SEIR Based Epidemic Modeling of COVID-19 in Turkey

Abstract

The coronavirus disease 2019 (COVID-19) outbreak was declared as an international pandemic rather than a local epidemic worldwide due to its rapid spread and high mortality. Countries have planned to return a new normal under some specific situations cope with economic effects that caused by the curfew and closure of companies when they reach the infection peak. Modeling the incidence of infectious diseases has attracted increasing attention from both scientific community and governments and a significant part of public health and intervention planning and have become a hot research topic in the last decades. Since epidemic diseases are effective in large populations, mathematical modeling has been used for a long time and has made important contributions in the analysis of these populations, in determining and controlling the spread rate of the epidemic. In this paper, we propose an extended model and calibrate the model and fitting with the coronavirus data by concerning with Susceptible-Exposed-Infected-Recovered (SEIR) model. In this study, we used an open public dataset to create a SEIR model for Turkey. This paper focuses on demonstrating the spreade of disease in Turkey. We gathered best approximation with the SEIR model parameters. The results suggest that SEIR model requires additional variables to model diasese spread accurately. Furthermore, the model needs to consist of data that always be the same case definition. The model plot results indicate the similar pattern.