Article
ZDB-ID	209242-6
ISSN	1471-2954 ; 0080-4649 ; 0962-8452 ; 0950-1193
ISSN (online)	1471-2954
ISSN	0080-4649 ; 0962-8452 ; 0950-1193
DOI	10.1098/rspb.2023.1739
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Article: Latent Gaussian random field mixture models

Bolin, David / Wallin, Jonas / Lindgren, Finn

Elsevier B.V. Computational statistics & data analysis. 2019 Feb., v. 130

2019

Abstract: For many problems in geostatistics, land cover classification, and brain imaging the classical Gaussian process models are unsuitable due to sudden, discontinuous, changes in the data. To handle data of this type, we introduce a new model class that ... ...

Abstract	For many problems in geostatistics, land cover classification, and brain imaging the classical Gaussian process models are unsuitable due to sudden, discontinuous, changes in the data. To handle data of this type, we introduce a new model class that combines discrete Markov random fields (MRFs) with Gaussian Markov random fields. The model is defined as a mixture of several, possibly multivariate, Gaussian Markov random fields. For each spatial location, the discrete MRF determines which of the Gaussian fields in the mixture that is observed. This allows for the desired discontinuous changes of the latent processes, and also gives a probabilistic representation of where the changes occur spatially. By combining stochastic gradient minimization with sparse matrix techniques we obtain computationally efficient methods for both likelihood-based parameter estimation and spatial interpolation. The model is compared to Gaussian models and standard MRF models using simulated data and in application to upscaling of soil permeability data.
Keywords	brain ; geostatistics ; image analysis ; land cover ; probabilistic models ; soil permeability
Language	English
Dates of publication	2019-02
Size	p. 80-93.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1478763-5
ISSN	0167-9473
ISSN	0167-9473
DOI	10.1016/j.csda.2018.08.007
Database	NAL-Catalogue (AGRICOLA)

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Article: Hierarchical Modelling of Haplotype Effects on a Phylogeny.

Selle, Maria Lie / Steinsland, Ingelin / Lindgren, Finn / Brajkovic, Vladimir / Cubric-Curik, Vlatka / Gorjanc, Gregor

Frontiers in genetics

2021 Volume 11, Page(s) 531218

Abstract: We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few ... ...

Abstract	We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations, and leveraging similarities can improve the estimation of effects. We build on extensive literature and develop an autoregressive model of order one that models haplotype effects by leveraging phylogenetic relationships described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network, and we refer to the model as the haplotype network model. The model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. Our key contribution is that we obtain a sparse model, and by using hierarchical autoregression, the flow of information between similar haplotypes is estimated from the data. A simulation study shows that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially with few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a study of mitochondrial haplotype effects on milk yield in cattle. We provide R code to fit the model with the INLA package.
Language	English
Publishing date	2021-01-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2020.531218
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Article ; Online: A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

Mejia, Amanda F. / Yue, Yu (Ryan) / Bolin, David / Lindgren, Finn / Lindquist, Martin A.

Journal of the American Statistical Association. 2020 Apr. 2, v. 115, no. 530 p.501-520

2020

Abstract: Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and ... ...

Abstract	Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a “massive univariate” approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Keywords	Bayesian theory ; humans ; linear models ; magnetism ; Bayesian smoothing ; Brain imaging ; Integrated nested Laplace approximation ; Spatial statistics ; Stochastic partial differential equation
Language	English
Dates of publication	2020-0402
Size	p. 501-520.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	2064981-2
ISSN	1537-274X
ISSN	1537-274X
DOI	10.1080/01621459.2019.1611582
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Constructing Priors that Penalize the Complexity of Gaussian Random Fields

Fuglstad, Geir-Arne / Simpson, Daniel / Lindgren, Finn / Rue, Håvard

Journal of the American Statistical Association. 2019 Jan. 2, v. 114, no. 525 p.445-452

2019

Abstract: Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill ... ...

Abstract	Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent penalized complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional, and three-dimensional Matérn GRFs with fixed smoothness. The prior is weakly informative and penalizes complexity by shrinking the range toward infinity and the marginal variance toward zero. We propose guidelines for selecting the hyperparameters, and a simulation study shows that the new prior provides a principled alternative to reference priors that can leverage prior knowledge to achieve shorter credible intervals while maintaining good coverage. We extend the prior to a nonstationary GRF parameterized through local ranges and marginal standard deviations, and introduce a scheme for selecting the hyperparameters based on the coverage of the parameters when fitting simulated stationary data. The approach is applied to a dataset of annual precipitation in southern Norway and the scheme for selecting the hyperparameters leads to conservative estimates of nonstationarity and improved predictive performance over the stationary model. Supplementary materials for this article are available online.
Keywords	atmospheric precipitation ; covariance ; equations ; guidelines ; models ; standard deviation ; variance ; Norway ; Bayesian ; Nonstationary ; Penalized complexity ; Priors ; Range ; Spatial models
Language	English
Dates of publication	2019-0102
Size	p. 445-452.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	2064981-2
ISSN	1537-274X
ISSN	1537-274X
DOI	10.1080/01621459.2017.1415907
Database	NAL-Catalogue (AGRICOLA)

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Article: A comparison between Markov approximations and other methods for large spatial data sets

Bolin, David / Lindgren, Finn

Elsevier B.V. Computational statistics & data analysis. 2013 May, v. 61

2013

Abstract: The Matérn covariance function is a popular choice for modeling dependence in spatial environmental data. Standard Matérn covariance models are, however, often computationally infeasible for large data sets. Recent results for Markov approximations of ... ...

Abstract	The Matérn covariance function is a popular choice for modeling dependence in spatial environmental data. Standard Matérn covariance models are, however, often computationally infeasible for large data sets. Recent results for Markov approximations of Gaussian Matérn fields based on Hilbert space approximations are extended using wavelet basis functions. Using a simulation-based study, these Markov approximations are compared with two of the most popular methods for computationally efficient model approximations, covariance tapering and the process convolution method. The methods are compared with respect to their computational properties when used for spatial prediction (kriging), and the results show that, for a given computational cost, the Markov methods have a substantial gain in accuracy compared with the other methods.
Keywords	covariance ; data collection ; kriging ; models ; prediction ; spatial data ; wavelet
Language	English
Dates of publication	2013-05
Size	p. 7-21.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1478763-5
ISSN	0167-9473
ISSN	0167-9473
DOI	10.1016/j.csda.2012.11.011
Database	NAL-Catalogue (AGRICOLA)

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