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SUMMARY The reliable forecasting of seismic sequences following a main shock has practical implications because effective post-event response is crucial in earthquake-stricken regions, aftershocks can progressively cause increased damage and compound economic losses. In the South Iceland Seismic Zone (SISZ), one of two large transform zones in Iceland where earthquake hazard is the highest, an intense seismic sequence took place during 17–24 June 2000, starting with a ${M}_{\rm{w}}$ 6.4 main shock on 17 June 2000, followed by another ${M}_{\rm{w}}$ 6.5 main shock four days later and on a different fault. Both earthquakes caused considerable damage and incurred heavy economic losses. They were immediately followed by intense aftershock activity on the causative faults and triggered earthquakes as far as 80 km away along the transform zone. To investigate the feasibility of forecasting the progression of such complex sequences, we calibrated a spatio-temporal epidemic-type aftershock sequence (ETAS) clustering model to the June 2000 seismic sequence in the framework of Bayesian statistics. Short-term seismicity forecasts were carried out for various forecasting intervals and compared with the observations, the first generated a few hours after the first main shock and followed by daily forecasts. The reliability of the early forecasts was seen to depend on the initial model parameters. By using an adaptive parameter inference approach where the posteriors from each preceding forecasting interval served as informative priors for the next, the fast convergence of the parametric values was ensured. As a result, the 16–84 percentile range of the forecasted number of events captured the actual number of observed events in all daily forecasts, and the model exhibited a strong spatial forecasting ability, even only a few hours after the main shock, and over all subsequent daily forecasts. We present the spatio-temporal ETAS parameters for the June 2000 sequence as ideal candidates of prior estimates for future operational earthquake forecasting of other Icelandic aftershock sequences. Past seismic sequences need to be analysed retrospectively to confirm the stability of the parameters of this study, effectively enable the application of the Bayesian ETAS model as an operational earthquake forecasting system for aftershocks in Iceland.

The earthquake ground motions of over 1700 earthquakes recorded on a small-aperture strong-motion array in south Iceland (ICEARRAY I) that is situated on a relatively uniform site condition characterized as rock, exhibit a statistically significant spatial variation of ground-motion amplitudes across the array. Both earthquake and microseismic horizontal-to-vertical spectral ratios (HVSR) have been shown to exhibit distinct and in some cases, bimodal peaks in amplification, indicating site resonance at periods of 0.1–0.3 s, a phenomenon that has been attributed to a surface layer of lava rock lying above a sedimentary layer, a structure that is then repeated with depth under the array. In this study, we implement a Bayesian hierarchical model (BHM) of the seismic ground motions that partitions the model residuals into earthquake event term, station term, and event–station term. We analyzed and compared peak ground acceleration (PGA) with the 5% damped pseudo-acceleration response spectrum (PSA) at oscillator periods of T = 0.05–1.0 s. The results show that the event terms, dominate the total variability of the ground-motion amplitudes over the array. However, the station terms are shown to increase in the period range of 0.1–0.3 s on most stations and to different extents, leading to an increase in the overall variability of ground motions at those periods, captured by a larger inter-station standard deviation. As the station terms are a measure of how much the ground motions at those stations deviate from the array average, they act as proxies for localized site effects and amplification factors. These results, improve our understanding of the key factors that affect the variation of seismic ground motions across the relatively small area of ICEARRAY I. This approach can help to improve the accuracy of earthquake hazard assessments on local scales, which in turn could contribute to more refined seismic risk assessments and engineering decision-making.

Publisher's version (útgefin grein), Introduction: Pneumococcus is an important respiratory pathogen. The 10-valent pneumococcal vaccine (PHiD-CV) was introduced into the Icelandic vaccination programme in 2011. The aim was to estimate the impact of PHiD-CV on paediatric hospitalisations for respiratory tract infections and invasive disease. Methods: The 2005–2015 birth-cohorts were followed until three years of age and hospitalisations were recorded for invasive pneumococcal disease (IPD), meningitis, sepsis, pneumonia and otitis media. Hospitalisations for upper- and lower respiratory tract infections (URTI, LRTI) were used as comparators. The 2005–2010 birth-cohorts were defined as vaccine non-eligible cohorts (VNEC) and 2011–2015 birth-cohorts as vaccine eligible cohorts (VEC). Incidence rates (IR) were estimated for diagnoses, birth-cohorts and age groups, and incidence rate ratios (IRR) between VNEC and VEC were calculated assuming Poisson variance. Cox regression was used to estimate the hazard ratio (HR) of hospitalisation between VNEC and VEC. Results: 51,264 children were followed for 142,315 person-years, accumulating 1,703 hospitalisations for the respective study diagnoses. Hospitalisations for pneumonia decreased by 20% (HR 0.80, 95%CI:0.67–0.95) despite a 32% increase in admissions for LRTI (HR 1.32, 95%CI:1.14–1.53). Hospital admissions for culture-confirmed IPD decreased by 93% (HR 0.07, 95%CI:0.01–0.50) and no hospitalisations for IPD with vaccine-type pneumococci were observed in the VEC. Hospitalisations for meningitis and sepsis did not change. A decrease in hospital admissions for otitis media was observed, but did not coincide with PHiD-CV introduction. Conclusion: Following the introduction of PHiD-CV in Iceland, hospitalisations for pneumonia and culture confirmed IPD decreased. Admissions for other LRTIs and URTIs increased during this period., An investigator-initiated study funded by GlaxoSmithKline Biologicals SA. Additionally, a grant was received from the Landspitali University Hospital Research Fund. GlaxoSmithKline Biologicals SA was provided the opportunity to review a draft version of this manuscript, but the authors are solely responsible for final content and interpretation. The authors received no financial support or other form of compensation related to the development of the manuscript.

The magnitude distribution of caldera earthquakes in the subglacial Bárðarbunga volcano in Central Iceland, characterized by the b-value, shows a systematic variation that is consistent with stress changes anticipated in the roof of an inflating magma chamber beneath the caldera. The b-value was 0.83 prior to the rupture of the chamber in August 2014 when a dike propagated laterally from the volcano to feed the eruption in Holuhraun. The b-value was relatively high following the collapse of the caldera, reflecting low stress in the magma chamber roof. Half a year later a decrease was observed in the b-value, concurrent with an increase in the seismicity, consistent with indications of recharging of the volcano magma chamber. The magnitude distribution was anomalous during the slow collapse of the caldera in association with the eruption. During this period the earthquake sequence appeared to consist of two populations, only one of which followed the conventional Gutenberg–Richter distribution. For a subglacial volcano, where geodetic methods are difficult to implement, the b-value of caldera earthquakes provides an important additional parameter for the monitoring of magma pressure variations.

