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Journal articles on the topic 'Bayesian hierarchical spatiotemporal models'

Author: Grafiati

Published: 13 July 2022

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1

Alghamdi, Taghreed, Khalid Elgazzar, and Taysseer Sharaf. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling." Future Internet 13, no. 9 (August 30, 2021): 225. http://dx.doi.org/10.3390/fi13090225.

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Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

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Cosandey-Godin, Aurelie, Elias Teixeira Krainski, Boris Worm, and Joanna Mills Flemming. "Applying Bayesian spatiotemporal models to fisheries bycatch in the Canadian Arctic." Canadian Journal of Fisheries and Aquatic Sciences 72, no. 2 (February 2015): 186–97. http://dx.doi.org/10.1139/cjfas-2014-0159.

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Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable species such as sharks, is a major challenge for contemporary fisheries management. Here we establish integrated nested Laplace approximations (INLA) and stochastic partial differential equations (SPDE) as two powerful tools for modelling patterns of bycatch through time and space. These novel, computationally fast approaches are applied to fit zero-inflated hierarchical spatiotemporal models to Greenland shark (Somniosus microcephalus) bycatch data from the Baffin Bay Greenland halibut (Reinhardtius hippoglossoides) gillnet fishery. Results indicate that Greenland shark bycatch is clustered in space and time, varies significantly from year to year, and there are both tractable factors (number of gillnet panels, total Greenland halibut catch) and physical features (bathymetry) leading to the high incidence of Greenland shark bycatch. Bycatch risk could be reduced by limiting access to spatiotemporal hotspots or by establishing a maximum number of panels per haul. Our method explicitly models the spatiotemporal correlation structure inherent in bycatch data at a very reasonable computational cost, such that the forecasting of bycatch patterns and simulating conservation strategies becomes more accessible.

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Blangiardo, Marta, Areti Boulieri, Peter Diggle, Frédéric B. Piel, Gavin Shaddick, and Paul Elliott. "Advances in spatiotemporal models for non-communicable disease surveillance." International Journal of Epidemiology 49, Supplement_1 (April 1, 2020): i26—i37. http://dx.doi.org/10.1093/ije/dyz181.

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Abstract Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.

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Bi, Rujia, Yan Jiao, Can Zhou, and Eric Hallerman. "A Bayesian spatiotemporal approach to inform management unit appropriateness." Canadian Journal of Fisheries and Aquatic Sciences 76, no. 2 (February 2019): 217–37. http://dx.doi.org/10.1139/cjfas-2017-0526.

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One prerequisite for sustainable fisheries management is to match management actions with biological processes. Stocks are fundamental units for fisheries management. Understanding the spatial structure of fish stocks is critical for conducting defensible stock assessments, applying efficient management strategies, and ensuring the sustainability of fish stocks. Yellow perch (Perca flavescens) is an important fishery in the Great Lakes. The appropriateness of its management units (MUs) has been identified as of high concern by the Great Lakes Fisheries Commission. Here we established integrated nested Laplace approximations and stochastic partial differential equations as two powerful tools for modeling spatiotemporal patterns of fish relative biomass. These fast computational approaches were applied to fit a Bayesian hierarchical hurdle model to occurrence and positive mass of yellow perch caught in gill-net surveys. Yellow perch relative biomass index has clear temporal variation and spatial heterogeneity, with the two middle MUs for yellow perch within Lake Erie merging together. The method explicitly models the spatiotemporal correlation structure inherent in biomass survey data at a reasonable computational cost, and the estimated spatiotemporal correlation informs stock structure.

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Neelon, Brian, Howard H. Chang, Qiang Ling, and Nicole S. Hastings. "Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits." Statistical Methods in Medical Research 25, no. 6 (September 30, 2016): 2558–76. http://dx.doi.org/10.1177/0962280214527079.

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Motivated by a study exploring spatiotemporal trends in emergency department use, we develop a class of two-part hurdle models for the analysis of zero-inflated areal count data. The models consist of two components—one for the probability of any emergency department use and one for the number of emergency department visits given use. Through a hierarchical structure, the models incorporate both patient- and region-level predictors, as well as spatially and temporally correlated random effects for each model component. The random effects are assigned multivariate conditionally autoregressive priors, which induce dependence between the components and provide spatial and temporal smoothing across adjacent spatial units and time periods, resulting in improved inferences. To accommodate potential overdispersion, we consider a range of parametric specifications for the positive counts, including truncated negative binomial and generalized Poisson distributions. We adopt a Bayesian inferential approach, and posterior computation is handled conveniently within standard Bayesian software. Our results indicate that the negative binomial and generalized Poisson hurdle models vastly outperform the Poisson hurdle model, demonstrating that overdispersed hurdle models provide a useful approach to analyzing zero-inflated spatiotemporal data.

