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Mavragani A, Bradley H, Li W, Bernson D, Dammann O, LaRochelle MR, Stopka TJ. Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach. JMIR Public Health Surveill 2023; 9:e41450. [PMID: 36763450 PMCID: PMC9960038 DOI: 10.2196/41450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 12/14/2022] [Accepted: 12/26/2022] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. OBJECTIVE The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. METHODS We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. RESULTS Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. CONCLUSIONS Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.
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Affiliation(s)
| | | | - Wenjun Li
- Department of Public Health, University of Massachusetts Lowell, Lowell, MA, United States
| | - Dana Bernson
- Office of Population Health, Department of Public Health, The Commonwealth of Massachusetts, Boston, MA, United States
| | - Olaf Dammann
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States.,Department of Gynecology and Obstetrics, Hannover Medical School, Hannover, Germany
| | - Marc R LaRochelle
- Clinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.,Grayken Center for Addiction, Boston Medical Center, Boston, MA, United States
| | - Thomas J Stopka
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States.,Department of Urban and Environmental Policy and Planning, Tufts University, Medford, MA, United States.,Department of Community Health, Tufts University, Medford, MA, United States
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Pavlyuk D. Temporal Aggregation Effects in Spatiotemporal Traffic Modelling. Sensors (Basel) 2020; 20:E6931. [PMID: 33291588 DOI: 10.3390/s20236931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 11/26/2020] [Accepted: 12/03/2020] [Indexed: 11/16/2022]
Abstract
Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.
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Maloney M, Merkle JA, Aadland D, Peck D, Horan RD, Monteith KL, Winslow T, Logan J, Finnoff D, Sims C, Schumaker B. Chronic wasting disease undermines efforts to control the spread of brucellosis in the Greater Yellowstone Ecosystem. Ecol Appl 2020; 30:e02129. [PMID: 32223053 DOI: 10.1002/eap.2129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 12/20/2019] [Accepted: 02/06/2020] [Indexed: 06/10/2023]
Abstract
Wildlife diseases pose a substantial threat to the provisioning of ecosystem services. We use a novel modeling approach to study the potential loss of these services through the imminent introduction of chronic wasting disease (CWD) to elk populations in the Greater Yellowstone Ecosystem (GYE). A specific concern is that concentrating elk at feedgrounds may exacerbate the spread of CWD, whereas eliminating feedgrounds may increase the number of elk on private ranchlands and the transmission of a second disease, brucellosis, from elk to cattle. To evaluate the consequences of management strategies given the threat of two concurrent wildlife diseases, we develop a spatiotemporal bioeconomic model. GPS data from elk and landscape attributes are used to predict migratory behavior and population densities with and without supplementary feeding. We use a 4,800 km2 area around Pinedale, Wyoming containing four existing feedgrounds as a case study. For this area, we simulate welfare estimates under a variety of management strategies. Our results indicate that continuing to feed elk could result in substantial welfare losses for the case-study region. Therefore, to maximize the present value of economic net benefits generated by the local elk population upon CWD's arrival in the region, wildlife managers may wish to consider discontinuing elk feedgrounds while simultaneously developing new methods to mitigate the financial impact to ranchers of possible brucellosis transmission to livestock. More generally, our methods can be used to weigh the costs and benefits of human-wildlife interactions in the presence of multiple disease risks.
