Spatial Statistical Models: an overview under the Bayesian Approach

Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data likelihood. Nevertheless, this modeling class is not well explored as the classification and regression machine learning models given their simplicity and often weak (data) independence supposition. In this manner, this systematic review aimed to unravel the main models presented in the literature in the past 20 years, identify gaps, and research opportunities. Elements such as random fields, spatial domains, prior specification, covariance function, and numerical approximations were discussed. This work explored the two subclasses of spatial smoothing global and local.Comment: 33 pages, 6 figure

Modelos de Processo Espacial Bayesiano para Padrões de Ativação em Mapeamento de Estimulação Magnética Transcraniana

In recent years, Spatial statistical models have been gaining rapid attention for solving problems in biological systems due to the improvement in spatial data collection. It has proven extremely important in unveiling spatial patterns and predicting biological processes. This project developed novel parametric and nonparametric Bayesian spatial statistical models to analyze data generated by the muscular responses elicited by Transcranial magnetic stimulation (TMS) pulses induced on the motor cortex of a patient. The goal is to unveil new insights into patients response patterns important for achieving successful TMS therapy sessions. The first contribution of this project is a systematic review and meta-analysis of the existing Bayesian spatial models that could be considered for analyzing TMS datasets. The second contribution is the development of a user-friendly interface for performing Bayesian spatial modeling for analyzing TMS datasets based on state-of-the-art methods. The interface was documented in an R package, which is publicly available. The third contribution proposed novel spatial statistical models for integrating geostatistical datasets in the form of prior elicitation in a Bayesian analysis. The models were validated using simulation studies, and findings show that naively integrating geostatistical TMS datasets without ensuring the consistency of the data is detrimental to the desired inferences. The final contribution proposed a Bayesian nonparametric spatial model that leads to a non-stationary and non-Gaussian spatial process for the joint modeling of geostatistical TMS datasets. The method used a mixture of Dependent Dirichlet processes to share information across sub-spatial models. Two simulation studies were used to validate the model performance, and the result showed superior performance compared with independent and exchangeable models. The main finding of this work is that the primary motor cortex within the motor cortex region of the brain is responsible for the largest activation in the movement of the right first dorsal interosseous muscle. The finding also showed that the corticospinal excitability decreases with multiple TMS pulses on the motor cortex; however, it begins to regain its excitability strength after several stimulations. The findings from this project could guide TMS practitioners to improve patients treatment experiences.Nos últimos anos, os modelos estatísticos espaciais têm recebido rápida atenção para resolver problemas em sistemas biológicos devido ao aprimoramento na coleta de dados espaciais. Eles têm se mostrado extremamente importantes na revelação de padrões espaciais e na previsão de processos biológicos. Este projeto desenvolveu novos modelos estatísticos espaciais paramétricos e não paramétricos Bayesianos para analisar dados gerados pelas respostas musculares desencadeadas por pulsos de estimulação magnética transcraniana (TMS) induzidos no córtex motor de um paciente. O objetivo é descobrir novas perspectivas sobre os padrões de resposta dos pacientes, um fator importante para o sucesso das sessões de terapia com TMS. A primeira contribuição deste projeto é uma revisão sistemática e meta-análise dos modelos espaciais Bayesianos existentes que podem ser considerados para analisar conjuntos de dados de TMS. A segunda contribuição é o desenvolvimento de uma interface do usuário para realizar modelagem espacial Bayesianas para análise de conjuntos de dados de TMS com base em métodos de última geração. A interface foi documentada em um pacote R, que está disponível publicamente. A terceira contribuição propôs novos modelos estatísticos espaciais para integrar conjuntos de dados geoestatísticos na forma de elicitação de priori em uma análise Bayesiana. Os modelos foram validados usando estudos de simulação, e os resultados mostram que a integração ingênua de conjuntos de dados geoestatísticos de TMS sem garantir a consistência dos dados é prejudicial para as inferências desejadas. A contribuição final propôs um modelo espacial não paramétrico Bayesiano que leva a um processo espacial não estacionário e não gaussiano para a modelagem conjunta de conjuntos de dados geoestatísticos de TMS. O método utilizou uma mistura de processos de Dirichlet dependentes para compartilhar informações entre os submodelos espaciais. Dois estudos de simulação foram usados para validar o desempenho do modelo, e o resultado mostrou desempenho superior em comparação com modelos independentes e intercambiáveis. O principal resultado deste trabalho é que o córtex motor primário, dentro da região do córtex motor do cérebro, é responsável pela maior ativação no movimento do músculo interósseo dorsal do primeiro dedo direito. Os resultados também mostraram que a excitabilidade corticospinal diminui com múltiplos pulsos de TMS no córtex motor; no entanto, começa a recuperar sua força de excitabilidade após várias estimulações. Tais resultados podem orientar os profissionais de TMS a melhorar a experiência de tratamento dos pacientes

Risk factors of concurrent malnutrition among children in Ethiopia: a bivariate spatial modeling approach