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Song, Chao, Yaqian He, Yanchen Bo, Jinfeng Wang, Zhoupeng Ren, and Huibin Yang. "Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models." International Journal of Environmental Research and Public Health 15, no. 7 (July 12, 2018): 1476. http://dx.doi.org/10.3390/ijerph15071476.

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Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health.

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Gopalan, Giri, Birgir Hrafnkelsson, Guðfinna Aðalgeirsdóttir, Alexander H. Jarosch, and Finnur Pálsson. "A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions." Cryosphere 12, no. 7 (July 11, 2018): 2229–48. http://dx.doi.org/10.5194/tc-12-2229-2018.

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Abstract. 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.

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Paradinas, I., D. Conesa, A. López-Quílez, A. Esteban, LM Martín López, JM Bellido, and MG Pennino. "Assessing the spatiotemporal persistence of fish distributions: a case study on two red mullet species (Mullus surmuletus and M. barbatus) in the western Mediterranean." Marine Ecology Progress Series 644 (June 25, 2020): 173–85. http://dx.doi.org/10.3354/meps13366.

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Understanding the spatiotemporal persistence of fish distributions is key to defining fish hotspots and effective fisheries-restricted areas (FRAs). Hierarchical Bayesian spatiotemporal models provide an excellent framework to understand these distributions, as they can accommodate different spatiotemporal behaviour in the data, primarily due to their flexibility. The aim of this research was to characterize the fundamental behavioural patterns of fish as persistent, opportunistic or progressive by comparing different spatiotemporal model structures in order to provide better information for marine spatial planning. To illustrate this method, the spatiotemporal distributions of 2 sympatric Mullidae species, the striped red mullet Mullus surmuletus and the red mullet M. barbatus, were analysed. The occurrence of each species, its conditional-to-presence abundance and median length were analysed using Mediterranean trawl survey data from the western Mediterranean between 2000 and 2016. Results demonstrate that there are various common hotspots of both species distributed along the Iberian coast. The convenient persistent spatiotemporal distribution of these hotspots facilitates the configuration of a network of connected FRAs for red mullets in the study area.

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Baer, Daniel R., Andrew B. Lawson, and Jane E. Joseph. "Joint space–time Bayesian disease mapping via quantification of disease risk association." Statistical Methods in Medical Research 30, no. 1 (January 2021): 35–61. http://dx.doi.org/10.1177/0962280220938975.

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Alzheimer’s disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer’s disease risk in order to preclude its inception in patients. Characterizing Alzheimer’s disease risk can be accomplished at the population-level by the space–time modeling of Alzheimer’s disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer’s disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer’s disease risk over space and time. From an application of these models to real-world Alzheimer’s disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer’s disease risk.

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Song, Li, Yang Li, Wei (David) Fan, and Peijie Wu. "Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: Insights from different hierarchical Bayesian random-effects models." Analytic Methods in Accident Research 28 (December 2020): 100137. http://dx.doi.org/10.1016/j.amar.2020.100137.

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Barboza, Gia Elise. "The Geography of Child Maltreatment: A Spatiotemporal Analysis Using Bayesian Hierarchical Analysis With Integrated Nested Laplace Approximation." Journal of Interpersonal Violence 34, no. 1 (April 1, 2016): 50–80. http://dx.doi.org/10.1177/0886260516639583.

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This study quantifies the spatiotemporal risk of child abuse and neglect in Los Angeles at the census tract level over a recent 4-year period, identifies areas of increased risk, and evaluates the role of structural disadvantage in substantiated child maltreatment referrals. Child maltreatment data on 83,379 child maltreatment cases in 1,678 census tracts spanning 2006-2009 were obtained from the Los Angeles County Department of Children and Family Services. Substantiated referral counts were analyzed across census tracts with Bayesian hierarchical spatial models using integrated nested Laplace approximations. Results showed that the unadjusted yearly rate of child abuse and neglect held fairly steady over the study period decreasing by only 2.57%. However, the temporal term in the spatiotemporal model reflected a downward trend beginning in 2007. High rates of abuse and neglect were predicted by several neighborhood-level measures of structural burden. Every 1-unit decrease in the social vulnerability index reduced the risk of child abuse and neglect by 98.3% (95% CrI = 1.869-2.1042) while every 1-unit increase in the Black–White dissimilarity index decreased child abuse and neglect risk by 70.6%. The interaction of these variables demonstrated the protective effect of racial heterogeneity in socially vulnerable neighborhoods. No such effect was found in neighborhoods characterized by low levels of vulnerability. Population-based child abuse and neglect prevention and intervention efforts should be aided by the characteristics of neighborhoods that demonstrate strong spatial patterns even after accounting for the role of race and place.

