A Mathematical Model of COVID-19 Pandemic: A Case Study of Bangkok, Thailand

Riyapan, Pakwan; Shuaib, Sherif Eneye; Intarasit, Arthit

doi:10.1155/2021/6664483

Cited by 68 publications

(68 citation statements)

References 34 publications

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“…In this section, we determine the basic reproduction number R0 and obtain the existence of the disease-free equilibrium (DFE) and the endemic equilibrium (EE) of system (2)(3)(4)(5)(6). Summing up the five equations of system (2-6) we get…”

Section: Next Generation Matrix and Reproduction Number/ Equivalent Threshold Parametermentioning

confidence: 99%

“…Therefore, from biological considerations, we study system (2)(3)(4)(5)(6) ]; where i, j =1,2,3..., m and where 𝑧 0 is the disease-free equilibrium. The entries of FV -1 give the rate at which infected individuals in 𝑧 𝑗 produce new infections in 𝑧 𝑖 , times the average length of time an individual spends in a single visit to compartment j. R0 is given by the spectral radius (dominant eigenvalue) of the matrix FV -1 .…”

Section: Next Generation Matrix and Reproduction Number/ Equivalent Threshold Parametermentioning

confidence: 99%

“…In this section we will obtain the disease free and endemic equilibrium point for the system described by (2)(3)(4)(5)(6). On solving equations (8-12), we get the disease-free equilibrium (DFE) point Z0 (0,0,0,0,0) and the Jacobian matrix for the above system is given by: -…”

Section: Disease Free and Endemic Equilibriummentioning

confidence: 99%

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A Mathematical Model for Stability Analysis of Covid like Epidemic/Endemic/Pandemic

Kumar

Awasthi

2021

Preprint

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The transmission and spread of infectious disease like Covid-19 occurs through horizontal and vertical mode. The causative pathogens for such kind of disease may be bacterium, protozoa, virus or toxin. The infectious diseases like AIDS, SARS, MARS, Polio Plague, Bubonic Plague and Covid-19 have destroyed the social and economic structure of world population. The world scientific community adopts different mechanisms to model and analyse the population dynamics of infectious disease outbreaks. Mathematical Modelling is the most effective tool to take the informed decision about the containment, control and eradication of the pandemic. The main focus of Government and public health authorities is to design the strategy in destabilising the spread and impact of the infections. A series of models-SIR, SEIR, SEIRD, SEAIHCRD, SAUQAR has been under study to combat the Covid-19 since its inception. An effort has been made to design the model based on reproduction number, endemic equilibrium and disease-free equilibrium to curtail the impact of Covid-19 through stability analysis methods-Hurwitz stability criteria, Lyapunov Method and Linear Stability Analysis.

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Section: Next Generation Matrix and Reproduction Number/ Equivalent Threshold Parametermentioning

confidence: 99%

Section: Next Generation Matrix and Reproduction Number/ Equivalent Threshold Parametermentioning

confidence: 99%

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A Mathematical Model for Stability Analysis of Covid like Epidemic/Endemic/Pandemic

Kumar

Awasthi

2021

Preprint

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“…Calistus N. Ngonghala et al focused on 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].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of COVID-19 Pandemic with Social Isolation and Vaccination Policies in Thailand

Jankhonkhan

Sawangtong

2021

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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.

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“…The use of mathematical models to describe the transmission and spread of COVID-19, so as to gain insights into the disease behaviour and develop strategies for its curtailment, has been extensively studied by researchers. (see in [5][6][7][8][9][10][11][12][13][14][15][16][17][18] and the references cited therein.) Our model has a unique difference from the models developed in [10,18] as follows: the authors of [18] developed a mathematical model of COVID-19 to investigate the impact of non-pharmaceutical interventions for possible control of the disease.…”

Section: Introductionmentioning

confidence: 99%

Qualitative and Quantitative Analyses of COVID-19 Dynamics

Faniran

Nkamba

Manga

2021

Axioms

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COVID-19 is a highly contagious disease which has spread across the world. A deterministic model that considers an important component of individuals with vertically transmitted underlying diseases (high-risk susceptible individuals), rather than the general public, is formulated in this paper. We also consider key parameters that are concerned with the disease. An epidemiological threshold, R0, is computed using next-generation matrix approach. This is used to establish the existence and global stability of equilibria. We identify the most sensitive parameters which effectively contribute to change the disease dynamics with the help of sensitivity analysis. Our results reveal that increasing contact tracing of the exposed individuals who are tested for COVID-19 and hospitalizing them, largely has a negative impact on R0. Results further reveal that transmission rate between low-risk/high-risk susceptible individuals and symptomatic infectious individuals β and incubation rate of the exposed individuals σ have positive impact on R0. Numerical simulations show that there are fewer high-risk susceptible individuals than the general public when R0<1. This may be due to the fact that high-risk susceptible individuals may prove a bit more difficult to control than the low-risk susceptible individuals as a result of inherited underlying diseases present in them. We thus conclude that high level of tracing and hospitalizing the exposed individuals, as well as adherence to standard precautions and wearing appropriate Personal Protective Equipment (PPE) while handling emergency cases, are needed to flatten the epidemic curve.

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A Mathematical Model of COVID-19 Pandemic: A Case Study of Bangkok, Thailand

Abstract: In this study, we propose a new mathematical model and analyze it to understand the transmission dynamics of the COVID-19 pandemic in Bangkok, Thailand. It is divided into seven compartmental classes, namely, susceptible S , exposed … Show more

Cited by 68 publications

References 34 publications

A Mathematical Model for Stability Analysis of Covid like Epidemic/Endemic/Pandemic

A Mathematical Model for Stability Analysis of Covid like Epidemic/Endemic/Pandemic

Model Predictive Control of COVID-19 Pandemic with Social Isolation and Vaccination Policies in Thailand

Qualitative and Quantitative Analyses of COVID-19 Dynamics

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