This study assesses spatiotemporal and sex-specific growth of Atlantic cod Gadus morhua in Icelandic waters. We use a Bayesian approach which lends itself to fitting and comparing nested models such as these. We then compare fitted parameters of these models to potential explanatory variables using a redundancy analysis (RDA) to look for drivers of growth in G. morhua. Results indicate that models that incorporate differences in growth among time, space and sex are the best-fitting models according to deviance information criterion (DIC). Results from RDA indicate that capelin Mallotus villosus recruitment and biomass is highly correlated with deviations in the von Bertalannfy growth parameter k and that L∞ is correlated with G. morhua landings in the model that uses year to account for time-varying growth and estimated G. morhua recruitment in the model that uses cohort to account for time-varying growth.

In this paper, we extend and analyze a Bayesian hierarchical spatiotemporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simulator for a physical process and the actual process values with a multivariate random walk. For computational efficiency, linear algebra for bandwidth limited matrices is utilized, and first-order emulator inference allows for the fast emulation of a numerical partial differential equation (PDE) solver. A test scenario from a physical system motivated by glaciology is used to examine the speed and accuracy of the computational methods used, in addition to the viability of modeling assumptions. We conclude by discussing how the model and associated methodology can be applied in other physical contexts besides glaciology.

Earthquake recordings on two small-aperture (covering ∼ 1.2 km2 each) strong-motion arrays in Iceland (ICEARRAY I and II) exhibit considerable variations in the spatial distribution of ground-motion amplitudes. To better understand this spatial variability, we use a Bayesian Hierarchical Model (BHM) that incorporates ground motions models (GMMs) for peak ground accelerations (PGA) developed from ground motion databases recorded by the two arrays, respectively. The posterior distributions of the model parameters are then determined using Markov Chain Monte Carlo simulations in the context of Bayesian statistical methods. The BHM allows the partitioning of a GMM into event, station, and event-station terms, which in turn allows the relative contributions of source, path, and site effects to be quantified. The results indicate that site effects can dominate the spatial distribution of ground-motion parameters (e.g., PGA) observed across both ICEARRAY I and II. Although the site conditions across ICEARRAY I have been classified as uniform (i.e., “rock” with a relatively flat topography), station terms contribute ∼ 13 % to the total variability in the amplitudes of predicted ground motions across the array. In contrast to ICEARRAY I, the variation of the geologic profiles and topography is much greater across ICEARRAY II. As a result, the inter-station variability is shown to contribute up to ∼ 57 % of the total variability in the amplitudes of predicted ground motions across the array, with the contributions being less constrained for ICEARRAY II than ICEARRAY I due to the relative sizes of the recorded ground motion databases. These results facilitate our understanding of the key factors that affect the variation of seismic ground motions across a relatively small area. Such a detailed microzonation is of great importance for earthquake hazard assessment on a local scale and has practical implications for engineering decision making.

Iceland is seismically the most active region in northern Europe. Large single earthquakes (~ $$ M_{\text{w}} $$ 7) and seismic sequences of moderate-to-strong earthquakes (~ $$ M_{\text{w}} $$ 6–6.5) have repeatedly occurred during past centuries in the populated South Iceland Seismic Zone (SISZ). The seismic hazard in Iceland has mainly been evaluated using ground motion models (GMMs) developed from strong-motion observations in other countries and only to a very limited extent from Icelandic data, despite a particularly rapid attenuation of ground motions with distance in Iceland. In this study, we evaluate the performance of these GMMs against the Icelandic strong-motion dataset, consisting of peak ground accelerations of moderate-to-strong ( $$ M_{\text{w}} $$ 5–6.5) and local (0–80 km) earthquakes in the SISZ. We find that these GMMs exhibit both a strong bias against the dataset and a relatively large variability, which calls their applicability and earlier hazard analyses into question. To address this issue, we recalibrate each of the GMMs to the dataset using Bayesian regression and Markov Chain Monte Carlo simulations. This approach allows useful prior information of the GMM parameters to be combined with the likelihood of the observed data and provides posterior probability density functions of model residuals and regression parameters. The recalibrated GMMs are unbiased with respect to the data and have a low total standard deviation of around 0.17 (base-10 logarithmic units). The model-to-model variability in the median predictions vary primarily with distance, reaching 0.05 the lowest for $$ M_{\text{w}} $$ 6.3–6.5 at intermediate distances. While the lack of near-fault and far-field data, particularly at large magnitudes, and the different functional forms of the GMMs calibrated to the same dataset may affect the results, the recalibrated GMMs should represent well the ground motions of a typical sequence of moderate-to-strong SISZ earthquakes. We present the recalibrated GMMs of this study as promising candidates for future use in ground motion prediction in Iceland e.g., in the context of either a logic tree or the backbone approach in probabilistic seismic hazard assessment.

Publisher's version (útgefin grein), Aim: The aim was to estimate the impact of the 10-valent pneumococcal vaccine (PHiD-CV) on tympanostomy tube placements (TTP) in children under five years of age in Iceland. Methods: This population-based observational cohort study followed 11 consecutive birth-cohorts 2005–2015 from birth until their fifth birthday. Population registries were merged using national identification numbers. The risk of TTP was compared between birth-cohorts adjusted for the number of previous otitis media diagnoses and antimicrobial prescriptions. A Cox regression model was applied and the hazard ratio (HR) of TTP was estimated between each birth-cohort and the last vaccine non-eligible birth-cohort. The vaccine impact of PHiD-CV10 on TTP was estimated as 1-HR ×100%. Results: In total, 51 247 children were followed for 210 724 person-years, of which 14 351 underwent 20 373 procedures. The estimated vaccine impact on TTP was −6% (95% CI −16% to 2.7%). Children in the vaccine-eligible cohorts had fewer previous otitis media diagnoses and had been prescribed fewer antimicrobials prior to the procedure than children in the vaccine non-eligible cohorts. Conclusion: Despite high uptake of PHiD-CV10, tympanostomy procedures increased in Iceland during the study period. Vaccine-eligible children had milder disease prior to the procedure. The reason underlying these findings are speculative., An investigator-initiated study funded by GlaxoSmithKline Biologicals SA. Additionally, a grant was received from the Landspitali University Hospital Research Fund. GlaxoSmithKline Biologicals SA was provided the opportunity to review a draft version of this manuscript, but the authors are solely responsible for final content and interpretation. The authors received no financial support or other form of compensation related to the development of the manuscript.