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Gilbert, L., E. Rouby, E. Tew-Kaï, J. Spitz, H. Peltier, V. Quilfen, and M. Authier. "Spatiotemporal models highlight influence of oceanographic conditions on common dolphin bycatch risk in the Bay of Biscay." Marine Ecology Progress Series 679 (November 25, 2021): 195–212. http://dx.doi.org/10.3354/meps13894.

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The population of short-beaked common dolphins Delphinus delphis of the Bay of Biscay (northeast Atlantic) has been subjected to potentially dangerous levels of bycatch since the 1990s. As the phenomenon intensifies, it represents a potent threat to the population. Here, we investigated the relationship between bycatch mortality and oceanographic processes. We assumed that oceanographic processes spatiotemporally structure the availability and aggregation of prey, creating areas prone to attract both common dolphins and fish targeted by fisheries. We used 2 datasets from 2012 to 2019: oceanographic data resulting from a circulation model and mortality data inferred from strandings. The latter allows location of mortality areas and quantification of the intensity of mortality events at sea. We fitted a series of spatiotemporal hierarchical Bayesian models using integrated nested Laplace approximations (INLA). Results provided first insights on how bycatch of common dolphins in the Bay of Biscay might be related to key seasonal and dynamic oceanographic features. We showed that from a statistical predictive point of view, the monthly trend of 2019 bycatch mortality could be predicted with few oceanographic covariates. This study highlights how gaining knowledge about environmental influences on interactions between short-beaked common dolphins and fisheries could have great conservation and management value. Identified relationships with oceanographic covariates were complex, as expected given the dynamic aspects of oceanographic processes, dolphins and fisheries distributions. Further research focusing on smaller time scales is needed to elucidate proximal drivers of common dolphin bycatch in the Bay of Biscay.

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Lubinda, Jailos, Yaxin Bi, Busiku Hamainza, Ubydul Haque, and Adrian J. Moore. "Modelling of malaria risk, rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden." PLOS Computational Biology 17, no. 3 (March 1, 2021): e1008669. http://dx.doi.org/10.1371/journal.pcbi.1008669.

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While mortality from malaria continues to decline globally, incidence rates in many countries are rising. Within countries, spatial and temporal patterns of malaria vary across communities due to many different physical and social environmental factors. To identify those areas most suitable for malaria elimination or targeted control interventions, we used Bayesian models to estimate the spatiotemporal variation of malaria risk, rates, and trends to determine areas of high or low malaria burden compared to their geographical neighbours. We present a methodology using Bayesian hierarchical models with a Markov Chain Monte Carlo (MCMC) based inference to fit a generalised linear mixed model with a conditional autoregressive structure. We modelled clusters of similar spatiotemporal trends in malaria risk, using trend functions with constrained shapes and visualised high and low burden districts using a multi-criterion index derived by combining spatiotemporal risk, rates and trends of districts in Zambia. Our results indicate that over 3 million people in Zambia live in high-burden districts with either high mortality burden or high incidence burden coupled with an increasing trend over 16 years (2000 to 2015) for all age, under-five and over-five cohorts. Approximately 1.6 million people live in high-incidence burden areas alone. Using our method, we have developed a platform that can enable malaria programs in countries like Zambia to target those high-burden areas with intensive control measures while at the same time pursue malaria elimination efforts in all other areas. Our method enhances conventional approaches and measures to identify those districts which had higher rates and increasing trends and risk. This study provides a method and a means that can help policy makers evaluate intervention impact over time and adopt appropriate geographically targeted strategies that address the issues of both high-burden areas, through intensive control approaches, and low-burden areas, via specific elimination programs.

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BRANSCUM, A. J., A. M. PEREZ, W. O. JOHNSON, and M. C. THURMOND. "Bayesian spatiotemporal analysis of foot-and-mouth disease data from the Republic of Turkey." Epidemiology and Infection 136, no. 6 (July 5, 2007): 833–42. http://dx.doi.org/10.1017/s0950268807009065.

