Which team will win the 2014 FIFA World Cup? A Bayesian approach for dummies

This paper presents several "ex ante" simulation exercises of the 2014 FIFA World Cup. Specifically, we estimate the probabilities of each national team advancing to different stages, using a basic Bayesian approach based on conjugate families. In particular, we use the Categorical-Dirichlet model in the first round and the Bernoulli-Beta model in the following stages. The novelty of our framework is given by the use of betting odds to elicit the hyperparameters of prior distributions. Additionally, we obtain the posterior distributions with the Highest Density Intervals of the probability to being champion for each team. We find that Brazil (19.95%), Germany (14.68%), Argentina (12.05%), and Spain (6.2%) have the highest probabilities of being champion. Finally, we identify some betting opportunities with our simulation exercises. In particular, Bosnia & Herzegovina is a promising, whereas Australia shows the lowest betting opportunities return

The impact of subsidized health insurance on the poor in Colombia: evaluating the case of Medellín

This paper uses count and binary data models with an endogenous dummy variable to evaluate the effect of the subsidized health care program in Medellin (Colombia). The subsidized program, which primarily covers poor people, is found to have a significant impact on the use of preventive medical care and hospitalization that might have a negative impact on the financial statements of the program. Specifically, econometric estimations of health care utilization indicate that there is both selection and moral hazard. These facts imply that the program can improve its coverage if mechanisms are created to lower the individual moral hazard effect.Este trabalho utiliza modelos de dados binários e de contagem com uma variável indicadora endógena para avaliar o efeito do programa de saúde em Medellín (Colômbia). Encontrou-se um impacto significativo do programa subsidiado, que abrange principalmente a população pobre, sobre o uso de assistência médica preventiva e de internação, o que pode ter um impacto negativo sobre as situações financeiras do programa. Especificamente, estimativas econométricas da utilização de cuidados de saúde indicam que há seleção e risco moral. Esses fatos sugerem que o programa pode melhorar sua cobertura se forem criados mecanismos para diminuir o efeito de risco moral individual

Energy Consumption Analysis Of Machining Centers Using Bayesian Analysis And Genetic Optimization

Responding to the current urgent need for low carbon emissions and high efficiency in manufacturing processes, the relationships between three different machining factors (depth of cut, feed rate, and spindle rate) on power consumption and surface finish (roughness) were analysed by applying a Bayesian seemingly unrelated regressions (SUR) model. For the analysis, an optimization criterion was established and minimized by using an optimization algorithm that combines evolutionary algorithm methods with a derivative-based (quasi-Newton) method to find the optimal conditions for energy consumption that obtains a good surface finish quality. A Bayesian ANOVA was also performed to identify the most important factors in terms of variance explanation of the observed outcomes. The data were obtained from a factorial experimental design performed in two computerized numerical control (CNC) vertical machining centers (Haas UMC-750 and Leadwell V-40iT). Some results from this study show that the feed rate is the most influential factor in power consumption, and the depth of cut is the factor with the stronger influence on roughness values. An optimal operational point is found for the three factors with a predictive error of less than 0.01% and 0.03% for the Leadwell V-40iT machine and the Haas UMC-750 machine, respectively

Hospital length of stay throughout bed pathways and factors affecting this time : A non-concurrent cohort study of Colombia COVID-19 patients and an unCoVer network project