In this study, we propose a data-driven method using the Deviance Information Criterion (DIC) to select the most suitable earthquake ground motion model (GMM) for application in probabilistic seismic hazard analysis (PSHA). The standard deviation (sigma) of the GMM is an important parameter for PSHA and plays an important role in data-driven methods. The main advantage of the proposed procedure is to introduce the posterior sigma as the key quantity for objectively ranking different candidate models against a given earthquake ground motion dataset. In the context of the Bayesian statistical framework, sigma is then determined for a given GMM based on the observed ground motions and at the same time takes into account the misfit of the GMM predictions to the observed ground motions. This feature addresses issues associated with other ranking methods where in some cases a considerable bias between the GMM predictions and the observed ground motions is effectively ignored. On the contrary, the DIC considers the influence of these two factors together by ranking models more favorably when they are associated with smaller bias and the determined sigma is close to the actual variability of the ground motions in the region under study. We submit the DIC method of this study as a useful and objective method for evaluating the performance of a GMM to a given dataset. This has potentially important application for PSHA when using multiple GMMs and either logic tree or backbone approaches are required to handle epistemic uncertainty in an appropriate manner.

We develop Bayesian statistical models that are designed for the inference of ice softness and basal sliding parameters, important glaciological quantities. These models are applied to Langjökull, the second largest temperate ice cap in Iceland at about 900 squared kilometers in area. The models make use of a relationship between physical parameters and ice velocity as stipulated by a shallow ice approximation that is generally applicable to Langjökull. The posterior distribution for ice softness concentrates around 18.2 × 10−25s−1Pa−3; moreover, spatially varying basal sliding parameters are inferred allowing for the decomposition of velocity into a deformation component and a sliding component, with spatial variation consistent with previous studies. Bayesian computation is conducted with a Gibbs sampling approach. The paper serves as an example of statistical inference for ice softness and basal sliding parameters at temperate, shallow glaciers using surface velocity data.

Introduction Streptococcus pneumoniae is a cause of infections that range in severity from acute otitis media (AOM) to pneumonia and invasive pneumococcal disease (IPD). The 10-valent pneumococcal conjugate vaccine (PHiD-CV10) was introduced into the Icelandic paediatric immunisation programme in 2011. The aim was to estimate the population impact and cost-effectiveness of PHiD-CV10 introduction. Methods Data on primary care visits from 2005–2015 and hospitalisations from 2005–2017 were obtained from population-based registries. A Bayesian time series analysis with synthetic controls was employed to estimate the number of cases of AOM, pneumonia and IPD that would have occurred between 2013–2017, had PHiD-CV10 not been introduced. Prevented cases were calculated by subtracting the observed number of cases from this estimate. The cost of the programme was calculated accounting for cost-savings due to prevented cases. Results The introduction of PHiD-CV10 prevented 13,767 (95% credible interval [CI] 2,511–29,410) visits for AOM from 2013–2015, and prevented 1,814 (95%CI -523-4,512) hospitalisations for pneumonia and 53 (95%CI -17-177) admissions for IPD from 2013–2017. Visits for AOM decreased both among young children and among children 4–19 years of age, with rate ratios between 0.72–0.89. Decreases were observed in both pneumonia hospitalisations (rate ratios between 0.67–0.92) and IPD (rate ratios between 0.27–0.94). The total cost of implementing PHiD-CV10 in Iceland was -7,463,176 United States Dollars (USD) (95%CI -16,159,551–582,135) with 2.1 USD (95%CI 0.2–4.7) saved for every 1 USD spent. Conclusions The introduction of PHiD-CV10 was associated with large decreases in visits and hospitalisations for infections commonly caused by pneumococcus and was cost-saving during the first five years of the immunisation programme.

The power-law rating curve has been used extensively in hydraulic practice and hydrology. It is given by $Q(h)=a(h-c)^b$, where $Q$ is discharge, $h$ is water elevation, $a$, $b$ and $c$ are unknown parameters. We propose a novel extension of the power-law rating curve, referred to as the generalized power-law rating curve. It is constructed by linking the physics of open channel flow to a model of the form $Q(h)=a(h-c)^{f(h)}$. The function $f(h)$ is referred to as the power-law exponent and it depends on the water elevation. The proposed model and the power-law model are fitted within the framework of Bayesian hierarchical models. By exploring the properties of the proposed rating curve and its power-law exponent, we find that cross sectional shapes that are likely to be found in nature are such that the power-law exponent $f(h)$ will usually be in the interval $[1.0,2.67]$. This fact is utilized for the construction of prior densities for the model parameters. An efficient Markov chain Monte Carlo sampling scheme, that utilizes the lognormal distributional assumption at the data level and Gaussian assumption at the latent level, is proposed for the two models. The two statistical models were applied to four datasets. In the case of three datasets the generalized power-law rating curve gave a better fit than the power-law rating curve while in the fourth case the two models fitted equally well and the generalized power-law rating curve mimicked the power-law rating curve., Main paper 29 pages, 4 tables, 7 figures. Supplementary material 5 pages, 4 figures

A general and flexible class of latent Gaussian models is proposed in this paper. The latent Gaussian model is adapted to the generalized additive model for location, scale and shape (GAMLSS), that is, the data density function of each data point can depend on more than a single linear predictor of the latent parameters. We refer to this framework as extended latent Gaussian models. The most commonly applied latent Gaussian models (LGMs) are such that a linear predictor is proposed only for the location parameter. Extended LGMs allow proposing linear predictors also for the scale parameter and potentially other parameters. We propose a novel computationally efficient Markov chain Monte Carlo sampling scheme for the extended LGMs which we refer to as the LGM split sampler. It is a two block Gibbs sampling scheme designed to exploit the model structure of the extended LGMs. An extended LGM is constructed for a simulated dataset and the LGM split sampler is implemented for posterior simulations. The results demonstrate the flexibility of the extended LGM framework and the efficiency of the LGM split sampler.