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SUMMARYA flexible hierarchical Bayesian spatiotemporal regression model for foot-and-mouth disease (FMD) was applied to data on the annual number of reported FMD cases in Turkey from 1996 to 2003. The longitudinal component of the model was specified as a latent province-specific stochastic process. This stochastic process can accommodate various types of FMD temporal profiles. The model accounted for differences in FMD occurrence across provinces and for spatial correlation. Province-level covariate information was incorporated into the analysis. Results pointed to a decreasing trend in the number of FMD cases in western Turkey and an increasing trend in eastern Turkey from 1996 to 2003. The model also identified provinces with high and with low propensities for FMD occurrence. The model's use of flexible structures for temporal trend and of generally applicable methods for spatial correlation has broad application to predicting future spatiotemporal distributions of disease in other regions of the world.

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Pinto, Cecilia, Morgane Travers-Trolet, Jed I. Macdonald, Etienne Rivot, and Youen Vermard. "Combining multiple data sets to unravel the spatiotemporal dynamics of a data-limited fish stock." Canadian Journal of Fisheries and Aquatic Sciences 76, no. 8 (August 2019): 1338–49. http://dx.doi.org/10.1139/cjfas-2018-0149.

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The biological status of many commercially exploited fishes remains unknown, mostly due to a lack of data necessary for their assessment. Investigating the spatiotemporal dynamics of such species can lead to new insights into population processes and foster a path towards improved spatial management decisions. Here, we focused on striped red mullet (Mullus surmuletus), a widespread yet data-limited species of high commercial importance. Aiming to quantify range dynamics in this data-poor scenario, we combined fishery-dependent and -independent data sets through a series of Bayesian mixed-effects models designed to capture monthly and seasonal occurrence patterns near the species’ northern range limit across 20 years. Combining multiple data sets allowed us to cover the entire distribution of the northern population of M. surmuletus, exploring dynamics at different spatiotemporal scales and identifying key environmental drivers (i.e., sea surface temperature, salinity) that shape occurrence patterns. Our results demonstrate that even when process and (or) observation uncertainty is high, or when data are sparse, if we combine multiple data sets within a hierarchical modelling framework, accurate and useful spatial predictions can still be made.

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Gunderson, Annika K., Rani E. Kumar, Cristina Recalde-Coronel, Luis E. Vasco, Andree Valle-Campos, Carlos F. Mena, Benjamin F. Zaitchik, Andres G. Lescano, William K. Pan, and Mark M. Janko. "Malaria Transmission and Spillover across the Peru–Ecuador Border: A Spatiotemporal Analysis." International Journal of Environmental Research and Public Health 17, no. 20 (October 13, 2020): 7434. http://dx.doi.org/10.3390/ijerph17207434.

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Border regions have been implicated as important hot spots of malaria transmission, particularly in Latin America, where free movement rights mean that residents can cross borders using just a national ID. Additionally, rural livelihoods largely depend on short-term migrants traveling across borders via the Amazon’s river networks to work in extractive industries, such as logging. As a result, there is likely considerable spillover across country borders, particularly along the border between Peru and Ecuador. This border region exhibits a steep gradient of transmission intensity, with Peru having a much higher incidence of malaria than Ecuador. In this paper, we integrate 13 years of weekly malaria surveillance data collected at the district level in Peru and the canton level in Ecuador, and leverage hierarchical Bayesian spatiotemporal regression models to identify the degree to which malaria transmission in Ecuador is influenced by transmission in Peru. We find that increased case incidence in Peruvian districts that border the Ecuadorian Amazon is associated with increased incidence in Ecuador. Our results highlight the importance of coordinated malaria control across borders.

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Myer, Mark H., Chelsea M. Fizer, Kenneth R. Mcpherson, Anne C. Neale, Andrew N. Pilant, Arturo Rodriguez, Pai-Yei Whung, and John M. Johnston. "Mapping Aedes aegypti (Diptera: Culicidae) and Aedes albopictus Vector Mosquito Distribution in Brownsville, TX." Journal of Medical Entomology 57, no. 1 (August 10, 2019): 231–40. http://dx.doi.org/10.1093/jme/tjz132.

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Abstract Aedes mosquitoes are vectors of several emerging diseases and are spreading worldwide. We investigated the spatiotemporal dynamics of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) mosquito trap captures in Brownsville, TX, using high-resolution land cover, socioeconomic, and meteorological data. We modeled mosquito trap counts using a Bayesian hierarchical mixed-effects model with spatially correlated residuals. The models indicated an inverse relationship between temperature and mosquito trap counts for both species, which may be due to the hot and arid climate of southern Texas. The temporal trend in mosquito populations indicated Ae. aegypti populations peaking in the late spring and Ae. albopictus reaching a maximum in winter. Our results indicated that seasonal weather variation, vegetation height, human population, and land cover determine which of the two Aedes species will predominate.

