Simulation of COVID-19 disease epidemic in Iran based on SIR model

Autor: Mehdi Kazempour Dizaji, Mohammad Varahram, Rahim Roozbahani, Atefe Abedini, Ali Zare, Arda Kiani, Mohammad Ali Emamhadi, Niloufar Alizedeh Kolahdozi, Syeyd Alireza Nadji, Majid Marjani
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Health Science Monitor, Vol 1, Iss 1, Pp 1-9 (2022)
Druh dokumentu: article
ISSN: 2980-8723
Popis: Background & Aims: The spread of the COVID-19 virus is currently considered the most important global health challenge. Therefore, it is very important to study and simulate the patterns of spread of this disease based on mathematical models. This study aimed to simulate the COVID-19 epidemic based on the SIR model, in Iran. Materials & Methods: In this study, the COVID-19 epidemic was simulated based on the susceptible-infected-recovered (SIR) mathematical model. According to the parameters of this model, different scenarios for this disease were examined. Finally, the simulation of the COVID-19 epidemic based on the values of these parameters was presented for Iran. Results: According to the results of this study, with increasing the level of social restrictions and health measures, the reproductive rate of COVID-19 decreased, and also with access to effective medicines and vaccines, the recovery rate of this disease increased, and fewer people became infected. Moreover, results showed that with the continuation of social restrictions and attention to health issues by the people in Iran, the peak of COVID-19 is seen within 50 days from the beginning of the epidemic also about 5% of the population is affected by this disease. The end of the initial wave of the disease was predicted at least 100 days after the onset of the epidemic. Conclusion: A simulation study to evaluate the prevalence of COVID-19 will provide comprehensive and complete information about the role of health care measures and social restrictions to prevent the spread of this disease to health researchers.
Databáze: Directory of Open Access Journals