BACKGROUND Acute otitis media (AOM) nonresponsive to antibiotics is most commonly caused by antibiotic-resistant Streptococcus pneumoniae and Haemophilus influenzae. A strategy for treating these infections with parenteral ceftriaxone was adopted at the Children's Hospital Iceland. The 10-valent pneumococcal H. influenzae protein D-conjugate vaccine was introduced into the vaccination program in Iceland in 2011. The aim was to study its effect on the incidence of AOM with treatment failure. METHODS This retrospective observational study included children who visited the Children's Hospital Iceland because of AOM or received ceftriaxone, regardless of indication from 2008-2015. Incidence rate was calculated for prevaccine (2008-2011) and postvaccine (2012-2015) periods using person-years at risk within the hospital's referral region. Incidence rate ratio of ceftriaxone treatment episodes of AOM was calculated using the Mantel-Haenzel method adjusting for age. Incidence risk ratio of ceftriaxone treatment if presenting to the hospital with AOM was calculated to adjust for rate of AOM visits. RESULTS Visits for AOM decreased from 47.5 to 33.9 visits per 1000 person-years, incidence rate ratio (IRR) 0.86 (95% confidence interval [CI]: 0.81-0.91), P < 0.001. Fewer AOM episodes were treated with ceftriaxone, decreasing from 6.49 to 2.96 treatment episodes per 1000 person-years, with an overall Mantel-Haenzel adjusted IRR 0.45 (95% CI: 0.37-0.54; P < 0.001). This remained significant after adjusting for the decrease in AOM visits, IRR 0.53 (95% CI: 0.44-0.63; P < 0.001). CONCLUSIONS Visits for AOM and ceftriaxone use decreased significantly after H. influenzae protein D-conjugate vaccine introduction. The observed decrease in ceftriaxone use is presumed to represent a decline in AOM with treatment failure, secondary to a decrease in resistant infections.

Publisher's version (útgefin grein), Forecasting of glacier mass balance is important for optimal management of hydrological resources, especially where glacial meltwater constitutes a significant portion of stream flow, as is the case for many rivers in Iceland. In this study, a method was developed and applied to forecast the summer mass balance of Brúarjökull glacier in southeast Iceland. In the present study, many variables measured in the basin were evaluated, including glaciological snow accumulation data, various climate indices and meteorological measurements including temperature, humidity and radiation. The most relevant single predictor variables were selected using correlation analysis. The selected variables were used to define a set of potential multivariate linear regression models that were optimized by selecting an ensemble of plausible models showing good fit to calibration data. A mass-balance estimate was calculated as a uniform average across ensemble predictions. The method was evaluated using fivefold cross-validation and the statistical metrics Nash–Sutcliffe efficiency, the ratio of the root mean square error to the std dev. and percent bias. The results showed that the model produces satisfactory predictions when forced with initial condition data available at the beginning of the summer melt season, between 15 June and 1 July, whereas less reliable predictions are produced for longer lead times., We thank the University of Iceland Research Fund which is supporting the first author. The project was also supported by the Energy Research Fund of the National Energy Company, Landsvirkjun by grants no. MEI-03-2015 and DOK-02-2017

Background Since the introduction of pneumococcal conjugate vaccines, vaccine type pneumococcal carriage and disease has decreased world-wide. The aim was to monitor changes in the nasopharyngeal carriage of pneumococci, the distribution of serotypes and antimicrobial resistance in children before and after initiation of the 10-valent pneumococcal vaccination in 2011, in a previously unvaccinated population. Methods Repeated cross-sectional study at 15 day-care centres in greater Reykjavik area. Nasopharyngeal swabs were collected yearly in March from 2009 to 2015. The swabs were selectively cultured for pneumococci, which were serotyped using latex agglutination and/or PCR and antimicrobial susceptibility determined. Two independent studies were conducted. In study 1, on total impact, isolates from children aged, Funding for this study was provided by GlaxoSmithKline Biologicals SA and a grant was received from the Landspitali University Hospital Research Fund. GlaxoSmithKline Biologicals SA was provided the opportunity to review a draft version of this manuscript but the authors are solely responsible for final content and interpretation. The authors received no financial support or other form of compensation related to the development of the manuscript.

Vaccinations with the 10-valent pneumococcal conjugated vaccine (PHiD-CV) started in Iceland in 2011. Protein D (PD) from H. influenzae, which is coded for by the hpd gene, is used as a conjugate in the vaccine and may provide protection against PD-positive H. influenzae., Vaccinations with the 10-valent pneumococcal conjugated vaccine (PHiD-CV) started in Iceland in 2011. Protein D (PD) from H. influenzae, which is coded for by the hpd gene, is used as a conjugate in the vaccine and may provide protection against PD-positive H. influenzae. We aimed to evaluate the effect of PHiD-CV vaccination on H. influenzae in children, both in carriage and in acute otitis media (AOM). H. influenzae was isolated from nasopharyngeal swabs collected from healthy children attending 15 day care centers in 2009 and from 2012 to 2017 and from middle ear (ME) samples from children with AOM collected from 2012 to 2017. All isolates were identified using PCR for the hpd and fucK genes. Of the 3,600 samples collected from healthy children, 2,465 were culture positive for H. influenzae (68.5% carriage rate); of these, 151 (6.1%) contained hpd-negative isolates. Of the 2,847 ME samples collected, 889 (31.2%) were culture positive for H. influenzae; of these, 71 (8.0%) were hpd negative. Despite the same practice throughout the study, the annual number of ME samples reduced from 660 in 2012 to 330 in 2017. The proportions of hpd-negative isolates in unvaccinated versus vaccinated children were 5.6% and 7.0%, respectively, in healthy carriers, and 5.4% and 7.8%, respectively, in ME samples. The proportion of hpd-negative isolates increased with time in ME samples but not in healthy carriers. The number of ME samples from children with AOM decreased. The PHiD-CV had no effect on the proportion of the hpd gene in H. influenzae from carriage, but there was an increase in hpd-negative H. influenzae in otitis media. The proportions of hpd-negative isolates remained similar in vaccinated and unvaccinated children.