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Breivik, Olav Nikolai, Geir Storvik, and Kjell Nedreaas. "Latent Gaussian models to decide on spatial closures for bycatch management in the Barents Sea shrimp fishery." Canadian Journal of Fisheries and Aquatic Sciences 73, no. 8 (August 2016): 1271–80. http://dx.doi.org/10.1139/cjfas-2015-0322.

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In the Barents Sea and adjacent water, fishing grounds are closed for shrimp fishing by the Norwegian Directorate of Fisheries Monitoring and Surveillance Service (MSS) if the expected number of juvenile fish caught are predicted to exceed a certain limit per kilogram shrimp (Pandalus borealis). Today, a simple ratio estimator, which does not fully utilize all data available, is in use. In this paper, we construct a Bayesian hierarchical spatiotemporal model for improved prediction of the bycatch ratio in the Barents Sea shrimp fishery. More predictable bycatch will be an advantage for the MSS because of more correct decisions and better resource allocation and also for the fishermen because of more predictable fishing grounds. The model assumes that the occurrence of shrimp and juvenile Atlantic cod (Gadus morhua) can be modeled by linked regression models containing several covariates (including 0-group abundance estimates) and random effects modeled as Gaussian fields. Integrated nested Laplace approximations is applied for fast calculation. The method is applied to prediction of the bycatch ratio for Atlantic cod.

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Sumetsky, Natalie, Jessica G. Burke, and Christina Mair. "Opioid-related diagnoses and HIV, HCV and mental disorders: using Pennsylvania hospitalisation data to assess community-level relationships over space and time." Journal of Epidemiology and Community Health 73, no. 10 (July 2, 2019): 935–40. http://dx.doi.org/10.1136/jech-2019-212551.

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BackgroundWe assessed the community-level spatiotemporal connexions between hospitalisations for common opioid comorbidities (HIV, hepatitis C (HCV) and mental disorders) and opioid-related hospitalisations in the current and previous year.MethodsWe used Bayesian hierarchical spatiotemporal Poisson regression with conditionally autoregressive spatial effects to assess counts of HCV-related, HIV-related and mental disorder–related hospitalisations at the ZIP code level from 2004 to 2014 in Pennsylvania. Models included rates of current-year and previous-year opioid-related hospitalisations as well as covariates measuring demographic and environmental characteristics.ResultsAfter adjusting for measures of demographic and environmental characteristics, current-year and previous-year opioid-related hospitalisations were associated with higher risk of HCV, HIV and mental disorders. The relative risks and 95% credible intervals for previous-year opioid-related hospitalisations were 1.092 (1.078 to 1.106) for HCV, 1.098 (1.068 to 1.126) for HIV and 1.020 (1.013 to 1.027) for mental disorders.ConclusionPrevious-year opioid-related hospitalisations are connected to common comorbid conditions such as HCV, HIV and mental disorders, illustrating some of the broader health-related impacts of the opioid epidemic. Public health interventions focused on the opioid epidemic must consider individual community needs and comorbid diagnoses.

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Yu, Zhuoran, Yimeng Duan, Shen Zhang, Xin Liu, and Kui Li. "A Spatiotemporal Prediction Model for Regional Scheduling of Shared Bicycles Based on the INLA Method." Journal of Advanced Transportation 2021 (July 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/4959504.

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Dock-less bicycle-sharing programs have been widely accepted as an efficient mode to benefit health and reduce congestions. And modeling and prediction has always been a core proposition in the field of transportation. Most of the existing demand prediction models for shared bikes take regions as research objects; therefore, a POI-based method can be a beneficial complement to existing research, including zone-level, OD-level, and station-level techniques. Point of interest (POI) is the location description of spatial entities, which can reflect the cycling route characteristics for both commuting and noncommuting trips to a certain extent, and is also the main generating point and attraction point of shared-bike travel flow. In this study, we make an effort to model a POI-level cycling demand with a Bayesian hierarchical method. The proposed model combines the integrated nested Laplace approximation (INLA) and random partial differential equation (SPDE) to cope with the huge computation in the modeling process. In particular, we have adopted the dock-less bicycle-sharing rental records of Mobike as a case study to validate our method; the study area was one of the fastest growing urban districts in Shanghai in August 2016. The operation results show that the method can help better understand, measure, and characterize spatiotemporal patterns of bike-share ridership at the POI level and quantify the impact of the spatiotemporal effect on bicycle-sharing use.

