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Papers by Ana Corberán-vallet

Journal of infection and public health, Jul 1, 2024

analysis of infectious disease surveillance data using syndromic information

Biometrical Journal, 2015

Spatial and Spatio-temporal Epidemiology, 2019

Visceral leishmaniasis (VL) is a parasitic disease that is endemic in more than 80 countries, and... more Visceral leishmaniasis (VL) is a parasitic disease that is endemic in more than 80 countries, and leads to high fatality rates when left untreated. We investigate the relationship of VL cases in dogs and human cases, specifically for evidence of VL in dogs leading to excess cases in humans. We use surveillance data for dogs and humans for the years 2007-2011 to conduct both spatial and spatio-temporal analyses. Several models are evaluated incorporating varying levels of dependency between dog and human data. Models including dog data show marginal improvement over models without; however, for a subset of spatial units with ample data, models provide concordant risk classification for dogs and humans at high rates (~70%). Limited reported dog case surveillance data may contribute to the results suggesting little explanatory value in the dog data, as excess human risk was only explained by dog risk in 5% of regions in the spatial analysis.

Expert Systems with Applications

Indice de figuras 1.1. Gráfico temporal de las series mensuales de ocupación hote lera en Castell... more Indice de figuras 1.1. Gráfico temporal de las series mensuales de ocupación hote lera en Castellón, Valencia y Alicante desde Enero de 2001 hasta Diciembre de 2006. Unidades: miles de viajeros............ 1.2. Histogramas de los parámetros de suavizado simulados de su distribución a posteriori en los análisis univariantes de las series temporales de ocupación h o te le r a ..

Xxxi Congreso Nacional De Estadistica E Investigacion Operativa V Jornadas De Estadistica Publica Murcia 10 13 De Febrero De 2009 Libro De Actas 2009 Isbn 978 84 691 8159 1, 2009

En este trabajo introducimos un procedimiento Bayesiano, basado en el modelo de Holt-Winters mult... more En este trabajo introducimos un procedimiento Bayesiano, basado en el modelo de Holt-Winters multivariante, que nos permite obtener predicciones de series temporales correlacionadas. Nuestra formulacion del modelo multivariante asume que cada una de las series temporales se ajusta al modelo de Holt-Winters univariante y que existe una correlacion contemporanea entre los errores de los modelos univariantes. La distribucion a posteriori de los parametros del modelo puede ser estimada a partir de metodos MCMC. La distribucion predictiva la estimamos mediante integracion Monte Carlo. Suponiendo que los parametros de suavizado de todos los modelos univariantes son iguales, el modelo de Holt-Winters multivariante puede formularse como un modelo de regresion multivariante tradicional, lo que simpli ca considerablemente su analisis. Utilizamos tecnicas de comparacion de modelos para contrastar el modelo homogeneo (parametros de suavizado iguales) frente al modelo general.

Handbook of Discrete-Valued Time Series, 2016

International Journal of Environmental Research and Public Health, 2021

Bronchiolitis has a high morbidity in children under 2 years old. Respiratory syncytial virus (RS... more Bronchiolitis has a high morbidity in children under 2 years old. Respiratory syncytial virus (RSV) is the most common pathogen causing the disease. At present, there is only a costly humanized monoclonal RSV-specific antibody to prevent RSV. However, different immunization strategies are being developed. Hence, evaluation and comparison of their impact is important for policymakers. The analysis of the disease with a Bayesian stochastic compartmental model provided an improved and more natural description of its dynamics. However, the consideration of different age groups is still needed, since disease transmission greatly varies with age. In this work, we propose a multivariate age-structured stochastic model to understand bronchiolitis dynamics in children younger than 2 years of age considering high-quality data from the Valencia health system integrated database. Our modeling approach combines ideas from compartmental models and Bayesian hierarchical Poisson models in a novel w...

Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region... more Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region of Valencia (Spain) and analyse the effect of vaccination strategies from a health-economic point of view. Compartmental mathematical models based on differential equations are commonly used in epidemiology to both understand the underlying mechanisms that influence disease transmission and analyse the impact of vaccination programs. However, a recently proposed Bayesian stochastic susceptible-infected-recovered-susceptible model in discrete-time provided an improved and more natural description of disease dynamics. In this work, we propose an extension of that stochastic model that allows us to simulate and assess the effect of a vaccination strategy that consists on vaccinating a proportion of newborns.

PLOS ONE, 2020

Many statistical models have been proposed to analyse small area disease data with the aim of des... more Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior distribution with an efficient estimate of the marginal likelihood of the data given this parameter. An extension of the model incorporating covariates is also shown. These covariates may incorporate additional information on the problem or they may account for spatial correlation in the data. We illustrate the performance of the proposed model through both a simulation study and a case study of reported cases of varicella in the city of Valencia, Spain.

Intelligent Systems Reference Library, 2010

This chapter presents a forecasting support system based on the exponential smoothing scheme to f... more This chapter presents a forecasting support system based on the exponential smoothing scheme to forecast time-series data. Exponential smoothing methods are simple to apply, which facilitates computation and considerably reduces data storage requirements. Consequently, they are widely used as forecasting techniques in inventory systems and business planning. After selecting the most adequate model to replicate patterns of the time series under study, the system provides accurate forecasts which can play decisive roles in organizational planning, budgeting and performance monitoring.

TOP, 2011

In this paper we analyze the importance of initial conditions in exponential smoothing models on ... more In this paper we analyze the importance of initial conditions in exponential smoothing models on forecast errors and prediction intervals. We work with certain exponential smoothing models, namely Holt's additive linear and Gardner's damped trend. We study some probability properties of those models, showing the influence of the initial conditions on the forecast, which highlights the importance of obtaining accurate estimates of initial conditions. Using the linear heteroscedastic modeling approach, we show how to obtain the joint estimation of initial conditions and smoothing parameters through maximum likelihood via box-constrained nonlinear optimization. Point-wise forecasts of future values and prediction intervals are computed under normality assumptions on the stochastic component. We also propose an alternative formulation of prediction intervals in order to obtain an empirical coverage closer to their nominal values; that formulation adds an additional term to the standard formulas for the estimation of the error variance. We illustrate the proposed approach by using the yearly data time-series from the M3-Competition.

Statistical Methods in Medical Research, 2012

Surveillance systems are often focused on more than one disease within a predefined area. On thos... more Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in...

Journal of Statistical Planning and Inference, 2009

This paper deals with the prediction of time series with missing data using an alternative formul... more This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.

International Journal of Forecasting, 2011

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model t... more This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters' model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection criterion is introduced into the forecasting scheme for selecting the most adequate multivariate model for describing the behaviour of the time series under study. The forecasting performance of this procedure is tested using some real examples. c

Complexity, 2018

We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time ... more We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time to understand chickenpox transmission in the Valencian Community, Spain. During the last decades, different strategies have been introduced in the routine immunization program in order to reduce the impact of this disease, which remains a public health’s great concern. Under this scenario, a model capable of explaining closely the dynamics of chickenpox under the different vaccination strategies is of utter importance to assess their effectiveness. The proposed model takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmission of the disease. As shown in a comparative study, these assumptions are fundamental to describe properly the evolution of the disease. The Bayesian analysis of the model allows us to calculate the posterior distribution of the model parameters and the posterior predictive distribution of chickenpox ...

Mathematics

This paper deals with the weighted combination of forecasting methods using intelligent strategie... more This paper deals with the weighted combination of forecasting methods using intelligent strategies for achieving accurate forecasts. In an effort to improve forecasting accuracy, we develop an algorithm that optimizes both the methods used in the combination and the weights assigned to the individual forecasts, COmbEB. The performance of our procedure can be enhanced by analyzing separately seasonal and non-seasonal time series. We study the relationships between prediction errors in the validation set and those of ex-post forecasts for different planning horizons. This study reveals the importance of setting the size of the validation set in a proper way. The performance of the proposed strategy is compared with that of the best prediction strategy in the analysis of each of the 100,000 series included in the M4 Competition.