The introduction of pneumococcal conjugate vaccines (PCVs) into childhood vaccination programs has reduced carriage of vaccine serotypes and pneumococcal disease. The 10-valent PCV was introduced in Iceland in 2011., The introduction of pneumococcal conjugate vaccines (PCVs) into childhood vaccination programs has reduced carriage of vaccine serotypes and pneumococcal disease. The 10-valent PCV was introduced in Iceland in 2011. The aim of this study was to determine PCV impact on the prevalence of serotypes, genetic lineages, and antimicrobial-resistant pneumococci isolated from the lower respiratory tract (LRT) of adults. Pneumococci isolated between 2009 and 2017 at the Landspitali University Hospital were included (n = 797). The hospital serves almost three-quarters of the Icelandic population. Isolates were serotyped and tested for antimicrobial susceptibility, and the genome of every other isolate collected between 2009 and 2014 was sequenced (n = 275). Serotypes and multilocus sequence types (STs) were extracted from the genome data. Three study periods were defined, 2009 to 2011 (PreVac), 2012 to 2014 (PostVac-I), and 2015 to 2017 (PostVac-II). The total number of isolates and vaccine-type (VT) pneumococci decreased from PreVac to PostVac-II (n = 314 versus n = 230 [p = 0.002] and n = 170 versus n = 33 [p

Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatiotemporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error-correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier., The Icelandic Research Fund (RANNIS) is thanked for funding this research.

Evidence is mixed on whether society-wide economic conditions affect cardiovascular health and the reasons for the suggested relationship are largely untested. We explore whether a short-term increase in labor supply affects the probability of acute myocardial infarctions, using a natural experiment in Iceland. In 1987 personal income taxes were temporarily reduced to zero, resulting in an overall increase in labor supply. We merge and analyze individual-level, registry-based data on earnings and AMIs including all Icelandic men and women aged 45–74 during the period 1982–1992. The results support the prominent hypothesis of increased work as a mechanism explaining worsening heart health in upswings, for men aged 45–64 who were self-employed. We furthermore find a larger increase in probability of AMIs during the tax-free year in men aged 45–54 than men aged 55–64.

Earthquake ground motion prediction in Iceland where strong-motion data is scarce poses a challenge as empirical ground motion models (GMM) developed from data in other regions systematically fail to capture the consistently large near-fault peak amplitudes and their rapid attenuation with distance from the earthquake source. Therefore, regional GMMs must be constructed but due to the limited data, and none above M w 6.5, earthquake source scaling is unconstrained at larger magnitudes. Instead, physics-based GMMs should be applied based on realistic earthquake source modeling. For that purpose, a seismological model constructed around the specific barrier model (SBM) has been calibrated in the context of the stochastic method using random vibration theory, to earthquake high-frequency strong-motions in the South Iceland Seismic Zone. The SBM is used as it provides a physically consistent and efficient description of the heterogeneous faulting processes that are responsible for the generation of high-frequency waves. On the basis of the concise point-source representation of radiated spectra from N subevents of the SBM the pseudo-spectral accelerations were modeled and compared with that of data in the spectral domain. Backwards model selection was then carried out using Bayesian inference with Monte Carlo simulations and Markov Chains. The number of parameters in the model inference was reduced to obtain stable Markov chains and posterior probability density functions for each parameter, eliminating parametric cross-correlations to the extent possible. The seismological model has been shown to be unbiased with respect to strong-motions in the SISZ, with a total standard deviation of 0.216 (common logarithm), with only a minor contribution from inter-event variability, suggesting a relatively uniform character of SISZ earthquake strong-motions. We showcase the application of the SBM extended into a finite-fault and model the three M w 6.3 - 6.5 earthquakes in the dataset, allowing subevents of varying sizes to populate the fault plane and provide a more realistic earthquake source and acceleration ground motion time history modeling. The time domain results are shown to capture the essential characteristics of the ground motions of the three largest earthquakes in the dataset. We present therefore the SBM as a physically consistent source model of SISZ earthquakes for the generation of synthetic strong-motion time histories or peak parameters, with potential applications in scenario simulations and probabilistic or deterministic seismic hazard assessment.

The Icelandic strong-motion dataset is relatively small and the largest recorded earthquake magnitude is M w 6.5, whereas events of around M w 7.0 are known to have occurred in the South Iceland Seismic Zone (SISZ). As a result, the required features for use in probabilistic seismic hazard assessment (PSHA), such as deviation from self-similar magnitude scaling and magnitude-distance dependent saturation of ground motions at larger magnitudes, is challenging. Compounding the issue, GMMs from other seismic regions exhibit a strong bias to the available Icelandic strong-motions, underpredicting in the near-fault and overpredicting in the far-field regions. In this study, we approach this issue by considering several GMMs that have either been used or recommended for PSHA in Iceland or have functional forms that satisfy the minimum requirements of GMMs used in PSHA. We recalibrate these GMMs to fit the Icelandic data in the context of the Bayesian statistical framework and a Markov Chain Monte Carlo (MCMC) algorithm, where model inference is carried out using both non-informative and informative priors for selected model coefficients from the original GMMs. Moreover, we used a random effects model to partition the aleatory variability into inter-event and intra-event components. We show that the GMMs with informative priors for magnitude scaling and magnitude-distance scaling terms, not only capture the high near-fault amplitudes and rapid ground motion attenuation with distance, but also introduce a controlled saturation of large magnitude ground motions, which is consistent with observations in other interplate regions. In this study we also introduce a simple GMM, based on informative priors from a model for magnitude-dependent earthquake depth in the SISZ, that fully captures the salient characteristics of the recalibrated GMMs. The presented models thus form a suite of new, essentially hybrid, empirical GMMs that can be used with confidence in predicting PGA and PSA for Icelandic earthquakes, with particular implications for the reassessment of the seismic hazard of Iceland.