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Herrmann, Christian, Penelope Vounatsou, Beat Thürlimann, Nicole Probst-Hensch, Christian Rothermundt, and Silvia Ess. "Impact of mammography screening programmes on breast cancer mortality in Switzerland, a country with different regional screening policies." BMJ Open 8, no. 3 (March 2018): e017806. http://dx.doi.org/10.1136/bmjopen-2017-017806.

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IntroductionIn the past decades, mortality due to breast cancer has declined considerably in Switzerland and other developed countries. The reasons for this decline remain controversial as several factors occurred almost simultaneously, including important advances in treatment approaches, breast cancer awareness and the introduction of mammography screening programmes in many European countries. In Switzerland, mammography screening programmes (MSPs) have existed in some regions for over 20 years but do not yet exist in others. This offers the possibility to analyse its effects with modern spatiotemporal methodology. We aimed to assess the spatiotemporal patterns and the effect of MSPs on breast cancer mortality.SettingSwitzerland.ParticipantsThe study covers breast cancer deaths of the female population of Switzerland during the period 1969–2012. We retrieved data from the Swiss Federal Statistical Office aggregated on a small-area level.DesignWe fitted Bayesian hierarchical spatiotemporal models on death rates indirectly standardised by national references. We used linguistic region, degree of urbanisation, duration of population-based screening programmes and socioeconomic index as covariates.ResultsIn Switzerland, breast cancer mortality in women slightly increased until 1989–1992 and declined strongly thereafter. Until 2009–2012, the standardised mortality ratio declined to 57% (95% CI 54% to 60%) of the 1969–1972 value. None of the other coefficients of the spatial regressions had a significant effect on breast cancer mortality. In 2009–2012, no region had significantly elevated or reduced breast cancer mortality at 95% credible interval level compared with the national mean.ConclusionThere has been a strong reduction of breast cancer mortality from the 1990s onwards. No important spatial disparities were observed. The factors studied (urbanisation, language, duration of population-based MSP and socioeconomic characteristics) did not seem to have an influence on them. Low participation rates and opportunistic screening use may have contributed to the low impact of MSPs.

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Lachs, Liam, John C. Bythell, Holly K. East, Alasdair J. Edwards, Peter J. Mumby, William J. Skirving, Blake L. Spady, and James R. Guest. "Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching." Remote Sensing 13, no. 14 (July 7, 2021): 2677. http://dx.doi.org/10.3390/rs13142677.

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Increasingly intense marine heatwaves threaten the persistence of many marine ecosystems. Heat stress-mediated episodes of mass coral bleaching have led to catastrophic coral mortality globally. Remotely monitoring and forecasting such biotic responses to heat stress is key for effective marine ecosystem management. The Degree Heating Week (DHW) metric, designed to monitor coral bleaching risk, reflects the duration and intensity of heat stress events and is computed by accumulating SST anomalies (HotSpot) relative to a stress threshold over a 12-week moving window. Despite significant improvements in the underlying SST datasets, corresponding revisions of the HotSpot threshold and accumulation window are still lacking. Here, we fine-tune the operational DHW algorithm to optimise coral bleaching predictions using the 5 km satellite-based SSTs (CoralTemp v3.1) and a global coral bleaching dataset (37,871 observations, National Oceanic and Atmospheric Administration). After developing 234 test DHW algorithms with different combinations of the HotSpot threshold and accumulation window, we compared their bleaching prediction ability using spatiotemporal Bayesian hierarchical models and sensitivity–specificity analyses. Peak DHW performance was reached using HotSpot thresholds less than or equal to the maximum of monthly means SST climatology (MMM) and accumulation windows of 4–8 weeks. This new configuration correctly predicted up to an additional 310 bleaching observations globally compared to the operational DHW algorithm, an improved hit rate of 7.9%. Given the detrimental impacts of marine heatwaves across ecosystems, heat stress algorithms could also be fine-tuned for other biological systems, improving scientific accuracy, and enabling ecosystem governance.

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McGlothlin, Anna E., and Kert Viele. "Bayesian Hierarchical Models." JAMA 320, no. 22 (December 11, 2018): 2365. http://dx.doi.org/10.1001/jama.2018.17977.

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Wikle, Christopher K., Ralph F. Milliff, Doug Nychka, and L. Mark Berliner. "Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds." Journal of the American Statistical Association 96, no. 454 (June 2001): 382–97. http://dx.doi.org/10.1198/016214501753168109.

