The dynamics of COVID-19: the effect of large-scale social restrictions

Rudianto Artiono, Budi Priyo Prawoto, Dayat Hidayat, Dwi Nur Yunianti, Yuliani Puji Astuti

Abstract


Due to the global pandemic of Covid-19, Indonesian government released a large-scale social restriction policy to reduce the spread of the disease. It still allowed people to have activities outside. The government also had another policy to divide covid-19 patient into three categories, such as People under Monitoring (ODP), Patients under Surveillance (PDP), and Confirmed Patients. This study aimed to explore the Covid-19 model in which large scale social restriction had been involved. It was not only to figure out the stability analysis of the model but also to predict the spread of the disease through numerical simulation. We constructed model based on the characteristic of Covid-19. Human population have been divided into five sub population, such as Susceptible (0), Susceptible (1), Exposed, Infected, and Recovered. Three of them related to the Covid-19 patient’s category. A disease-free equilibrium and endemic equilibrium have been determined. By using Next Generation Matrix, the basic reproduction number also had been obtained. Stability analysis have been done to explore the existence of the disease. A large scale-social restriction had a significant effect to the spread of the disease. The effectiveness of policy ranges from 80%-100% and it will reduce the number of people under monitoring.

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Published: 2020-10-28

How to Cite this Article:

Rudianto Artiono, Budi Priyo Prawoto, Dayat Hidayat, Dwi Nur Yunianti, Yuliani Puji Astuti, The dynamics of COVID-19: the effect of large-scale social restrictions, Commun. Math. Biol. Neurosci., 2020 (2020), Article ID 76

Copyright © 2020 Rudianto Artiono, Budi Priyo Prawoto, Dayat Hidayat, Dwi Nur Yunianti, Yuliani Puji Astuti. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

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