Publisher's version (útgefin grein), Vaccination with pneumococcal conjugate vaccines (PCVs) disrupts the pneumococcal population. Our aim was to determine the impact of the 10-valent PCV on the serotypes, genetic lineages, and antimicrobial susceptibility of pneumococci isolated from children in Iceland. Pneumococci were collected between 2009 and 2017 from the nasopharynges of healthy children attending 15 day care centers and from the middle ears (MEs) of children with acute otitis media from the greater Reykjavik capital area. Isolates were serotyped and tested for antimicrobial susceptibility. Whole-genome sequencing (WGS) was performed on alternate isolates from 2009 to 2014, and serotypes and multilocus sequence types (STs) were extracted from the WGS data. Two study periods were defined: 2009 to 2011 (PreVac) and 2012 to 2017 (PostVac). The overall nasopharyngeal carriage rate was similar between the two periods (67.3% PreVac and 61.5% PostVac, P = 0.090). Vaccine-type (VT) pneumococci decreased and nonvaccine-type (NVT) pneumococci (serotypes 6C, 15A, 15B/C, 21, 22F, 23A, 23B, 35F, and 35B) significantly increased in different age strata post-PCV introduction. The total number of pneumococci recovered from ME samples significantly decreased as did the proportion that were VTs, although NVT pneumococci (6C, 15B/C, 23A, and 23B) increased significantly. Most serotype 6C pneumococci were multidrug resistant (MDR). Serotype 19F was the predominant serotype associated with MEs, and it significantly decreased post-PCV introduction: these isolates were predominantly MDR and of the Taiwan19F-14 PMEN lineage. Overall, the nasopharyngeal carriage rate remained constant and the number of ME-associated pneumococci decreased significantly post-PCV introduction; however, there was a concomitant and statistically significant shift from VTs to NVTs in both collections of pneumococci., This was an investigator-initiated study funded by GlaxoSmithKline Biologicals SA. Grants were received from the Landspitali University Hospital Research Fund, the Eimskip University Fund, the Wellcome Trust (research fellowship 083511/Z/07/Z and grant 04992/Z/14/Z to A.B.B.), and the John Fell Fund (grant 123/734 to A.B.B.). Work at the Wellcome Sanger Institute was supported by the Wellcome core funding (grant 206194 to S.D.B.)

Publisher's version (útgefin grein). Publisher’s Note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations., Background: Antimicrobial resistance is a public-health threat and antimicrobial consumption is the main contributor. The ten-valent pneumococcal conjugate vaccine (PHiD-CV10) was introduced into the Icelandic vaccination program in 2011. The aim was to estimate the vaccine impact of PHiD-CV10 on outpatient antimicrobial prescriptions in children. Methods: Eleven Icelandic birth-cohorts (2005–2015) were followed from birth until three years of age or to the end of the study period (December 31, 2016). Birth-cohorts were grouped as vaccine non-eligible (VNEC, 2005–2010) or vaccine eligible (VEC, 2011–2015). Data on primary care visits for respiratory infections and antimicrobial prescriptions were extracted from two national registers. Using national identification numbers, prescriptions were linked to physician visits if filled within three days of the visit. Incidence rates and incidence rate ratios between VNEC and VEC were calculated. An Andersen-Gill model was used to model the individual level data, accounting for repeated events and censoring. Vaccine impact was calculated as (1 – Hazard Ratio) × 100%. Results: Included were 53,510 children who contributed 151,992 person-years of follow-up and filled 231,660 antimicrobial prescriptions. The incidence rate was significantly lower in the VEC compared to the VNEC, 144.5 and 157.2 prescriptions per 100 person-years respectively (IRR 0.92, 95%CI 0.91–0.93). Children in VEC were more likely to have filled zero (IRR 1.16 (95%CI 1.10–1.23) and 1–4 (IRR 1.08 95%CI 1.06–1.11) prescriptions compared to children in VNEC. The vaccine impact of PHiD-CV10 against all-cause antimicrobial prescriptions was 5.8% (95%CI 1.6–9.8%).When only considering acute otitis media-associated prescriptions, the vaccine impact was 21.8% (95%CI 11.5–30.9%). Conclusion: The introduction of PHiD-CV10 lead to reduced antimicrobial use in children, mainly by reducing acute otitis media episodes. This intervention therefore reduces both disease burden and could slow the spread of antimicrobial resistance., An investigator-initiated study funded by GlaxoSmithKline Biologicals SA. Additionally, a grant was received from the Landspitali University Hospital Research Fund. GlaxoSmithKline Biologicals SA was provided the opportunity to review a draft version of this manuscript, but the authors are solely responsible for final content and interpretation. The authors received no financial support or other form of compensation related to the development of the manuscript.

Discharge in rivers is commonly estimated by the use of a rating curve constructed from pairs of measured water elevations and discharges at a specific location. The Bayesian approach has been successfully applied to estimate discharge rating curves that are based on the standard power-law. In this paper the standard power-law model is extended by adding a B-spline function. The extended model is compared to the standard power-law model by applying the models to discharge data sets from sixty one different rivers. In addition four rivers are analyzed in detail to demonstrate the benefit of the extended model. The models are compared using two measures, the Deviance Information Criterion (DIC) and Bayes factor. The former provides robust comparison of fit adjusting for the different complexity of the models and the latter measures the evidence of one model against the other. The extended model captures deviations in the data from the standard power-law but reduces to the standard power-law when that model is adequate. The extended model provides substantially better fit than the standard power-law model for about 30% of the rivers and performs better for 60% of the rivers when extrapolating large discharge values.

A reliable estimation of regional ground motion plays a critical role in probabilistic seismic hazard analysis (PSHA). The earthquake resistant design of structures within a region of a small spatial scale is often based on the assumption of relatively uniform form-factors which leads to the assumption of same station condition. However, for some small-scale regions this may not be the case. In this study, we propose a new Bayesian Hierarchical Model (BHM) for peak ground acceleration (PGA) records from two small-aperture Icelandic strong-motion arrays. The proposed BHM characterizes source effect, local station effect, source-station effect, and an error term that represents the measurement error and other unaccounted factors, separately. Posterior inference is based on a Markov chain Monte Carlo algorithm that uses the Metropolis algorithm. Uncertainty in unknown parameters is assessed through their joint posterior density. Analysis of PGA records based on the proposed BHM will improve the comprehensive understanding of the source effects, localized station conditions, and wave propagation.