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25

Torabi, Mahmoud, and Rhonda J. Rosychuk. "Hierarchical Bayesian Spatiotemporal Analysis of Childhood Cancer Trends." Geographical Analysis 44, no. 2 (April 2012): 109–20. http://dx.doi.org/10.1111/j.1538-4632.2012.00839.x.

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26

Williams, Daniel. "Hierarchical Bayesian models of delusion." Consciousness and Cognition 61 (May 2018): 129–47. http://dx.doi.org/10.1016/j.concog.2018.03.003.

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27

Zhuang, Lili, and Noel Cressie. "Bayesian hierarchical statistical SIRS models." Statistical Methods & Applications 23, no. 4 (November 2014): 601–46. http://dx.doi.org/10.1007/s10260-014-0280-9.

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28

Zou, Jian, Zhongqiang Zhang, and Hong Yan. "A hybrid hierarchical Bayesian model for spatiotemporal surveillance data." Statistics in Medicine 37, no. 28 (July 23, 2018): 4216–33. http://dx.doi.org/10.1002/sim.7909.

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29

Zhu, Yuxin, Yanchen Bo, Jinzong Zhang, and Yuexiang Wang. "Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model." Journal of Atmospheric and Oceanic Technology 35, no. 1 (January 2018): 91–109. http://dx.doi.org/10.1175/jtech-d-17-0116.1.

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AbstractThis study focuses on merging MODIS-mapped SSTs with 4-km spatial resolution and AMSR-E optimally interpolated SSTs at 25-km resolution. A new data fusion method was developed—the Spatiotemporal Hierarchical Bayesian Model (STHBM). This method, which is implemented through the Markov chain Monte Carlo technique utilized to extract inferential results, is specified hierarchically by decomposing the SST spatiotemporal process into three subprocesses, that is, the spatial trend process, the seasonal cycle process, and the spatiotemporal random effect process. Spatial-scale transformation and spatiotemporal variation are introduced into the fusion model through the data model and model parameters, respectively, with suitably selected link functions. Compared with two modern spatiotemporal statistical methods—the Bayesian maximum entropy and the robust fixed rank kriging—STHBM has the following strength: it can simultaneously meet the expression of uncertainties from data and model, seamless scale transformation, and SST spatiotemporal process simulation. Utilizing multisensors’ complementation, merged data with complete spatial coverage, high resolution (4 km), and fine spatial pattern lying in MODIS SSTs can be obtained through STHBM. The merged data are assessed for local spatial structure, overall accuracy, and local accuracy. The evaluation results illustrate that STHBM can provide spatially complete SST fields with reasonably good data values and acceptable errors, and that the merged SSTs collect fine spatial patterns lying in MODIS SSTs with fine resolution. The accuracy of merged SSTs is between MODIS and AMSR-E SSTs. The contribution to the accuracy and the spatial pattern of the merged SSTs from the original MODIS SSTs is stronger than that of the original AMSR-E SSTs.

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Yan, Jun, Mary Kathryn Cowles, Shaowen Wang, and Marc P. Armstrong. "Parallelizing MCMC for Bayesian spatiotemporal geostatistical models." Statistics and Computing 17, no. 4 (July 31, 2007): 323–35. http://dx.doi.org/10.1007/s11222-007-9022-2.

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31

Hooten, Mevin B., Christopher K. Wikle, Robert M. Dorazio, and J. Andrew Royle. "Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions." Biometrics 63, no. 2 (January 23, 2007): 558–67. http://dx.doi.org/10.1111/j.1541-0420.2006.00725.x.

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32

Graham, Patrick. "Intelligent Smoothing Using Hierarchical Bayesian Models." Epidemiology 19, no. 3 (May 2008): 493–95. http://dx.doi.org/10.1097/ede.0b013e31816b7859.

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33

Anglim, Jeromy, and Sarah K. A. Wynton. "Hierarchical Bayesian models of subtask learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 41, no. 4 (2015): 957–74. http://dx.doi.org/10.1037/xlm0000103.

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34

Roos, Małgorzata, Thiago G. Martins, Leonhard Held, and Håvard Rue. "Sensitivity Analysis for Bayesian Hierarchical Models." Bayesian Analysis 10, no. 2 (June 2015): 321–49. http://dx.doi.org/10.1214/14-ba909.

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35

Campitelli, Guillermo, and Guillermo Macbeth. "Hierarchical Graphical Bayesian Models in Psychology." Revista Colombiana de Estadística 37, no. 2Spe (December 26, 2014): 319–39. http://dx.doi.org/10.15446/rce.v37n2spe.47940.