Publisher Copyright: Copyright: © 2023 Ruiz Galvis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Predictions of hospital beds occupancy depends on hospital admission rates and the length of stay (LoS) according to bed type (general ward -GW- and intensive care unit -ICU- beds). The objective of this study was to describe the LoS of COVID-19 hospital patients in Colombia during 2020-2021. Accelerated failure time models were used to estimate the LoS distribution according to each bed type and throughout each bed pathway. Acceleration factors and 95% confidence intervals were calculated to measure the effect on LoS of the outcome, sex, age, admission period during the epidemic (i.e., epidemic waves, peaks or valleys, and before/after vaccination period), and patients geographic origin. Most of the admitted COVID-19 patients occupied just a GW bed. Recovered patients spent more time in the GW and ICU beds than deceased patients. Men had longer LoS than women. In general, the LoS increased with age. Finally, the LoS varied along epidemic waves. It was lower in epidemic valleys than peaks, and decreased after vaccinations began in Colombia. Our study highlights the necessity of analyzing local data on hospital admission rates and LoS to design strategies to prioritize hospital beds resources during the current and future pandemics.Peer reviewe

Diseños óptimos bayesianos para estimación de parámetros en farmacocinética / Bayesian optimal designs for parameters estimation in pharmacokinetics

En farmacología, particularmente en el campo farmacocinético, el interés fundamental es estudiar la concentración de un medicamento en plasma. En esta área usualmente se tiene modelos de tipo no lineal dadas las características particulares de administración del medicamento. Desde el enfoque bayesiano, el objetivo de construir diseños óptimos sujetos a una función de utilidad es maximizar la utilidad esperada asociada a algún funcional de interés para el investigador. En este trabajo se realizó una caracterización de los diseños óptimos obtenidos a través de dos funciones de utilidad asociadas a un criterio de optimalidad bayesiano (D-optimalidad bayesiano), para estimar en forma óptima los parámetros de dos modelos no lineales: 1) Modelos monocompartimental con tasa de absorción y eliminación, 2) Modelo bicompartimental con tasa de eliminación y absorción reversible para el segundo compartimiento, ambos modelos bajo el supuesto de normalidad en los errores. Dicha caracterización se realizó vía simulación y para maximizar la utilidad se recurrió a la evolución diferencial. / Abstract: In pharmacolgy, especially on the pharmacokinetics field, the main interest is the study of the plasma medicine concentration. This area uses non-linear models given by the particular administration of a medicine. The purpose of Bayesian approach is to construct optimal designs restricted to a utility function, to maximize the expected utility associated to some functional in which the investigator is interested. In this work we made a characterization of the optimal designs obtained from two utility functions associated to an optimal Bayesian criteria (Bayes D-optimality) to obtain optimal parameter estimates for two non-linear models: 1) one-compartment model with absorption and elimination rate, 2) two-compartment model with absorption and elimination rates reversible for the second compartment, both models under normality assumption for errors. The cited characterization was done via simulation and using Differential Evolution to maximize the utility.Maestrí

Modelagem totalmente Bayesiana para análise de grupos com dados de fMRI

Functional magnetic resonance imaging or functional MRI (fMRI) is a non-invasive way to assess brain activity by detecting changes associated with blood flow. In this thesis, we propose a fully Bayesian procedure to analyze fMRI data for individual and group stages. For the individual stage, we use a Matrix-Variate Dynamic Linear Model (MDLM), where the temporal dependence is modeled through the state parameters and the spatial dependence is modeled only locally, taking the nearest neighbors of each voxel location. For the group stage, we take advantage of the posterior distribution of the state parameters obtained at the individual stage and create a new posterior distribution that represents the updated beliefs for the group analysis. Since the posterior distribution for the state parameters is indexed by the time t, we propose three options for algorithms that allow on-line estimated curves for the state parameters to be drawn and posterior probabilities to be computed in order to assess brain activation for both individual and group stages. We illustrate our method through two practical examples and offer an assessment using real resting-state data to compute empirical false-positive brain activation rates. Finally, we make available the R package BayesDLMfMRI to perform task-based fMRI data analysis for individual and group stages using the method proposed in this thesis.Imagens de ressonância magnética funcional ou MRI funcional (fMRI) é uma forma não invasiva de avaliar a atividade cerebral através da detecção de mudanças relacionadas ao fluxo sanguíneo. Nesta tese propomos uma modelagem Bayesiana completa para analisar dados de fMRI para o caso individual e em grupos. Para a etapa individual, usamos a Modelo Linear Dinâmico Matriz-Variado (MLDMV), onde a dependência temporal é modelada através dos parâmetros de estado e a dependência espacial é modelada apenas localmente, considerando os vizinhos mais próximos de cada voxel. Para a fase de grupos, a partir da distribuição posterior dos parâmetros de estado obtidos no estágio individual criamos uma nova distribuição posterior que representam as crenças atualizadas para a análise de grupo. Como a distribuição posterior dos parâmetros de estado é indexada pelo tempo t, propomos três opções para algoritmos que permitem amostrar curvas estimadas dos parâmetros de estado e calcular probabilidades posteriores para avaliar a ativação cerebral para os estágios individual e de grupo. Ilustramos nosso método por meio de dois exemplos práticos e oferecemos uma avaliação usando dados reais de resting-state para calcular taxas empíricas de ativações falso-positivas. Finalmente, disponibilizamos um pacote de R (BayesDLMfMRI) para executar análises de dados de fMRI baseada em tarefas para etapas individuais e de grupo usandoo método proposto nesta tese

