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The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst's, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health). [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a regression analysis of energy consumption in the banking sector. In our case study, the target area is the Spanish banking sector, for which we divide the available data into a prediction and a validation subset. Power models were developed using test data from 55 banks. From the analysis, three models were obtained; where the first proposed model can be used to predict the energy consumption of the whole banking sector, while the rest of the models estimate the energy consumption for branches with low winter climate severity (Model 2) and high winter climate severity (Model 3). Models 2 and 3 differ from the first model in that they need independent variables measured in situ. As a result, the uncertainty of the response variable in the function of the independent variables is reduced by 56.8% for the first model and by 65.2% and 68.5% for the second and third proposed models, respectively. The validation of the first model, which is the model with the lowest determination coefficient, shows that this model is appropriate for predicting the energy consumption of bank branches with good energy consumption performance and detecting inefficiencies in bank branches with poor energy consumption performance. [Copyright &y& Elsevier]

Abstract: The paper compares the wave hindcast in the Western Mediterranean sea using the reanalysis wind fields from HIPOCAS and ERA-40 from ECMWF for November 2001. The study has concentrated on the Mediterranean coast of Spain where there are known difficulties with the wind and wave modelling. Two winter storms have been compared. The main differences between the significant wave heights using the ERA-40 reanalysis (ECMWF) and HIPOCAS reanalysis winds were observed to increase when moving southwards in the geographical domain at the offshore locations. Systematic negative biases of Hs were obtained with the ERA-40 data at all the coastal locations analyzed, whereas positive biases are typical for the HIPOCAS reanalysis. For offshore and coastal locations when using the ERA-40 data the Hs biases increased moving to South, while this pattern was not so clear for the HIPOCAS data. The inconsistencies in the comparisons of modelled waves against measurements seem to be associated with the quality of the wind fields. [Copyright &y& Elsevier]