Abstract
A mathematical model for forecasting the transmission of the COVID-19 outbreak is proposed to investigate the effects of quarantined and hospitalized individuals. We analyze the proposed model by considering the existence and the positivity of the solution. Then, the basic reproduction number (R0)—the expected number of secondary cases produced by a single infection in a completely susceptible population—is computed by using the next-generation matrix to carry out the stability of disease-free equilibrium and endemic equilibrium. The results show that the disease-free equilibrium is locally asymptotically stable if R0<1, and the endemic equilibrium is locally asymptotically stable if R0>1. Numerical simulations of the proposed model are illustrated. The sensitivity of the model parameters is considered in order to control the spread by intervention strategies. Numerical results confirm that the model is suitable for the outbreak that occurred in Thailand.
Highlights
It is well-known that the world is battling with a new infectious disease, namely, a novel coronavirus disease
We investigate local stability of both disease-free equilibrium and endemic equilibrium
The analysis shows the impact of parameters on the basic reproduction number in order to control the spread of COVID-19 disease
Summary
It is well-known that the world is battling with a new infectious disease, namely, a novel coronavirus disease. Infected individuals have many symptoms such as cough, difficulty in breathing, and fever [9], because the respiratory system can be destroyed by a coronavirus These outbreaks have affected people and economics around the world, since many governments used lockdown policies to reduce the spreading of the disease. Several pieces of research proposed mathematical models to forecast the spreading of infections such as HIV [16,17,18,19], tuberculosis [20], Ebola [21,22,23,24], Dengue [14,15,25], Zika [12,26,27], MERS [28,29,30], and SARS [29,31,32] Since this outbreak is a global problem, there are many mathematical models to predict the behavior of transmission for COVID-19 [33].
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