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Affiliation(s)
- Matthew Maloney
- HS Strategy Department 01114, University of Utah Health Sciences, 102 S 200 E, Salt Lake City, Utah, 84109, USA
| | - Jerod A Merkle
- Department of Zoology and Physiology, University of Wyoming, 1000 East University Avenue, Laramie, Wyoming, 82071, USA
| | - David Aadland
- Department of Economics, University of Wyoming, 1000 E. University Avenue, Laramie, Wyoming, 82072, USA
| | - Dannele Peck
- USDA Agricultural Research Service, 1701 Centre Avenue, Fort Collins, Colorado, 80526, USA
| | - Richard D Horan
- Department of Agricultural, Food, and Resource Economics, Justin S Morrill Hall of Agriculture, Michigan State University, 446 W. Circle Drive, Rm 303B, East Lansing, Michigan, 48824, USA
| | - Kevin L Monteith
- Haub School of Environment and Natural Resources, Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, Bim Kendall House, 804 East Fremont Street, Laramie, Wyoming, 82072, USA
| | - Thach Winslow
- Wyoming Livestock Board, 1934 Wyott Drive, Cheyenne, Wyoming, 82002, USA
| | - Jim Logan
- Wyoming Livestock Board, 1934 Wyott Drive, Cheyenne, Wyoming, 82002, USA
| | - David Finnoff
- Department of Economics, University of Wyoming, 1000 E. University Avenue, Laramie, Wyoming, 82072, USA
| | - Charles Sims
- Howard H. Baker Jr. Center for Public Policy and Department of Economics, The University of Tennessee, 1640 Cumberland Avenue, Knoxville, Tennessee, 37996, USA
| | - Brant Schumaker
- Department of Veterinary Sciences, College of Agriculture & Natural Resources, University of Wyoming, 1174 Snowy Range Road, Laramie, Wyoming, 82070, USA
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Howell PE, Hossack BR, Muths E, Sigafus BH, Chenevert-Steffler A, Chandler RB. A statistical forecasting approach to metapopulation viability analysis. Ecol Appl 2020; 30:e02038. [PMID: 31709679 DOI: 10.1002/eap.2038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 09/13/2019] [Accepted: 09/26/2019] [Indexed: 06/10/2023]
Abstract
Conservation of at-risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two-step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently developed statistical PVAs differ because forecasts are made conditional on models fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 yr of data on the federally threatened Chiricahua leopard frog (Lithobates chiricahuensis) to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50-yr time horizon indicated that metapopulation extinction risk was <4% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 3.9% (95% CI 0-37%) by year 2066. Restoring six ponds by increasing their hydroperiod reduced extinction risk to <1% and greatly reduced uncertainty (95% CI 0-2%). Our results suggest that managers can mitigate the impacts of drought and environmental stochasticity on metapopulation viability by maintaining ponds that hold water throughout the year and keeping them free of invasive predators. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.
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Affiliation(s)
- Paige E Howell
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, 180 East Green Street, Georgia, 30602, USA
| | - Blake R Hossack
- U.S. Geological Survey, Northern Rocky Mountain Science Center, Missoula, Montana, 59801, USA
| | - Erin Muths
- U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado, 80526, USA
| | - Brent H Sigafus
- U.S. Geological Survey, Southwest Biological Science Center, Tucson, Arizona, 85721, USA
| | - Ann Chenevert-Steffler
- U.S. Fish & Wildlife Service, Buenos Aires NWR, P.O. Box 109, Sasabe, Arizona, 85633, USA
| | - Richard B Chandler
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, 180 East Green Street, Georgia, 30602, USA
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Min KD, Lee JY, So Y, Cho SI. Deforestation Increases the Risk of Scrub Typhus in Korea. Int J Environ Res Public Health 2019; 16:E1518. [PMID: 31035715 PMCID: PMC6539434 DOI: 10.3390/ijerph16091518] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 04/22/2019] [Accepted: 04/24/2019] [Indexed: 12/05/2022]
Abstract
Background: Scrub typhus is an important public health issue in Korea. Risk factors for scrub typhus include both individual-level factors and environmental drivers, and some are related to the increased density of vector mites and rodents, the natural hosts of the mites. In this regard, deforestation is a potential risk factor, because the deforestation-induced secondary growth of scrub vegetation may increase the densities of mites and rodents. To examine this hypothesis, this study investigated the association between scrub typhus and deforestation. Methods: We acquired district-level data for 2006-2017, including the number of cases of scrub typhus reported annually, deforestation level, and other covariates. Deforestation was assessed using preprocessed remote-sensing satellite data. Bayesian regression models, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models, were examined, and spatial autocorrelation was considered in hierarchical models. A sensitivity analysis was conducted using different accumulation periods for the deforestation level to examine the robustness of the association. Results: The final models showed a significant association between deforestation and the incidence of scrub typhus (relative risk = 1.20, 95% credible interval = 1.15-1.24). The sensitivity analysis gave consistent results, and a potential long-term effect of deforestation for up to 5 years was shown. Conclusion: The results support the potential public health benefits of forest conservation by suppressing the risk of scrub typhus, implying the need for strong engagement of public health sectors in conservation issues from a One Health perspective.
