Abstract
This study concerns the COVID-19 pandemic in Thailand related to social isolation and vaccination policies. The behavior of disease spread is described by an epidemic model via a system of ordinary differential equations. The invariant region and equilibrium point of the model, as well as the basic reproduction number, are also examined. Moreover, the model is fitted to real data for the second wave and the third wave of the pandemic in Thailand by a sum square error method in order to forecast the future spread of infectious diseases at each time. Furthermore, the model predictive control technique with quadratic programming is used to investigate the schedule of preventive measures over a time horizon. As a result, firstly, the plan results are proposed to solve the limitation of ICU capacity and increase the survival rate of patients. Secondly, the plan to control the outbreak without vaccination shows a strict policy that is difficult to do practically. Finally, the vaccination plan significantly prevents disease transmission, since the populations who get the vaccination have immunity against the virus. Moreover, the outbreak is controlled in 28 weeks. The results of a measurement strategy for preventing the disease are examined and compared with a control and without a control. Thus, the schedule over a time horizon can be suitably used for controlling.
Highlights
COVID-19 is a new disease that appeared at the end of 2019 and is caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
This study developed a mathematical model to describe the COVID-19 pandemic’s disease spread in Thailand
The model is built using a system of nonlinear differential equations, and it is evaluated using model parameter estimation and real data from newly infected and death cases of the COVID-19 pandemic in Thailand
Summary
COVID-19 is a new disease that appeared at the end of 2019 and is caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Enahoro Iboi et al organized the classical model to assess the effectiveness of public health education campaigns during the COVID-19 pandemic in the United States This model concerns the isolation and non-isolation of people, as well as the number of hospitalized and non-hospitalized individuals [21], and Pakwan Riyapan et al. Ngonghala et al focused on Axioms 2021, 10, 274 the isolation of non-infected and infected people, and looked into using the quarantine model to resolve the COVID-19 pandemic They used sensitivity analysis to investigate the interaction of the outbreak and the parameters of transmission, quarantine, and contact tracing [4,22].
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