SEAMHCRD deterministic compartmental model based on clinical stages of infection for COVID-19 pandemic in Sultanate of Oman

Author:

Varghese Abraham,Kolamban Shajidmon,Sherimon Vinu,Lacap Eduardo M.,Ahmed Saad Salman,Sreedhar Jagath Prasad,Al Harthi Hasina,Al Shuaily Huda Salim

Abstract

AbstractThe present novel coronavirus (COVID-19) infection has engendered a worldwide crisis on an enormous scale within a very short period. The effective solution for this pandemic is to recognize the nature and spread of the disease so that appropriate policies can be framed. Mathematical modelling is always at the forefront to understand and provide an adequate description of the transmission of any disease. In this research work, we have formulated a deterministic compartmental model (SEAMHCRD) including various stages of infection, such as Mild, Moderate, Severe and Critical to study the spreading of COVID-19 and estimated the model parameters by fitting the model with the reported data of ongoing pandemic in Oman. The steady-state, stability and final pandemic size of the model has been proved mathematically. The various transmission as well as transition parameters are estimated during the period from June 4th to July 30th, 2020. Based on the currently estimated parameters, the pandemic size is also predicted for another 100 days. Sensitivity analysis is performed to identify the key model parameters, and the parameter gamma due to contact with the symptomatic moderately infected is found to be more significant in spreading the disease. Accordingly, the corresponding basic reproduction number has also been computed using the Next Generation Matrix (NGM) method. As the value of the basic reproduction number (R0) is 0.9761 during the period from June 4th to July 30th, 2020, the disease-free equilibrium is stable. Isolation and tracing the contact of infected individuals are recommended to control the spread of disease.

Funder

The Research Council

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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