The Impact Of Subsidized Health Insurance On The Poor In Colombia: Evaluating The Case Of Medellín

This paper uses count and binary data models with an endogenous dummy variable to evaluate the effect of the subsidized health care program in Medellin (Colombia). The subsidized program, which primarily covers poor people, is found to have a significant impact on the use of preventive medical care and hospitalization that might have a negative impact on the financial statements of the program. Specifically, econometric estimations of health care utilization indicate that there is both selectionand moral hazard. These facts imply that the program can improve its coverage if mechanisms are created to lower the individual moral hazardeffect

Data_Sheet_1_Socioeconomic disparities associated with mortality in patients hospitalized for COVID-19 in Colombia.pdf

Socioeconomic disparities play an important role in the development of severe clinical outcomes including deaths from COVID-19. However, the current scientific evidence in regard the association between measures of poverty and COVID-19 mortality in hospitalized patients is scant. The objective of this study was to investigate whether there is an association between the Colombian Multidimensional Poverty Index (CMPI) and mortality from COVID-19 in hospitalized patients in Colombia from May 1, 2020 to August 15, 2021. This was an ecological study using individual data on hospitalized patients from the National Institute of Health of Colombia (INS), and municipal level data from the High-Cost Account and the National Administrative Department of Statistics. The main outcome variable was mortality due to COVID-19. The main exposure variable was the CMPI that ranges from 0 to 100% and was categorized into five levels: (i) level I (0%−20%), (ii) level II (20%−40%), (iii) level III (40%−60%), (iv) level IV (60%−80%); and (v) level V (80%−100%). The higher the level, the higher the level of multidimensional poverty. A Bayesian multilevel logistic regression model was applied to estimate Odds Ratio (OR) and their corresponding 95% credible intervals (CI). In addition, a subgroup analysis was performed according to the epidemiological COVID-19 waves using the same model. The odds for dying from COVID-19 was 1.46 (95% CI 1.4–1.53) for level II, 1.41 (95% CI 1.33–1.49) for level III and 1.70 (95% CI 1.54–1.89) for level IV hospitalized COVID-19 patients compared with the least poor patients (CMPI level I). In addition, age and male sex also increased mortality in COVID-19 hospitalized patients. Patients between 26 and 50 years-of-age had 4.17-fold increased odds (95% CI 4.07–4.3) of death compared with younger than 26-years-old patients. The corresponding for 51–75 years-old patients and those above the age of 75 years were 9.17 (95% CI 8.93–9.41) and 17.1 (95% CI 16.63–17.56), respectively. Finally, the odds of death from COVID-19 in hospitalized patients gradually decreased as the pandemic evolved. In conclusion, socioeconomic disparities were a major risk factor for mortality in patients hospitalized for COVID-19 in Colombia.</p