Journal of infection and public health, Jul 1, 2024

analysis of infectious disease surveillance data using syndromic information

Biometrical Journal, 2015

Spatial and Spatio-temporal Epidemiology, 2019

Visceral leishmaniasis (VL) is a parasitic disease that is endemic in more than 80 countries, and... more Visceral leishmaniasis (VL) is a parasitic disease that is endemic in more than 80 countries, and leads to high fatality rates when left untreated. We investigate the relationship of VL cases in dogs and human cases, specifically for evidence of VL in dogs leading to excess cases in humans. We use surveillance data for dogs and humans for the years 2007-2011 to conduct both spatial and spatio-temporal analyses. Several models are evaluated incorporating varying levels of dependency between dog and human data. Models including dog data show marginal improvement over models without; however, for a subset of spatial units with ample data, models provide concordant risk classification for dogs and humans at high rates (~70%). Limited reported dog case surveillance data may contribute to the results suggesting little explanatory value in the dog data, as excess human risk was only explained by dog risk in 5% of regions in the spatial analysis.

Expert Systems with Applications

Indice de figuras 1.1. Gráfico temporal de las series mensuales de ocupación hote lera en Castell... more Indice de figuras 1.1. Gráfico temporal de las series mensuales de ocupación hote lera en Castellón, Valencia y Alicante desde Enero de 2001 hasta Diciembre de 2006. Unidades: miles de viajeros............ 1.2. Histogramas de los parámetros de suavizado simulados de su distribución a posteriori en los análisis univariantes de las series temporales de ocupación h o te le r a ..

Xxxi Congreso Nacional De Estadistica E Investigacion Operativa V Jornadas De Estadistica Publica Murcia 10 13 De Febrero De 2009 Libro De Actas 2009 Isbn 978 84 691 8159 1, 2009

En este trabajo introducimos un procedimiento Bayesiano, basado en el modelo de Holt-Winters mult... more En este trabajo introducimos un procedimiento Bayesiano, basado en el modelo de Holt-Winters multivariante, que nos permite obtener predicciones de series temporales correlacionadas. Nuestra formulacion del modelo multivariante asume que cada una de las series temporales se ajusta al modelo de Holt-Winters univariante y que existe una correlacion contemporanea entre los errores de los modelos univariantes. La distribucion a posteriori de los parametros del modelo puede ser estimada a partir de metodos MCMC. La distribucion predictiva la estimamos mediante integracion Monte Carlo. Suponiendo que los parametros de suavizado de todos los modelos univariantes son iguales, el modelo de Holt-Winters multivariante puede formularse como un modelo de regresion multivariante tradicional, lo que simpli ca considerablemente su analisis. Utilizamos tecnicas de comparacion de modelos para contrastar el modelo homogeneo (parametros de suavizado iguales) frente al modelo general.

Handbook of Discrete-Valued Time Series, 2016

International Journal of Environmental Research and Public Health, 2021

Bronchiolitis has a high morbidity in children under 2 years old. Respiratory syncytial virus (RS... more Bronchiolitis has a high morbidity in children under 2 years old. Respiratory syncytial virus (RSV) is the most common pathogen causing the disease. At present, there is only a costly humanized monoclonal RSV-specific antibody to prevent RSV. However, different immunization strategies are being developed. Hence, evaluation and comparison of their impact is important for policymakers. The analysis of the disease with a Bayesian stochastic compartmental model provided an improved and more natural description of its dynamics. However, the consideration of different age groups is still needed, since disease transmission greatly varies with age. In this work, we propose a multivariate age-structured stochastic model to understand bronchiolitis dynamics in children younger than 2 years of age considering high-quality data from the Valencia health system integrated database. Our modeling approach combines ideas from compartmental models and Bayesian hierarchical Poisson models in a novel w...

Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region... more Our objective in this paper is to model the dynamics of respiratory syncytial virus in the region of Valencia (Spain) and analyse the effect of vaccination strategies from a health-economic point of view. Compartmental mathematical models based on differential equations are commonly used in epidemiology to both understand the underlying mechanisms that influence disease transmission and analyse the impact of vaccination programs. However, a recently proposed Bayesian stochastic susceptible-infected-recovered-susceptible model in discrete-time provided an improved and more natural description of disease dynamics. In this work, we propose an extension of that stochastic model that allows us to simulate and assess the effect of a vaccination strategy that consists on vaccinating a proportion of newborns.

PLOS ONE, 2020

Many statistical models have been proposed to analyse small area disease data with the aim of des... more Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior distribution with an efficient estimate of the marginal likelihood of the data given this parameter. An extension of the model incorporating covariates is also shown. These covariates may incorporate additional information on the problem or they may account for spatial correlation in the data. We illustrate the performance of the proposed model through both a simulation study and a case study of reported cases of varicella in the city of Valencia, Spain.

Intelligent Systems Reference Library, 2010

This chapter presents a forecasting support system based on the exponential smoothing scheme to f... more This chapter presents a forecasting support system based on the exponential smoothing scheme to forecast time-series data. Exponential smoothing methods are simple to apply, which facilitates computation and considerably reduces data storage requirements. Consequently, they are widely used as forecasting techniques in inventory systems and business planning. After selecting the most adequate model to replicate patterns of the time series under study, the system provides accurate forecasts which can play decisive roles in organizational planning, budgeting and performance monitoring.

TOP, 2011

In this paper we analyze the importance of initial conditions in exponential smoothing models on ... more In this paper we analyze the importance of initial conditions in exponential smoothing models on forecast errors and prediction intervals. We work with certain exponential smoothing models, namely Holt's additive linear and Gardner's damped trend. We study some probability properties of those models, showing the influence of the initial conditions on the forecast, which highlights the importance of obtaining accurate estimates of initial conditions. Using the linear heteroscedastic modeling approach, we show how to obtain the joint estimation of initial conditions and smoothing parameters through maximum likelihood via box-constrained nonlinear optimization. Point-wise forecasts of future values and prediction intervals are computed under normality assumptions on the stochastic component. We also propose an alternative formulation of prediction intervals in order to obtain an empirical coverage closer to their nominal values; that formulation adds an additional term to the standard formulas for the estimation of the error variance. We illustrate the proposed approach by using the yearly data time-series from the M3-Competition.

Statistical Methods in Medical Research, 2012

Surveillance systems are often focused on more than one disease within a predefined area. On thos... more Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in...

Journal of Statistical Planning and Inference, 2009

This paper deals with the prediction of time series with missing data using an alternative formul... more This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.

International Journal of Forecasting, 2011

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model t... more This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters' model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection criterion is introduced into the forecasting scheme for selecting the most adequate multivariate model for describing the behaviour of the time series under study. The forecasting performance of this procedure is tested using some real examples. c

Complexity, 2018

We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time ... more We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time to understand chickenpox transmission in the Valencian Community, Spain. During the last decades, different strategies have been introduced in the routine immunization program in order to reduce the impact of this disease, which remains a public health’s great concern. Under this scenario, a model capable of explaining closely the dynamics of chickenpox under the different vaccination strategies is of utter importance to assess their effectiveness. The proposed model takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmission of the disease. As shown in a comparative study, these assumptions are fundamental to describe properly the evolution of the disease. The Bayesian analysis of the model allows us to calculate the posterior distribution of the model parameters and the posterior predictive distribution of chickenpox ...

Mathematics

This paper deals with the weighted combination of forecasting methods using intelligent strategie... more This paper deals with the weighted combination of forecasting methods using intelligent strategies for achieving accurate forecasts. In an effort to improve forecasting accuracy, we develop an algorithm that optimizes both the methods used in the combination and the weights assigned to the individual forecasts, COmbEB. The performance of our procedure can be enhanced by analyzing separately seasonal and non-seasonal time series. We study the relationships between prediction errors in the validation set and those of ex-post forecasts for different planning horizons. This study reveals the importance of setting the size of the validation set in a proper way. The performance of the proposed strategy is compared with that of the best prediction strategy in the analysis of each of the 100,000 series included in the M4 Competition.