PROMEDyA

Our research group works on prediction and optimisation techniques. Prediction of future observations and optimisation in order to build automatic decision support tools in an uncertain environment. This has already led to interesting results, especially in the fields of medicine and public health, but also in industry and finance. Consequently, our purpose is twofold: to deepen the study of prediction models that can incorporate temporal and/or spatial relationships, as well as covariates, and to continue advancing in the development of methodologies that incorporate uncertainty in optimisation, which will allow us to build new tools to support decision-making in different areas of planning and management. In particular, we will investigate the analysis of scenarios with the presence of uncertainty for which there is information given by a series of historical data, for which we will use time series models, and those in which in addition to the temporal relationship there is a neighbourhood relationship between contemporaneous observations, for which we will use spatio-temporal models. Previous work have considered prediction models based on dynamic state-space models with innovations, and have advanced theoretically in the incorporation of multistationarity, covariates and autocorrelated errors, with the aim that they can be used in the automatic prediction of banks of time series. Recently, we have introduced fuzzy time series methods to obtain as a prediction a fuzzy number that can be incorporated into optimisation models using fuzzy logic to incorporate uncertainty. Our aim is to extend the field of application of these methodologies by means of simulation-optimisation models that make it possible to analyse the quality of the solutions and the optimisation of the objectives. We are also working on compositional time series, which allow us to contextualise mixed models for multivariate longitudinal compositional data in a microbiome setting. Recently, we have been working on the combination of forecasts, using weighted averages of forecasts obtained with different methods, with the aim of designing a decision support system that allows the adjustment of weights and the optimal selection of prediction models to obtain more reliable forecasts. The study of the geographical and temporal variability of health phenomena is currently very popular in the world of epidemiology. Numerous risk smoothing models that simultaneously incorporate the spatial dependence of risks between nearby regions and the temporal dependence of risks for each of the regions have been proposed in recent years. Classical models have been designed for retrospective analysis of incidence time series and therefore do not address fundamental issues from the point of view of planning preventive and control actions such as predicting the onset of outbreaks, predicting epidemic peaks and ending epidemics. We want to analyse some problems related to the prediction of the spread of epidemics, focusing simultaneously on the spatial and temporal components of the problem. Along the same lines, simultaneous incorporation of temporal and spatial components in the models studied, we are working on various scenarios: Bayesian hierarchical model extension, recently proposed by members of our team, which allows estimating risks and detecting clusters simultaneously where it is considered that the spatial dependence between relative risks does not necessarily conform to neighbou