Spatial and Spatio‐temporal Bayesian Models with R‐INLA

Blangiardo, Marta; Cameletti, Michela

doi:10.1002/9781118950203

Cited by 551 publications

(650 citation statements)

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“…Note that in R-INLA the smoothness parameter λ, which is usually kept fixed to ensure model identifiability, is by default equal to 1; in addition the SPDE parameters are represented as log(τ ) = θ 1 (τ is related to the variance through the relationship σ 2 ω = 1/(4πκ 2 τ 2 )) and log(κ) = θ 2 , with θ 1 and θ 2 being given independent Normal(0,1) prior distributions (for more details see Blangiardo and Cameletti, 2015). Moreover, weakly informative Normal priors centered on 0 and with a small precision equal to 0.01 are specified for the fixed effects in Equation (1).…”

Section: First Stage: Spatio-temporal No 2 Modelmentioning

confidence: 99%

“…Latent Gaussian models can be represented through hierarchical structures; using the general notation adopted in Blangiardo and Cameletti (2015), at the first stage the model for the data y = (y 1 , . .…”

Section: Bayesian Inference With Inla and Spdementioning

confidence: 99%

“…INLA (Rue et al, 2009;Blangiardo and Cameletti, 2015) is a computationally efficient alternative to Markov chain Monte Carlo (MCMC) methods, which are usually adopted for Bayesian inference but suffer from computational complexity, especially in case of large datasets characterized by high spatial and/or temporal resolution. INLA performs approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations and its use is now well established in several research fields, including ecology, epidemiology, econometrics and environmental science (Illian et al, 2013;Blangiardo et al, 2013;Musenge et al, 2013;Bilancia and Demarinis, 2014;Carson and Mills Flemming, 2014;Cosandey-Godin et al, 2015;Bivand et al, 2014;Muff et al, 2015;Mtambo et al, 2015;Gómez-Rubio et al, 2015), also thanks to the availability of the R-INLA package.…”

Section: Bayesian Inference With Inla and Spdementioning

confidence: 99%

See 2 more Smart Citations

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Blangiardo

Finazzi

Cameletti

2016

Spatial and Spatio-temporal Epidemiology

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Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalization or mortality data. However this approach limits the analysis to individuals characterized by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population.We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO 2 concentration integrating several data sources characterized by different spatio-temporal resolution; in the second stage we link the concentration to the β 2 -agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach.

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Section: First Stage: Spatio-temporal No 2 Modelmentioning

confidence: 99%

Section: Bayesian Inference With Inla and Spdementioning

confidence: 99%

Section: Bayesian Inference With Inla and Spdementioning

confidence: 99%

See 1 more Smart Citation

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Blangiardo

Finazzi

Cameletti

2016

Spatial and Spatio-temporal Epidemiology

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“…Type III interaction is composed of spatially structured but temporally unstructured effects and assumes that small-area incident risk exhibits spatial autocorrelation for each time period but are independent in time (i.e., s i and γ t interact). Type IV space-time interaction captures both spatial and temporal structure and assumes that calls-for-service for a space-time unit of analysis are both spatially and temporally correlated (i.e., s i and φ t interact) [40,44]. This specification is reasonable when the trend of calls-for-service for one area is similar over time and geographically adjacent areas exhibit similar trends.…”

Section: Prior Distributionsmentioning

confidence: 99%

“…When data are available for large spatial and temporal extents, INLA spatio-temporal models may be applied to analyze small-area data for a country (e.g., Canada was composed of 56,204 DAs in 2011) over many years to identify generalizable, regional, and neighbourhood-specific patterns in calls-for-service. When spatiotemporal point data are available, INLA can be extended via stochastic differential partial equation (SPDE) modeling [40].…”

Section: Modeling Bigger Spatio-temporal Datasets With Inlamentioning

confidence: 99%

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

Luan

Quick

Law

2016

IJGI

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This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns) were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00-03:59) and highest in the evening (20:00-21:59), while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted.

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Association of Medicaid Expansion With Opioid Overdose Mortality in the United States

et al. 2020

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IMPORTANCE The Patient Protection and Affordable Care Act (ACA) permits states to expand Medicaid coverage for most low-income adults to 138% of the federal poverty level and requires the provision of mental health and substance use disorder services on parity with other medical and surgical services. Uptake of substance use disorder services with medications for opioid use disorder has increased more in Medicaid expansion states than in nonexpansion states, but whether ACA-related Medicaid expansion is associated with county-level opioid overdose mortality has not been examined. OBJECTIVE To examine whether Medicaid expansion is associated with county × year counts of opioid overdose deaths overall and by class of opioid.

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Spatial and Spatio‐temporal Bayesian Models with R‐INLA

Cited by 551 publications

References 0 publications

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

Association of Medicaid Expansion With Opioid Overdose Mortality in the United States

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