<abstract xmlns="http://www.w3.org/1999/xhtml">In 2020, due to the COVID − 19 pandemic, various epidemiological models appeared in major studies [16, 22, 21], which differ in terms of complexity, type, etc. In accordance with the hypothesis, a complex model is more accurate and gives more reliable results than a simpler one because it takes into consideration more parameters.In this paper we study three different epidemiological models: a SIR, a SEIR and a SEIR − type model. Our aim is to set up differential equation models, which rely on similar parameters, however, the systems of equation and number of parameters deviate from each other. A visualization dashboard is implemented through this study, and thus, we are able not only to study the models but also to make users understand the differences between the complexity of epidemiological models, and ultimately, to share a more specific overview about these that are defined by differential equations [24].In order to validate our results, we make a comparison between the three models and the empirical data from Northern Italy and Wuhan, based on the infectious cases of COVID-19. To validate our results, we calculate the values of the parameters using the Least Square optimization algorithm.</abstract>

In 2020, due to the COVID − 19 pandemic, various epidemiological models appeared in major studies [16, 22, 21], which differ in terms of complexity, type, etc. In accordance with the hypothesis, a complex model is more accurate and gives more reliable results than a simpler one because it takes into consideration more parameters.In this paper we study three different epidemiological models: a SIR, a SEIR and a SEIR − type model. Our aim is to set up differential equation models, which rely on similar parameters, however, the systems of equation and number of parameters deviate from each other. A visualization dashboard is implemented through this study, and thus, we are able not only to study the models but also to make users understand the differences between the complexity of epidemiological models, and ultimately, to share a more specific overview about these that are defined by differential equations [24].In order to validate our results, we make a comparison between the three models and the empirical data from Northern Italy and Wuhan, based on the infectious cases of COVID-19. To validate our results, we calculate the values of the parameters using the Least Square optimization algorithm.

Comparing epidemiological models with the help of visualization dashboards

Sapientia Hungarian University of Transylvania

Faculty of Mathematics and Computer Science

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Comparing epidemiological models with the help of visualization dashboards"}

In 2020, due to the COVID − 19 pandemic, various epidemiological models appeared in major studies [16, 22, 21], which differ in terms of complexity, type, etc....

Comparing epidemiological models with the help of visualization dashboards

Publié en ligne: 29 janv. 2021

Pages: 260 - 282

Reçu: 13 oct. 2020

Accepté: 11 nov. 2020

DOI: https://doi.org/10.2478/ausi-2020-0016

Mots clés
Visualization dashboard, epidemiological model, optimization algorithm

© 2020 Csaba Farkas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Comparing epidemiological models with the help of visualization dashboards

Publié en ligne: 29 janv. 2021

Pages: 260 - 282

Reçu: 13 oct. 2020

Accepté: 11 nov. 2020

DOI: https://doi.org/10.2478/ausi-2020-0016

Mots clésVisualization dashboard, epidemiological model, optimization algorithm

© 2020 Csaba Farkas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Visualization dashboard, epidemiological model, optimization algorithm