Abstract

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.

Highlights

  • The definition of a model that defines the behavior of a phenomenon like a pandemic relies on several well-known parameters like the R0, the estimation of this parameter, based on the data we obtain from the observations is not a simple task, due to the complexity to cope with a new phenomenon, and the time-lapse we use to analyze this information

  • To cope with these estimation problems, and relying on an approximation based on the data, we can use simulation, as a technique that can be used to represent the causality of our models, to estimate correctly the parameters if they fit with the different datasets that we have

  • We present this approach and we point to some preliminary results regarding the parametrization of our models

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Summary

Introduction

The definition of a model that defines the behavior of a phenomenon like a pandemic relies on several well-known parameters like the R0 , the estimation of this parameter, based on the data we obtain from the observations is not a simple task, due to the complexity to cope with a new phenomenon, and the time-lapse we use to analyze this information.To cope with these estimation problems, and relying on an approximation based on the data, we can use simulation, as a technique that can be used to represent the causality of our models, to estimate correctly the parameters if they fit with the different datasets that we have.this approach relies on a specific codification of a simulation model, which may have some undetected problems, i.e., a codification error. The definition of a model that defines the behavior of a phenomenon like a pandemic relies on several well-known parameters like the R0 , the estimation of this parameter, based on the data we obtain from the observations is not a simple task, due to the complexity to cope with a new phenomenon, and the time-lapse we use to analyze this information To cope with these estimation problems, and relying on an approximation based on the data, we can use simulation, as a technique that can be used to represent the causality of our models, to estimate correctly the parameters if they fit with the different datasets that we have.

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