Search

The aim is to analyse that, during the current pandemic situation, the reduction in the number of cases of influenza suggests that the non-pharmaceutical interventions (NPIs) applied to contain the expansion of SARS-CoV-2 also affect the influenza expansion. We analyse the interaction of influenza and SARS-CoV-2 spread based on an extended SEIRD model for the Catalonia region in Spain. We show that the dynamic evolution of the spread of SARS-CoV-2 and influenza generates a small interference. This interference causes a small reduction in the number of cases of seasonal influenza, reducing its expansion over the population. Other elements like the face mask mandates, social distancing and hand cleaning boost the reduction in both expansions. Influenza expansion will present a small reduction in the number of cases due to the interaction with SARS-CoV-2 expansion but mainly because the NPIs applied to the population.

Specification and Description Language (SDL) is a language that can represent the behavior and structure of a model completely and unambiguously. It allows the creation of frameworks that can run a model without the need to code it in a specific programming language. This automatic process simplifies the key phases of model building: validation and verification. SDLPS is a simulator that enables the definition and execution of models using SDL. In this paper, we present a new library that enables the execution of SDL models defined on SDLPS infrastructure on a HPC platform, such as a supercomputer, thus significantly speeding up simulation runtime. Moreover, we apply the SDL language to a social science use case, thus opening a new avenue for facilitating the use of HPC power to new groups of users. The tools presented here have the potential to increase the robustness of modeling software by improving the documentation, verification, and validation of the models.

The great social and economic impact that the COVID-19 pandemic has had on a global level has encouraged the development of new mathematical models that make it possible to better manage this and future pandemics. Here, we propose an extension of the classical epidemiological compartmental model SIR, the SEIAMD model (Susceptible–Exposed–Identified–Asymptomatic–iMmunized–Deceased), which considers the appearance of new virus variants, the use of vaccines, the existence of nonidentified asymptomatic individuals, and the loss of immunity acquired by infection or vaccination. Using an optimization model coded in Python that allows us to determine the change points that represent different behaviors of infected people, the SEIAMD model calculates, from official data, the different effective contact rates that were observed during the first seven waves of the COVID-19 pandemic in Tokyo due to the application of Non-Pharmaceutical Interventions (NPIs) and social habits. The closeness of the results obtained with our model and the real data, as well as the accuracy of predictions and observations, confirm the suitability of our model for studying the evolution of the COVID-19 pandemic in Tokyo.

We investigate the effects of school reopening on the evolution of COVID-19 infections during the second wave in Spain studying both regional and age-group variation within an interrupted time-series design. Spain's 17 Autonomous Communities reopened schools at different moments in time during September 2020. We find that in-person school reopening correlates with a burst in infections in almost all those regions. Data from Spanish regions gives a further leverage: in some cases, pre-secondary and secondary education started at different dates. The analysis of those cases does not allow to conclude whether reopening one educational stage had an overall stronger impact than the other. To provide a plausible mechanism connecting school reopening with the burst in contagion, we study the Catalan case in more detail, scrutinizing the interrupted time-series patterns of infections among age-groups and the possible connections between them. The stark and sudden increase in contagion among older children (10–19) just after in-person school reopening appears to drag the evolution of other age-groups according to Granger causality. This might be taken as an indirect indication of household transmission from offspring to parents with important societal implications for the aggregate dynamics of infections.

A simulation model, and more generically, a model, is founded on its assumptions. Assurance of the model’s correctness and correct use is needed to achieve accreditation. Often the exercise of working with a specific code misunderstands the overall process, focusing the resources on the model coding and forgetting the needed resources to ensure the validation of every step of the model definition and coding. The goal of this work is to present a methodology to help in the definition and use of the assumptions in the modeling process. To do so, we present a process to conduct a simulation project, an assumptions taxonomy, and a method that simplifies working with those assumptions. We propose to extend the traditional Validation, Verification, and Accreditation processes to a process composed of eight Validation, Verification, and Accreditation phases that cover the overall life cycle of a model. Although this paper is focused on a simulation model, we can extend the proposed method to a more general modeling approach.

The software selection process in the context of a big company is not an easy task. In the Business Intelligence area, this decision is critical, since the resources needed to implement the tool are huge and imply the participation of all organization actors. We propose to adopt the systemic quality model to perform a neutral comparison between four business intelligence self-service tools. To assess the quality, we consider eight characteristics and eighty-two metrics. We built a methodology to evaluate self-service BI tools, adapting the systemic quality model. As an example, we evaluated four tools that were selected from all business intelligence platforms, following a rigorous methodology. Through the assessment, we obtained two tools with the maximum quality level. To obtain the differences between them, we were more restrictive increasing the level of satisfaction. Finally, we got a unique tool with the maximum quality level, while the other one was rejected according to the rules established in the methodology. The methodology works well for this type of software, helping in the detailed analysis and neutral selection of the final software to be used for the implementation.

