A new avenue for Bayesian inference with INLA

Niekerk, Janet van; Krainski, Elias Teixeira; Rustand, Denis; Rue, Haåvard

doi:10.48550/arxiv.2204.06797

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…For this reason, we work at the municipality level, which manages to capture the equine movement patterns at the state level quite well. Finally, combining Bayesian hierarchical non-stationary barrier mode and social network analysis warrants further research and would benefit from an in-depth evaluation compared to more traditional approaches (van Niekerk et al, 2022). In addition, we cannot inform the stability of these areas over large periods of time given variations due to climate change and animal movement patterns.…”

Section: Limitations and Further Remarksmentioning

confidence: 99%

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk‐based surveillance

Cárdenas

Sanchez

Lopes

et al. 2022

Transbounding Emerging Dis

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Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017.We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from highrisk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low-and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.

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Section: Limitations and Further Remarksmentioning

confidence: 99%

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk‐based surveillance

Cárdenas

Sanchez

Lopes

et al. 2022

Transbounding Emerging Dis

View full text Add to dashboard Cite

show abstract

“…To evaluate the mesh, we used the production of triangles that appeared regular in size and shape (Krainski et al, 2019). To avoid computational errors, increase the stability of the program, and reduce computational time when running the models, we corrected the Laplace method with variational Bayes by setting inla.mode = "experimental" (Gaedke- Merzhäuser et al, 2022;Van Niekerk et al, 2022;Van Niekerk & Rue, 2021).…”

Section: Discussionmentioning

confidence: 99%

Spatial modeling of two mosquito vectors of West Nile virus using integrated nested Laplace approximations

et al. 2023

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The abundance of Culex restuans and Culex pipiens in relation to ecological predictors is poorly understood in regions of the United States where their ranges overlap. It is suspected that these species play different roles in spreading West Nile virus (WNV) in these regions, but few studies have modeled these species separately or accounted for spatial correlation using Bayesian methods. We used mosquito surveillance data collected by the Pennsylvania Department of Environmental Protection from 2002 to 2016 and integrated nested Laplace approximations with the stochastic partial differential equation approach to predict C. restuans and C. pipiens abundance in relation to several ecological predictors. We then made a predictive risk surface of abundance for each species at locations that were not sampled. Explanatory variables in the models included ecological variables previously described to be important predictors of the abundance of these mosquito species. Developed habitat, temperature, and precipitation were important predictor variables for the abundance of C. restuans, whereas developed habitat, snow water equivalent, and normalized difference water index were important predictor variables for the abundance of C. pipiens. The abundance of C. restuans had a negative relationship with developed habitat in contrast to C. pipiens abundance, which had a positive relationship with developed habitat. Julian date was modeled as a temporal trend for both species and showed C. restuans to be more abundant from late April through late June and C. pipiens to be more abundant from July through September. A seasonal crossover was observed between these two species on Julian day 185, 4 July. We observed different spatial patterns of abundance in the predictive risk maps of each of the species. Our results indicate that modeling the abundance of these species spatially and separately in regions where these two mosquito vectors coexist can help gain further insight into understanding the epidemiology of WNV in human and susceptible animal populations.

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“…where θ are the hyperparameters of the model. The latent parameter space of the model has traditionally been defined as x = (β, u, η), but can now also be formulated without the linear predictor as x = (β, u), which was recently presented in [57].…”

Section: Latent Gaussian Modelsmentioning

confidence: 99%

Parallelized integrated nested Laplace approximations for fast Bayesian inference

Gaedke-Merzhäuser

Niekerk²,

Schenk³

et al. 2022

Stat Comput

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There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work we present parallelization strategies for the methodology of integrated nested Laplace approximations (INLA), a popular framework for performing approximate Bayesian inference on the class of Latent Gaussian models. Our approach makes use of nested thread-level parallelism, a parallel line search procedure using robust regression in INLA's optimization phase and the state-of-the-art sparse linear solver PARDISO. We leverage mutually independent function evaluations in the algorithm as well as advanced sparse linear algebra techniques. This way we can flexibly utilize the power of today's multi-core architectures. We demonstrate the performance of our new parallelization scheme on a number of different real-world applications. The introduction of parallelism leads to speedups of a factor 10 and more for all larger models. Our work is already integrated in the current version of the open-source R-INLA package, making its improved performance conveniently available to all users.

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A new avenue for Bayesian inference with INLA

Cited by 4 publications

References 44 publications

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk‐based surveillance

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk‐based surveillance

Spatial modeling of two mosquito vectors of West Nile virus using integrated nested Laplace approximations

Parallelized integrated nested Laplace approximations for fast Bayesian inference

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