Global Stability Analysis and Parameter Estimation for a Diphtheria Model: A Case Study of an Epidemic in Rohingya Refugee Camp in Bangladesh

Islam, Zahurul; Ahmed, Shohel; Rahman, Md. Mustafizur; Karim, M Abdel; Amin, Maryse

doi:10.1155/2022/6545179

Cited by 3 publications

(2 citation statements)

References 35 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several previous studies modeling the problem of diphtheria spread include Islam et al [9] in 2022, the SLIR model was developed by assuming the latent population as asymptomatic diphtheria-infected individuals and this latent population cannot transmit diphtheria. In 2017, Sornbundit et al [10] developed a SIR epidemic model to determine the spread of diphtheria in 77 provinces of Thailand.…”

Section: Introductionmentioning

confidence: 99%

“…This study was developed from the SLIR model studied by Islam et al [9] namely dividing the infected population into infected with symptoms and infected without symptoms, dividing the recovered population into recovered with full immunity and recovered with partial immunity, and assuming the population recovered can be reinfected. The model in this study also maintains optimal control in the form of vaccinations for susceptible populations and treatment for infected populations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Control of Mathematical Model of Diphtheria Spreading

Amalia

Toaha²

2022

JDM

View full text Add to dashboard Cite

This article examines optimal control model for the spread of diphtheria disease. Diphtheria is an infectious disease caused by the bacterium Corynebacterium diphtheriae. This model is divided into six compartments, namely population of susceptibles (𝑆), population of latent (L), population of infected with symptoms (Is), population of infected without symptoms (Ia), population of recovered with full immunity (Rf) and population of recovered with partial immunity (Rp). Two optimal controls are applied in the model, namely vaccination and treatment. The problem of optimal control is solved by using Pontryagin's minimal principle, which consists in solving a set of necessary conditions that must be satisfied by the optimal control and its associated state. The numerical method used to solve the optimal control problem is the forward-backward sweep method. Based on the results of numerical simulations, both controls should be administered in large numbers and continuously since the beginning of observation in order to reduce the number of diphtheria infected population and to control the spread of diphtheria.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Control of Mathematical Model of Diphtheria Spreading

Amalia

Toaha²

2022

JDM

View full text Add to dashboard Cite

show abstract

A Cost‐Effective Epidemiological Exposition of Diphtheria Outbreak by the Optimal Control Model: A Case Study of Rohingya Refugee Camp in Bangladesh

Islam,

Kabir,

Rahman

et al. 2024

Complexity

View full text Add to dashboard Cite

We consider a deterministic optimal control approach with cost‐effectiveness analysis for the diphtheria outbreak in the Rohingya refugee camp in Bangladesh. A deterministic epidemic dynamical model for diphtheria outbreaks has been developed with three optimal controls: vaccination, latent, and infectious treatment. Here, the qualitative study of the model has been interpreted. An objective function has been regarded as a cost function introduced by the optimal controls and the diseases themselves. Then, the existence and uniqueness of the optimal system have also been shown with the help of Pontryagin’s minimum principle. A numerical investigation has been carried out to solve the state and adjoint systems of optimality conditions. Furthermore, it has been investigated using several levels of availability of the optimal controls (vaccination, latent, and infectious treatment) to identify which level gives the best outcomes. Moreover, the cost‐effective analysis has been inquired with the numerical value of the cost function and the total cured population in each control strategy to clarify the strategy with the least cost but maximum remediation.

show abstract

Investigation of Statistical Machine Learning Models for COVID-19 Epidemic Process Simulation: Random Forest, K-Nearest Neighbors, Gradient Boosting

et al. 2022

View full text Add to dashboard Cite

COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results of their forecasting. The models developed are based on Random Forest, K-Nearest Neighbors, and Gradient Boosting methods. The models were studied for the adequacy and accuracy of predictive incidence for 3, 7, 10, 14, 21, and 30 days. The study used data on new cases of COVID-19 in Germany, Japan, South Korea, and Ukraine. These countries are selected because they have different dynamics of the COVID-19 epidemic process, and their governments have applied various control measures to contain the pandemic. The simulation results showed sufficient accuracy for practical use in the K-Nearest Neighbors and Gradient Boosting models. Public health agencies can use the models and their predictions to address various pandemic containment challenges. Such challenges are investigated depending on the duration of the constructed forecast.

show abstract

Global Stability Analysis and Parameter Estimation for a Diphtheria Model: A Case Study of an Epidemic in Rohingya Refugee Camp in Bangladesh

Cited by 3 publications

References 35 publications

Optimal Control of Mathematical Model of Diphtheria Spreading

Optimal Control of Mathematical Model of Diphtheria Spreading

A Cost‐Effective Epidemiological Exposition of Diphtheria Outbreak by the Optimal Control Model: A Case Study of Rohingya Refugee Camp in Bangladesh

Investigation of Statistical Machine Learning Models for COVID-19 Epidemic Process Simulation: Random Forest, K-Nearest Neighbors, Gradient Boosting

Contact Info

Product

Resources

About