The purpose of this study is to determine Girona Cathedral’s thermal performance and to verify that it is suitable for containing historical pieces of art. We present the results of our analysis of the indoor thermal conditions during the period from January 2011 to December 2016. Real data were collected from strategically located probes inside the cathedral and from an outside weather station. The results were compared with an EnergyPlus computer model to verify the calculations and improve decision making. The model considers the envelope’s thermal inertia, the characteristics of the enclosure, and the passive systems for performing thermal analysis. The cathedral’s indoor environment is very stable. Because of a high capability of thermal-energy accumulation, a one-month thermal lag between indoor and outdoor temperatures exists. Furthermore, the maximum and minimum temperatures are dampened, with a two-degree variation above or below the outdoor average, depending on the season. The outdoor humidity can vary from 40% to 100% daily, whereas the indoor humidity variation is around 5%. This indoor stable condition protects the artistic objects in the building. This six-year monitoring and analysis allows for the determination that high-inertia buildings are suitable for displaying and preserving pieces of art and antique furniture, protecting them from deterioration.

The spread of the SARS-CoV-2 modeling is a challenging problem because of its complex nature and lack of information regarding certain aspects. In this paper, we explore a Digital Twin approach to model the pandemic situation in Catalonia. The Digital Twin is composed of three different dynamic models used to perform the validations by a Model Comparison approach. We detail how we use this approach to obtain knowledge regarding the effects of the nonpharmaceutical interventions and the problems we faced during the modeling process. We use Specification and Description Language (SDL) to represent the compartmental forecasting model for the SARS-CoV-2. Its graphical notation simplifies the different specialists’ understanding of the model hypotheses, which must be validated continuously following a Solution Validation approach. This model allows the successful forecasting of different scenarios for Catalonia. We present some formalization details, discuss the validation process and present some results obtained from the validation model discussion, which becomes a digital twin of the pandemic in Catalonia.

Based on a SEIRD model (Susceptible, Exposed, Infective, Recovered and Deceased) for COVID-19 infection with a new parametrization using a high infection rate, and a low fatality, we define the model in System Dynamics, Python, and Specification and Description Language (SDL). The different implementations obtained can be improved depending on the capabilities of the approach and, more interestingly, can be used to improve the Validation and Verification processes. In this paper, we are focused on describing how this comparison with other models’ validation processes allows us to find the parameters of the system dynamics model, hence the parameters of the pandemic. This is a crucial element, specifically in this case, because the data are not complete or validated for different reasons. We use using existing data from Korea and Spain and showing that the proposed method and the obtained parametrization for the model fit with the empirical evidence. We discuss some implications of the validation process and the model parametrization. We use this approach to implement a Decision support system that shows the current pandemic situation in Catalonia.

Simplifying the encoding of a simulation conceptual model representation reduces the number of errors that will be detected in the verification phase. In this paper, we present a mapping between Petri nets, a well-known formalism, and FlexSim, a well-known simulation tool. The proposal is illustrated through an example of how a model specified in a Petri net can be encoded easily, reducing the time needed to understand and verify the model. In the proposed methodology, the mapping must be defined at the initial stage of the encoding, starting from (in this case) a Petri net conceptual model, and ending at the encoding tool (FlexSim in this case). The main advantages of the proposed methodology are discussed.

We present a methodology to enable users to interact with the statistical information (survey data) of a public opinion institute which is stored in Cloud infrastructure. Mainly using R, this approach was developed following the open-data philosophy. Also, as we used R, the implementation is mainly based on open-source software. R has several advantages from the point of view of data management and acquisition, as it is becoming a common framework that can be used to structure the processes involved in any statistical operation. It further simplifies the access to data and enables the use of all the power of R in Cloud infrastructure. This methodology was applied successfully to develop a tool to manage the data of the Centre d’Estudis d’Opinió, but it can be applied to other institutions to enable open access to their data. The infrastructure was also deployed to Cloud infrastructure to assure its scalability and 24/7 access.

A Solution Validation involves comparing the data obtained from the system that are implemented following the model recommendations, as well as the model results. This paper presents a Solution Validation that has been performed with the aim of certifying that a set of computer-optimized designs, for a double façade, are consistent with reality. To validate the results obtained through simulation models, based on dynamic thermal calculation and using Computational Fluid Dynamic techniques, a comparison with the data obtained by monitoring a real implemented prototype has been carried out. The new validated model can be used to describe the system thermal behavior in different climatic zones without having to build a new prototype. The good performance of the proposed double façade solution is confirmed since the validation assures there is a considerable energy saving, preserving and even improving interior comfort. This work shows all the processes in the Solution Validation depicting some of the problems we faced and represents an example of this kind of validation that often is not considered in a simulation project.