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Affiliation(s)
- Kyung-Duk Min
- Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
| | - Ju-Yeun Lee
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
| | - Yeonghwa So
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
| | - Sung-Il Cho
- Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
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Anderson SC, Ward EJ. Black swans in space: modeling spatiotemporal processes with extremes. Ecology 2018; 100:e02403. [PMID: 29901233 DOI: 10.1002/ecy.2403] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 01/26/2018] [Accepted: 03/29/2018] [Indexed: 11/11/2022]
Abstract
In ecological systems, extremes can happen in time, such as population crashes, or in space, such as rapid range contractions. However, current methods for joint inference about temporal and spatial dynamics (e.g., spatiotemporal modeling with Gaussian random fields) may perform poorly when underlying processes include extreme events. Here we introduce a model that allows for extremes to occur simultaneously in time and space. Our model is a Bayesian predictive-process GLMM (generalized linear mixed-effects model) that uses a multivariate-t distribution to describe spatial random effects. The approach is easily implemented with our flexible R package glmmfields. First, using simulated data, we demonstrate the ability to recapture spatiotemporal extremes, and explore the consequences of fitting models that ignore such extremes. Second, we predict tree mortality from mountain pine beetle (Dendroctonus ponderosae) outbreaks in the U.S. Pacific Northwest over the last 16 yr. We show that our approach provides more accurate and precise predictions compared to traditional spatiotemporal models when extremes are present. Our R package makes these models accessible to a wide range of ecologists and scientists in other disciplines interested in fitting spatiotemporal GLMMs, with and without extremes.
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Affiliation(s)
- Sean C Anderson
- School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, Washington, 98195, USA.,Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, British Columbia, V6T 6N7, Canada
| | - Eric J Ward
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanographic and Atmospheric Administration, 2725 Montlake Blvd E, Seattle, Washington, 98112, USA
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Dekoninck L, Botteldooren D, Int Panis L. Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models. Int J Environ Res Public Health 2017; 14:E586. [PMID: 28561799 DOI: 10.3390/ijerph14060586] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Revised: 05/21/2017] [Accepted: 05/26/2017] [Indexed: 11/21/2022]
Abstract
Personal exposure is sensitive to the personal features and behavior of the individual, and including interpersonal variability will improve the health and quality of life evaluations. Participatory sensing assesses the spatial and temporal variability of environmental indicators and is used to quantify this interpersonal variability. Transferring the participatory sensing information to a specific study population is a basic requirement for epidemiological studies in the near future. We propose a methodology to reduce the void between participatory sensing and health research. Instantaneous microscopic land-use regression modeling (µLUR) is an innovative approach. Data science techniques extract the activity-specific and route-sensitive spatiotemporal variability from the data. A data workflow to prepare and apply µLUR models to any mobile population is presented. The µLUR technique and data workflow are illustrated with models for exposure to traffic related Black Carbon. The example µLURs are available for three micro-environments; bicycle, in-vehicle, and indoor. Instantaneous noise assessments supply instantaneous traffic information to the µLURs. The activity specific models are combined into an instantaneous personal exposure model for Black Carbon. An independent external validation reached a correlation of 0.65. The µLURs can be applied to simulated behavioral patterns of individuals in epidemiological cohorts for advanced health and policy research.
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Abstract
Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.
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Affiliation(s)
- Michael A Henson
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, U.S.A.
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Evers L, Molinari DA, Bowman AW, Jones WR, Spence MJ. Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring. Environmetrics 2015; 26:431-441. [PMID: 26900339 PMCID: PMC4744788 DOI: 10.1002/env.2347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 03/30/2015] [Accepted: 05/09/2015] [Indexed: 06/05/2023]
Abstract
Fitting statistical models to spatiotemporal data requires finding the right balance between imposing smoothness and following the data. In the context of P-splines, we propose a Bayesian framework for choosing the smoothing parameter, which allows the construction of fully automatic data-driven methods for fitting flexible models to spatiotemporal data. An implementation, which is highly computationally efficient and exploits the sparsity of the design and penalty matrices, is proposed. The findings are illustrated using a simulation study and two examples, all concerned with the modelling of contaminants in groundwater. This suggests that the proposed strategy is more stable that competing methods based on the use of criteria such as generalised cross-validation and Akaike's Information Criterion. © 2015 The Authors. Environmetrics Published by John Wiley Sons Ltd.
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Affiliation(s)
- L. Evers
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowU.K.
| | - D. A. Molinari
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowU.K.
| | - A. W. Bowman
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowU.K.
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