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36

Kemp, Charles, Amy Perfors, and Joshua B. Tenenbaum. "Learning overhypotheses with hierarchical Bayesian models." Developmental Science 10, no. 3 (May 2007): 307–21. http://dx.doi.org/10.1111/j.1467-7687.2007.00585.x.

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37

Glassen, Thomas, and Verena Nitsch. "Hierarchical Bayesian models of cognitive development." Biological Cybernetics 110, no. 2-3 (May 24, 2016): 217–27. http://dx.doi.org/10.1007/s00422-016-0686-6.

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38

Escobar, Michael D. "Nonparametric Bayesian methods in hierarchical models." Journal of Statistical Planning and Inference 43, no. 1-2 (January 1995): 97–106. http://dx.doi.org/10.1016/0378-3758(94)00011-j.

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39

Erickson, Richard A., Daniel S. Stich, and Jillian L. Hebert. "fishStan: Hierarchical Bayesian models for fisheries." Journal of Open Source Software 7, no. 71 (March 21, 2022): 3444. http://dx.doi.org/10.21105/joss.03444.

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40

Laferriere, Samuel, Marco Bonizzato, Sandrine L. Cote, Numa Dancause, and Guillaume Lajoie. "Hierarchical Bayesian Optimization of Spatiotemporal Neurostimulations for Targeted Motor Outputs." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 6 (June 2020): 1452–60. http://dx.doi.org/10.1109/tnsre.2020.2987001.

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41

Silva, Giovani L., C. B. Dean, Théophile Niyonsenga, and Alain Vanasse. "Hierarchical Bayesian spatiotemporal analysis of revascularization odds using smoothing splines." Statistics in Medicine 27, no. 13 (2008): 2381–401. http://dx.doi.org/10.1002/sim.3094.

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42

Hagmayer, York, and Ralf Mayrhofer. "Hierarchical Bayesian models as formal models of causal reasoning." Argument & Computation 4, no. 1 (March 2013): 36–45. http://dx.doi.org/10.1080/19462166.2012.700321.

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43

Mizutani, Daijiro, Kodai Matsuoka, and Kiyoyuki Kaito. "Hierarchical Bayesian Estimation of Mixed Hazard Models." IABSE Symposium Report 99, no. 23 (May 6, 2013): 638–45. http://dx.doi.org/10.2749/222137813806481428.

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44

Cook, Edward, MP Tingley, E. Wahl, and E. Zorita. "Bayesian hierarchical models for climate field reconstruction." PAGES news 19, no. 2 (July 2011): 78–79. http://dx.doi.org/10.22498/pages.19.2.78.

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45

STEINBAKK, GUNNHILDUR HÖGNADÓTTIR, and GEIR OLVE STORVIK. "Posterior Predictivep-values in Bayesian Hierarchical Models." Scandinavian Journal of Statistics 36, no. 2 (June 2009): 320–36. http://dx.doi.org/10.1111/j.1467-9469.2008.00630.x.

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46

Porter, Aaron T., Scott H. Holan, and Christopher K. Wikle. "Bayesian semiparametric hierarchical empirical likelihood spatial models." Journal of Statistical Planning and Inference 165 (October 2015): 78–90. http://dx.doi.org/10.1016/j.jspi.2015.04.002.

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47

Song, Hae-Ryoung, Andrew B. Lawson, and Daniela Nitcheva. "Bayesian hierarchical models for food frequency assessment." Canadian Journal of Statistics 38, no. 3 (March 10, 2010): 506–16. http://dx.doi.org/10.1002/cjs.10052.

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48

Dahlin, Johan, Fredrik Lindsten, Thomas B. Schön, and Adrian Wills. "Hierarchical Bayesian ARX models for robust inference." IFAC Proceedings Volumes 45, no. 16 (July 2012): 131–36. http://dx.doi.org/10.3182/20120711-3-be-2027.00318.

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49

Sale, S. E. "3D extinction mapping using hierarchical Bayesian models." Monthly Notices of the Royal Astronomical Society 427, no. 3 (November 20, 2012): 2119–31. http://dx.doi.org/10.1111/j.1365-2966.2012.21662.x.

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50

Huppenkothen, Daniela, Brendon J. Brewer, David W. Hogg, Iain Murray, Marcus Frean, Chris Elenbaas, Anna L. Watts, Yuri Levin, Alexander J. van der Horst, and Chryssa Kouveliotou. "DISSECTING MAGNETAR VARIABILITY WITH BAYESIAN HIERARCHICAL MODELS." Astrophysical Journal 810, no. 1 (September 1, 2015): 66. http://dx.doi.org/10.1088/0004-637x/810/1/66.

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