Respiratory tract infections (RTIs) and antibiotic usage are common in children, increasing the risk of antibacterial resistance. The introduction of protein-conjugated pneumococcal vaccines has led to reduction in pneumococcal infections. In 2011, pneumococcal protein-conjugated vaccine-10 was introduced into the national childhood vaccination in Iceland, a population not earlier vaccinated against pneumococcus, with 95% vaccine uptake in the first year. The aim of the study was to evaluate the number of children visiting the Children's Hospital Iceland for RTIs before and after the introduction of the vaccine.Admissions and visits to the Children's Hospital because of RTIs were recorded, and children aged 3 months to 2 years in the nonvaccine eligible cohort (born 2008-2010) were compared with the vaccine eligible cohort (born in 2011). Statistical analysis was done using large sample Z test and incidence rate ratios (IRRs) were calculated.A significant reduction in incidence rate was found when comparing the nonvaccine eligible cohort with the vaccine eligible cohort, both for acute otitis media (AOM) (IRR: 0.76; 95% confidence interval: 0.67-0.87; P0.0001) and for pneumonia (IRR: 0.77; 95% confidence interval: 0.64-0.95; P0.01).A significant reduction in hospital visits because of AOM and pneumonia in children vaccinated with pneumococcal protein-conjugated vaccine-10 was established. The abrupt and significant reduction of AOM is unusually clear. This reduction was noted very early after initiation of the vaccination.

In this article a Bayesian hierarchical model (BHM) for observed monthly precipitation is proposed. This BHM incorporates covariates based on an output on a fine grid from a regional meteorological model. At the data level of the BHM, the observed monthly precipitation is transformed using the Box–Cox transformation, and each month is modeled separately. To capture spatial correlation at the data level, a Gaussian field with Matern correlation function is used. It is assumed that the data are subject to measurement error. The location and log-scale parameters at the latent level are also modeled with Gaussian fields with Matern correlation functions. An output from a regional meteorological model on a fine grid is used to construct spatial covariates for the latent parameters of the BHM for each month of the year. These covariates are then projected onto each of the observed sites for each month and incorporated into the BHM. Markov chain Monte Carlo simulation is used for posterior inference and Bayesian kriging is used to predict the latent parameters on the grid. This BHM was applied to observed data on monthly precipitation, which come from forty sites across Iceland from the years 1958 to 2006. The data were corrected for wind, wetting, and evaporation loss. An output from a linear model of orographic precipitation defined on a 1 km by 1 km grid over Iceland was used to construct the covariates for the BHM. Copyright © 2015 John Wiley & Sons, Ltd.

We propose a computationally efficient statistical method to obtain distributional properties of annual maximum 24 hour precipitation on a 1 km by 1 km regular grid over Iceland. A latent Gaussian model is built which takes into account observations, spatial variations and outputs from a local meteorological model. A covariate based on the meteorological model is constructed at each observational site and each grid point in order to assimilate available scientific knowledge about precipitation into the statistical model. The model is applied to two data sets on extreme precipitation, one uncorrected data set and one data set that is corrected for phase and wind. The observations are assumed to follow the generalized extreme value distribution. At the latent level, we implement SPDE spatial models for both the location and scale parameters of the likelihood. An efficient MCMC sampler which exploits the model structure is constructed, which yields fast continuous spatial predictions for spatially varying model parameters and quantiles.

Background The 10-valent pneumococcal conjugate vaccine (PHiD-CV10) was introduced in Iceland in 2011, without catch-up. The aim of this study was to estimate vaccine impact (VI) on acute otitis media (AOM). Methods In this whole-population study, all primary care visits due to AOM from 2005 to 2015 in children

Day care attendance and antibiotic consumption are major risk factors for carriage of antibiotic-susceptible and non-susceptible pneumococci. We describe the nasopharyngeal carriage of antibiotic-susceptible and non-susceptible pneumococci among children at day care centres (DCCs), analyse the association of potential risk factors with carriage, and examine the effects of a hygiene intervention on carriage.Thirty DCCs in 2 communities were included in a cohort intervention trial. Nasopharyngeal cultures and information on the children were obtained every 6 months. The study lasted 2.5 y and the hygiene intervention was introduced at half of the DCCs during the last 1.5 y of the study. The results were analysed using a mixed effects logistic regression model.A total of 5663 cultures were obtained from 2399 children, of which 55.6% grew pneumococci. Of the pneumococci, 27.9% were penicillin-non-susceptible (PNSP). The hygiene intervention was associated with a decreased risk of pneumococcal carriage, but this did not reach statistical significance for PNSP carriage. Pneumococcal and PNSP carriage was negatively associated with age, varied significantly between DCCs, and was positively associated with the number of preceding colds. Individual antibiotic use (mainly penicillin/amoxicillin) at the time of sampling and/or during the preceding month was associated with a decreased risk of pneumococcal and PNSP carriage. Individual use of cephalosporins was associated with an increased risk of carriage of penicillin and TMP-SMX-non-susceptible pneumococci.The hygiene intervention at the DCCs reduced the risk of pneumococcal carriage and the individual use of antibiotics was found to affect carriage in a complex manner.

A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from the Burfell site in Iceland. The comparison reveals that the simulated AEP values based on the proposed Monte Carlo approach have a distribution that is in close agreement with actual AEP from two test wind turbines at the Burfell site, while the simulated AEP of the Weibull approach is such that the P50 and the scale are substantially lower and the P90 is higher. Thus, the Weibull approach yields AEP that is not in line with the actual variability in AEP, while the Monte Carlo approach gives a realistic estimate of the distribution of AEP.

Valid and reliable instruments are needed to measure how family members perceive support from nurses when a family member is experiencing serious illness. The purpose of this article is to describe the development and psychometric testing of a new instrument, the Iceland-Family Perceived Support Questionnaire (ICE-FPSQ). The concepts in the original version of the ICE-FPSQ (suggesting 24 items and 4 categories) were developed from the Calgary Family Intervention Model. In the first phase of the instrument construction, 179 family members answered the original ICE-FPSQ, and 236 answered the questionnaire in the second phase of testing. Principal Component Analysis (PCA) reduced the original questionnaire to 21 items. Cronbach’s α = .959 explained 68% of the total variance, with three factors emerging: (a) emotional support (α = .925), (b) recognition of families’ strengths (α = .926), and (c) cognitive support (α = .841). Confirmatory Factor Analyses (CFA) resulted in a final version of the questionnaire containing 14 items with total alpha of .961 and two factors: (a) cognitive support (α = .881) and (b) emotional support (α = .952). The instrument measures family’s perceptions of support provided by nurses and will be helpful in examining the usefulness of family nursing interventions.

