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Utilizing bayesian modeling and MCMC for Accurate Characterization of Naturally Occurring Radionuclides Reference Background Levels in Mining Areas

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World Journal of Nuclear Science and Technology 2024年第4期14卷 179-187页

作者： Djicknack Dione Papa Macoumba Faye Nogaye Ndiaye Moussa Hamady Sy Oumar Ndiaye Alassane Traoré Ababacar Sadikhe Ndao Institute Technologies of Nuclear Applied Cheikh Anta Diop University of Dakar Dakar Senegal Department of Physics Faculty of Sciences and Techniques Cheikh Anta Diop University of Dakar Dakar Senegal

Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.

关键词： Radionuclides bayesian modeling MCMC HDI 40K 232Th 238U

bayesian estimation of the time-varying reproduction number for pulmonary tuberculosis in Iran:A registry-based study from 2018 to 2022 using new smear-positive cases

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Infectious Disease Modelling 2024年第3期9卷 963-974页

作者： Maryam Rastegar Eisa Nazar Mahshid Nasehi Saeed Sharafi Vahid Fakoor Mohammad Taghi Shakeri Department of Biostatistics School of HealthMashhad University of Medical SciencesMashhadIran Orthopedic Research Center Mazandaran University of Medical SciencesSariIran Centre for Communicable Diseases Control Ministry of Health and Medical EducationTehranIran Department of Statistics Faculty of Mathematical SciencesFerdowsi University of MashhadMashhadIran Social Determinants of Health Research Center Mashhad University of Medical SciencesMashhadIran Department of Epidemiology School of Public HealthIran University of Medical SciencesTehran

Introduction Tuberculosis(TB)is one of the most prevalent infectious diseases in the world,causing major public health problems in developing *** rate of TB incidence in Iran was estimated to be 13 per 100,000 in *** study aimed to estimate the reproduction number and serial interval for pulmonary tuberculosis in *** and methods The present national historical cohort study was conducted from March 2018 to March 2022 based on data from the National Tuberculosis and Leprosy Registration Center of Iran's Ministry of Health and Medical Education(MOHME).The study included 30,762 tuberculosis cases and 16,165 new smear-positive pulmonary tuberculosis patients in *** estimated the reproduction number of pulmonary tuberculosis in a bayesian framework,which can incorporate uncertainty in estimating *** analyses were accomplished in R *** The mean age at diagnosis of patients was 52.3±21.2 years,and most patients were in the 35–63 age group(37.1%).Among the data,9121(56.4%)cases were males,and 7044(43.6%)were *** patients,7459(46.1%)had a delayed diagnosis between 1 and 3 ***,3039(18.8%)cases were non-Iranians,and 2978(98%)were *** time-varying reproduction number for pulmonary tuberculosis disease was calculated at an average of 1.06±0.05(95%Crl 0.96–1.15).Conclusions In this study,the incidence and the time-varying reproduction number of pulmonary tuberculosis showed the same *** mean of the time-varying reproduction number indicated that each infected person is causing at least one new infection over time,and the chain of transmission is not being disrupted.

关键词： Reproduction number bayesian modeling Tuberculosis Iran

A STOCHASTIC ALGORITHM FOR FAULT INVERSE PROBLEMS IN ELASTIC HALF SPACE WITH PROOF OF CONVERGENCE

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Journal of Computational Mathematics 2022年第6期40卷 955-976页

作者： Darko Volkov Department of Mathematical Sciences WPIWorcesterMassachusettsUSA

A general stochastic algorithm for solving mixed linear and nonlinear problems was introduced in[11].We show in this paper how it can be used to solve the fault inverse problem,where a planar fault in elastic half-space and a slip on that fault have to be reconstructed from noisy surface displacement *** the parameter giving the plane containing the fault denoted by m and the regularization parameter for the linear part of the inverse problem denoted by C,both modeled as random variables,we derive a formula for the posterior marginal of *** C as a random variable allows to sweep through a wide range of possible values which was shown to be superior to selecting a fixed value[11].We prove that this posterior marginal of m is convergent as the number of measurement points and the dimension of the space for discretizing slips *** put,our proof only assumes that the regularized discrete error functional for processing measurements relates to an order 1 quadrature rule and that the union of the finite-dimensional spaces for discretizing slips is *** proof relies on trace class operator theory to show that an adequate sequence of determinants is uniformly *** also explain how our proof can be extended to a whole class of inverse problems,as long as some basic requirements are ***,we show numerical simulations that illustrate the numerical convergence of our algorithm.

关键词： Mixed Linear and nonlinear inverse problems bayesian modeling Regularization Approximation to solutions by quadrature Convergence of Random Variables Elasticity equations in unbounded domains

Identifying Malaria Hotspots Regions in Ghana Using bayesian Spatial and Spatiotemporal Models

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Infectious Diseases & Immunity 2024年第2期4卷 69-78页

作者： Abdul-Karim Iddrisu Dominic Otoo Gordon Hinneh Yakubu Dekongmene Kanyiri Kanimam Yaaba Samuel Cecilia Kubio Francis Balungnaa Dhari Veriegh Department of Mathematics and Statistics University of Energy and Natural ResourcesSunyani 233Ghana Department of Statistical Service Ghana Statistical ServiceBono RegionSunyani 233Ghana Department of Mathematics and ICT Gambaga College of EducationSunyani 233Ghana Human Resources Division University of Energy and Natural ResourcesSunyani 233Ghana Department of Biological Science University of Energy and Natural ResourcesSunyani 233Ghana.

Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical *** affects millions of people each year and imposes a substantial burden on the health care system and *** risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease *** study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented,and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria ***:The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical *** studied the spatial and spatiotemporal patterns of the relative risk of malaria using bayesian spatial and spatiotemporal *** malaria risk for each region was mapped to visually identify regions with malaria *** and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models,*** estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R ***:The spatial model indicated an increased risk of malaria in the North East,Bono East,Ahafo,Central,Upper West,Brong Ahafo,Ashanti,and Eastern *** spatiotemporal model results highlighted an elevated malaria risk in the North East,Upper West,Upper East,Savannah,Bono East,Central,Bono,and Ahafo *** spatial and spatiotemporal models identified the North East,Upper West,Bono East,Central,and Ahafo Regions as hotspots for malaria *** variations in risk were evident across regions(H=104.9,P<0.001).Although climatic and economic factors influenced malaria infection,statistical significance was not

关键词： Malaria Disease hotspot bayesian modeling Conditional auto-regressive Integrated Nested Laplace Approximation Spatial and spatiotemporal models