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Guan Y, Page GL, Reich BJ, Ventrucci M, Yang S. Spectral adjustment for spatial confounding. Biometrika 2023; 110:699-719. [PMID: 38500847 PMCID: PMC10947425 DOI: 10.1093/biomet/asac069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024] Open
Abstract
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the exposure to the mean of the response variable. Within this general framework, we propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matérn coherence function to more robust semiparametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets.
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Affiliation(s)
- Yawen Guan
- Department of Statistics, University of Nebraska, 343C Hardin Hall, Lincoln, Nebraska 68583, U.S.A
| | - Garritt L Page
- Department of Statistics, Brigham Young University, 238 TMCB, Provo, Utah 84602, U.S.A
| | - Brian J Reich
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina 27695, U.S.A
| | - Massimo Ventrucci
- Department of Statistical Sciences, University of Bologna, Via Zamboni 33, Bologna 40126, Italy
| | - Shu Yang
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina 27695, U.S.A
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2
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Copula modelling with penalized complexity priors: the bivariate case. TEST-SPAIN 2023. [DOI: 10.1007/s11749-022-00843-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
AbstractWe explore the use of penalized complexity (PC) priors for assessing the dependence structure in a multivariate distribution F, with a particular emphasis on the bivariate case. We use the copula representation of F and derive the PC prior for the parameter governing the copula. We show that any $$\alpha $$
α
-divergence between a multivariate distribution and its counterpart with independent components does not depend on the marginal distribution of the components. This implies that the PC prior for the parameters of the copula can be elicited independently of the specific form of the marginal distributions. This represents a useful simplification in the model building step and may offer a new perspective in the field of objective Bayesian methodology. We also consider strategies for minimizing the role of subjective inputs in the prior elicitation step. Finally, we explore the use of PC priors in Bayesian hypothesis testing. Our prior is compared with competing default priors both for estimation purposes and testing.
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Yu D, Fang C. How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11416. [PMID: 36141688 PMCID: PMC9517077 DOI: 10.3390/ijerph191811416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Urban crimes are a severe threat to livable and sustainable urban environments. Many studies have investigated the patterns, causes, and strategies for curbing the occurrence of urban crimes. It is found that neighborhood socioeconomic status, physical environment, and ethnic composition all might play a role in the occurrence of urban crimes. Inspired by the recent interest in exploring urban crime patterns with spatial data analysis techniques and the development of Bayesian hierarchical analytical approaches, we attempt to explore the inherently intricate relationships between urban assaultive violent crimes and the neighborhood socioeconomic status, physical environment, and ethnic composition in Paterson, NJ, using census data of the American Community Survey, alcohol and tobacco sales outlet data, and abandoned property listing data from 2013. Analyses are set at the census block group level. Urban crime data are obtained from the Paterson Police Department. Instead of examining relationships at a global level with both non-spatial and spatial analyses, we examine in depth the potential locally varying relationships at the local level through a Bayesian hierarchical spatially varying coefficient model. At both the global and local analysis levels, it is found that median household income is decisively negatively related to urban crime occurrence. Percentage of African Americans and Hispanics, number of tobacco sales outlets, and number of abandoned properties are all positively related with urban crimes. At the local level of analysis, however, the different factors have varying influence on crime occurrence throughout the city of Paterson, with median household income having the broadest influence across the city. The practice of applying a Bayesian hierarchical spatial analysis framework to understand urban crime occurrence and urban neighborhood characteristics enables urban planners, stakeholders, and public safety officials to engage in more active and targeted crime-reduction strategies.
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Affiliation(s)
- Danlin Yu
- Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, USA
| | - Chuanglin Fang
- Center for Urban and Regional Planning Design and Research, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100045, China
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Jaya IGNM, Folmer H. Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease. JOURNAL OF GEOGRAPHICAL SYSTEMS 2022; 24:527-581. [PMID: 35221792 PMCID: PMC8857957 DOI: 10.1007/s10109-021-00368-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 10/08/2021] [Indexed: 05/16/2023]
Abstract
Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the "big n" problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January-July and a decrease in the period August-December. Supplementary Information The online version contains supplementary material available at 10.1007/s10109-021-00368-0.
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Affiliation(s)
- I. Gede Nyoman Mindra Jaya
- Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands
- Statistics Department, Padjadjaran University, Bandung, Indonesia
| | - Henk Folmer
- Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands
- Statistics Department, Padjadjaran University, Bandung, Indonesia
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Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe. Sci Rep 2022; 12:726. [PMID: 35082316 PMCID: PMC8791935 DOI: 10.1038/s41598-021-04277-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 11/08/2021] [Indexed: 01/08/2023] Open
Abstract
Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.
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Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. COMPLEX INTELL SYST 2021; 7:2655-2678. [PMID: 34777970 PMCID: PMC8256231 DOI: 10.1007/s40747-021-00424-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/05/2021] [Indexed: 12/24/2022]
Abstract
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
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Affiliation(s)
- Onur Dogan
- Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey.,Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, 35665 Izmir, Turkey
| | - Sanju Tiwari
- Department of Computer Science, Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico
| | - M A Jabbar
- Vardhaman College of Engineering, Kacharam, India
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Sosa J, Buitrago L. Time-varying coefficient model estimation through radial basis functions. J Appl Stat 2021; 49:2510-2534. [PMID: 35757039 PMCID: PMC9225525 DOI: 10.1080/02664763.2021.1910938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 03/25/2021] [Indexed: 10/21/2022]
Abstract
In this paper, we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.
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Affiliation(s)
- Juan Sosa
- Departamento de Estadística, Universidad Nacional de Colombia, Carrera 45 # 26-85, Bogotá, Colombia
| | - Lina Buitrago
- Departamento de Estadística, Universidad Nacional de Colombia, Carrera 45 # 26-85, Bogotá, Colombia
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Heuclin B, Mortier F, Trottier C, Denis M. Bayesian varying coefficient model with selection: An application to functional mapping. J R Stat Soc Ser C Appl Stat 2020. [DOI: 10.1111/rssc.12447] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Benjamin Heuclin
- IMAG, Univ Montpellier, CNRS Montpellier France
- CIRAD, UMR AGAP Montpellier France
| | - Frédéric Mortier
- Forêts et Sociétés Cirad Montpellier France
- Forêts et Sociétés Univ Montpellier, Cirad Montpellier France
| | - Catherine Trottier
- IMAG, Univ Montpellier, CNRS Montpellier France
- Univ Paul‐Valéry Montpellier 3 Montpellier France
| | - Marie Denis
- CIRAD, UMR AGAP Montpellier France
- AGAP Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier France
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