Instruments that are able to capture changes related to an intervention are of great value to the scientific as well as to the clinical community. The Iceland-Expressive Family Functioning Questionnaire (ICE-EFFQ) measures expressive emotions, collaboration, problem solving, communication, and behavior in families experiencing a chronic or an acute illness. The conceptual framework of the Calgary Family Assessment Model (Wright & Leahey, 2009) was used to construct the original questionnaire of 45 items and 10 subcategories. A total of 557 family members with a recent illness experience of a close relative answered the ICE-EFFQ in three different studies. Principal component factor analysis reduced the original questionnaire to 22 items with five factors emerging and a total Cronbach’s alpha coefficient of α = 0.912 accounting for 60.3% of the total variability. Confirmatory factor analysis from two studies produced the final version of the questionnaire consisting of 17 items and four factors.

Introduction: Allergic disorders are an increasing health problem in many countries, in particular among children. We have evaluated the prevalence and manifestations of allergy in a cohort of young Icelanders for more than two decades. Variations in the epidemiology and clinical expression of allergy in different communities may help to identify etiological factors contributing to these disorders. Methods: A cohort of 179 children has been monitored for allergic manifestations for two decades, at the ages of two, four, eight, and 15 years, and most recently at the age of 21 years involving 120 of the participants. Results: Cumulative prevalences of 40%, 45%, and 29% have been observed, respectively, for rhinoconjunctivitis, eczema, and asthma during the study period. None had developed rhinoconjunctivitis at the age of about 2 years, but the point prevalence gradually increased to 33% at the age of 21 years. Conversely, the prevalence of eczema was 31% at the age of 2 years, but gradually declined to 8% at the age of 21 years. The prevalence of asthma peaked at 28% at the age of 4 years, but declined thereafter and has remained stable at about 13% from the age of eight to 21 years. Discussion: The prevalence of allergic diseases is high in Iceland among children and young individuals. Asthma and atopic eczema are very common in childhood, but decreases with age while the prevalence of rhinoconjunctivitis increases markedly. The very high and increasing prevalence of rhinoconjunctivitis among 15- to 21-year-old individuals is noteworthy.

Aim: Evaluation of long-term outcome of extremely low-birthweight (ELBW) teenagers born in Iceland in 1991–1995. Method: Participants, 30 of 35 ELBW survivors and 30 full term control teenagers (14–19 years), were assessed for disabilities, health problems and learning difficulties. Results of national standardized tests in mathematics and Icelandic language were compared with results of neurodevelopmental assessment at 5 years of age. Results: A quarter of the ELBW teenagers had disabilities. All were initially diagnosed with neurodevelopmental disorders early in life and neurosensory and/or intellectual disabilities were confirmed later in childhood. Chronic lung disorders, neurological problems and psychiatric disorders were most common health problems. Growth parameters were within normal limits for most of the ELBW teenagers. Learning difficulties affected 57% of the ELBW teenagers, 20% attended special education classes and 37% required special teaching. Results of national standardized tests were significantly lower for ELBW survivors and were significantly related to the results of neurodevelopmental assessment at 5 years of age. Interpretation: A quarter of ELBW teenagers have disabilities albeit most of them mild. Chronic health problems and learning difficulties affect many ELBW survivors. Changes with time emphasize need of long-term follow-up.

River discharges are traditionally modeled by employing a standard power-law methodology. Recently, the Bayesian approached has successfully been applied to improve the estimates of the standard power-law. In this article, an extension to the standard power-law based on Bayesian B-splines is developed and tested on data sets from 61 different rivers. The extended model is evaluated against the standard power-law using two measures, the Deviance Information Criterion and Bayes factor. The extended model captures deviations in the data from the standard power-law but reduces to the standard power-law when that model is adequate. The standard power-law is inadequate for 26% of the rivers while the extended model provides an adequate fit in all of those cases and for the remaining 74% of the rivers the extended model and the power-law model both give adequate fit with almost identical estimates.

The risk factors for febrile, respiratory, and gastrointestinal illnesses in preschool children at day care centres have not been adequately identified and may differ between countries.In this repeated cross-sectional and longitudinal study of children at day care centres, we analyzed various potential risk factors for infectious illnesses using a mixed effect hierarchical Poisson regression model. The results of the analyses and the incidence rates of these illnesses are reported herein.The study lasted 2.5 y (2000-2003) and was divided into five 6-month periods (seasons). Nine hundred and seventeen children participated, and their mean age within each season was 3.6-4.1 y. The only consistent risk factors identified were young age and winter season. No consistent risk factors at the day care centres (facilities or hygiene practices) were identified. The incidences of the febrile, respiratory, and gastrointestinal illnesses varied significantly between seasons (winter/summer) and between age groups.Risk factors of infectious illnesses are difficult to identify and because they may vary between countries, international recommendations on expensive intervention strategies are not justifiable. Rates of respiratory illnesses in Icelandic preschool children appear higher than in other countries, but rates of gastrointestinal illnesses lower.

Bayesian hierarchical modeling is applied to the analysis of annual minimum and maximum temperatures. In both cases the generalized extreme value distribution is selected as the marginal distribution at each site due to its flexibility and theoretical basis. This distribution has three unknown parameters, a location parameter, a scale parameter and a shape parameter. The location and scale parameters are assumed to vary across sites while the shape parameter is assumed to be the same for all sites. The location and the scale parameters are modeled as two independent Gaussian spatial processes which are governed by the Matern correlation function. These spatial processes play a central role in spatial predictions at unobserved sites. The location parameter is also modeled with a common time effect which includes a linear trend and a linear site effect with altitude, distance to open sea, latitude and longitude as covariates. This model allows for spatial predictions for any set of unobserved sites, the scale of the grid can be as fine as possible as long as the covariates are observed at each of the unobserved sites. Data on the annual minimum and maximum temperatures in Iceland from 1961 to 2009 at 72 sites are analyzed and used to predict the 2nd percentile of the minimum temperature for the 12-month period from July 2011 to June 2012 and the 98th percentile of maximum temperature for the year 2011 for a large set of unobserved sites across Iceland at which the covariates are available. The spatial predictions reveal that the 2nd percentile of minimum temperature for 2011 to 2012 reaches −35°C in the central part of Iceland and ranges from −12 to −22°C around the coast of Iceland with the lowest coastal temperatures in the Northern part and the Northeastern part. The 98th percentile of maximum temperature in 2011 ranges from 10 to 30°C where the warmest areas are found in the Eastern part, the North-Eastern part and the Southern part of the country. The estimated increase in minimum and maximum temperatures over the years 1961 to 2009 is 0.71 and 0.47°C per decade, respectively, while the average annual temperature increased 0.24°C per decade over the same period.