Malnutrition among children remains a challenge to public health in Africa, and it is responsible for the increased infant mortality rate in Ethiopia. This study aims to identify the joint determinant factors of malnutrition among under-age five children in Ethiopia and quantify the regional prevalence across the country. A Bayesian hierarchical linear mixed model, with a bivariate conditional autoregressive model, was adopted to account for the joint spatial prevalence pattern of malnutrition among under-age five children in Ethiopia. The data were provided by the Demography and Health Surveys program. The study revealed that gender, wealth index, mothers' education, toilet system, fever, birth order, birth interval, contraceptive use, and diarrhea, are significant risk factors of child malnutrition. The three malnutrition indicators are most prevalent in the Afar region, while stunting and underweight are most prevalent in Amhara, Beneshangul Gumuz, and Tigray, and the wasting and underweight are most prevalent in the Gambela and Somali. Findings revealed that a stunted child is more likely to be underweight than being wasted, and a wasted child is more likely to be underweight than being stunted. The findings also revealed significant changes in the prevalence of malnutrition for different mothers and children age groups. For cost-effective malnutrition intervention, programs for parents, such as awareness creation about the modern types of contraceptives, appropriate birth spacing, the benefits of antenatal and postnatal care, for example, are a propitious method to mitigate malnutrition in high prevalent regions than direct intervention to the children. Abbreviations: BICAR, Bivariate Conditional Autoregressive; DHS, Demographic and Health Survey; EDHS, Ethiopia Demographic and Health Survey; EA, Enumeration Area; GMRF, Gaussian Markov Random Field; HAZ, Height for Age Z-sscore; INLA, Integrated Nexted Laplace Approximation; MCAR, Multivariate Conditional Autoregressive; MCMC, Markov Chain Monte Carlo; SNNPR, Southern Nations, Nationalities, and People's Region; WAZ, Weight for Age Z-score; WHZ, Weight for Height Z-score; WHO, World Health Organizatio

Under age five children survival times in Nigeria: a Bayesian spatial modeling approach

Abstract Background Nigeria is among the top five countries in the world with the highest under-five mortality rates. In addition to the general leading causes of under-five mortality, studies have shown that disparity in sociocultural values and practices across ethnic groups in Nigeria influence child survival, thus there is a need for scientific validation. This study quantified the survival probabilities and the impact of socioeconomic and demographic factors, proximate and biological determinants, and environmental factors on the risk of under-five mortality in Nigeria. Methods The Kaplan-Meier survival curve, Nelson Aalen hazard curve, and components survival probabilities were estimated. The Exponential, Gamma, Log-normal, Weibull, and Cox hazard models in a Bayesian mixed effect hierarchical hazard modeling framework with spatial components were considered, and the Deviance and Watanabe Akaike information criteria were used to select the best model for inference. A $$5\%$$ 5 % level of significance was assumed throughout this work. The 2018 Nigeria Demographic and Health Survey dataset was used, and the outcome variable was the time between birth and death or birth and the date of interview for children who were alive on the day of the interview. Results Findings show that the probability of a child dying within the first two months is 0.04, and the probability of a boy child dying before attaining age five is 0.106, while a girl child is 0.094 probability. Gender, maternal education, household wealth status, source of water and toilet facility, residence, mass media, frequency of antenatal and postnatal visits, marital status, place of delivery, multiple births, who decide healthcare use, use of bednet are significant risk factors of child mortality in Nigeria. The mortality risk is high among the maternal age group below 24 and above 44years, and birth weight below 2.5Kg and above 4.5Kg. The under-five mortality risk is severe in Kebbi, Kaduna, Jigawa, Adamawa, Gombe, Kano, Kogi, Nasarawa, Plateau, and Sokoto states in Nigeria. Conclusion This study accentuates the need for special attention for the first two months after childbirth as it is the age group with the highest expected mortality. A practicable way to minimize death in the early life of children is to improve maternal healthcare service, promote maternal education, encourage delivery in healthcare facilities, positive parental attitude to support multiple births, poverty alleviation programs for the less privileged, and a prioritized intervention to Northern Nigeria

A situational assessment of treatments received for childhood diarrhea in the Federal Republic of Nigeria.

We assess progress towards improved case management of childhood diarrhea in Nigeria over a period of targeted health systems reform from 2013 to 2018. Individual and community data from three Demographic and Health Survey rounds are leveraged in a geospatial model designed for stratified estimation by venue of treatment seeking and State. Our analysis reveals a highly regionalised health system undergoing rapid change. Nationally, there have been substantial increases in the proportion of children under 5 years old with diarrhea receiving the recommended oral rehydration therapy after seeking treatment at either a health clinic (0.57 [0.44-0.69; 95% CI] in 2008; 0.70 [0.54-0.83] in 2018) or chemist/pharmacy (0.28 [0.17-0.42] in 2008; 0.48 [0.31-0.64] in 2018). Yet State-level variations in venue attendance and performance by venue have conspired to hold the overall proportion receiving this potentially life-saving therapy (0.45 [0.35-0.55] in 2018) to well-below ideal coverage levels. High performing states that have demonstrated significant improvements include Kano, Jigawa and Borno, while under-performing states that have suffered declines in coverage include Kaduna and Taraba. The use of antibiotics is not recommended for mild cases of childhood diarrhea yet remains concerningly high nationally (0.27 [0.19-0.36] in 2018) with negligible variation between venues. Antibiotic use rates are particularly high in Enugu, Kaduna, Taraba, Kano, Niger and Kebbi, yet welcome reductions were identified in Jigawa, Adamawa and Osun. These results support the conclusions of previous studies and build the strength of evidence that urgent action is needed throughout the multi-tiered health system to improve the quality and equity of care for common childhood illnesses in Nigeria