Analysing of Tuberculosis in Turkey through SIR, SEIR and BSEIR Mathematical Models

Ucakan, Yasin; Gülen, Seda; Köklü, Kevser

doi:10.1080/13873954.2021.1881560

Cited by 24 publications

(23 citation statements)

References 47 publications

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“…Although this model has been successful in describing disease behaviour, it is unrealistic to ignore other compartments and control techniques such as immunisation, treatment, quarantine, isolation, and the impact of age and sex. As a result, several researchers have concentrated on developing more realistic simulations [18]. Egger, Johnson, Althaus, Schöni, Salanti, Low and Norris [7] described that mathematical modelling studies can be used to generate emergency guidelines or assess the pandemic potential of emerging outbreaks.…”

Section: Seir Modelmentioning

confidence: 99%

Solving SEIR Model Using Symmetrized Runge Kutta Methods

Bakar¹,

Razali²

2022

Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)

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During these pandemic, SEIR model has become a popular topic among researchers. Such epidemiological model is said to be a great decision tool to forecast the behaviour of Covid19 outbreak for future actions. Following trend, this paper attempts to use symmetrized Runge Kutta methods; Implicit Midpoint Rule (IMR) and Implicit Trapezoidal Rule (ITR), to solve this model. The base method; IMR and ITR are tested with one-step symmetrization (1ASIMR, 1ASITR, 1PSIMR, and 1PSITR) and two-step symmetrization (2ASIMR, 2ASITR, 2PSIMR and 2PSITR) in both active and passive modes. Symmetrized Runge-Kutta method is best when using along stiff equations. Thus, we used high rate of disease transmission, β to study the efficiency of each method and predict the proportion of individuals in each category according to the SEIR model. All the parameters and values are obtained through official websites of Malaysia and calculated based on previous studies starting from 2 nd December 2021 to 1st January 2022. The equilibrium points: disease free equilibrium (DFE) and the disease endemic equilibrium (DEE) are presented and calculated. Next, the basic reproduction number, R 0 is computed using the next generation method. The result depicted R 0 > 1, which indicates the disease has spread over. Finally, 2PSIMR is found to be the best method out of all. The efficiency of the methods is discussed and compared.

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Section: Seir Modelmentioning

confidence: 99%

Solving SEIR Model Using Symmetrized Runge Kutta Methods

Bakar¹,

Razali²

2022

Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)

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“…At the beginning of the twentieth century, Kermack and McKendrick 9 established the SIR (Susceptible Infected Recovered) model using the nonlinear dynamics method, and began to have the establishment of the dynamic model of infectious diseases and the analysis and research of its dynamics. Then according to the transmission characteristics of different infectious diseases, people developed the SIRS model that considers the secondary disease.…”

Section: Introductionmentioning

confidence: 99%

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

Qiu

Sun

et al. 2022

Sci Rep

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Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the epidemic. Based on the SEIR model, the improved SEIR models were established with considering the incubation period, the isolated population, and genetic algorithm (GA) parameter optimization method. The improved SEIR models can predict the trend of the epidemic situation better and obtain the more accurate epidemic-related parameters. Comparing some key parameters, it is capable to evaluate the impact of different epidemic prevention measures and the implementation of different epidemic prevention levels on the COVID-19, which has significant guidance for further epidemic prevention measures.

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“…Moreover, the demand side has been affected by lockdown and consumers' physical spending increasing the challenges on the market. The COVID-19 outbreak re-exposes the importance of epidemic researches and the development of mathematical models to describe the behavior of epidemics [11]. Modeling describes the dynamic of epidemics and helps to take informed public health interventions [1].…”

Section: Introductionmentioning

confidence: 99%

Modeling Shock Propagation on Supply Chain Networks: A Stochastic Logistic-Type Approach

Colapinto¹,

Torre²,

Laganà³

et al. 2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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Supply Chains have been more and more suffering from unexpected industrial, natural events, or epidemics that might disrupt the normal flow of materials, information, and money. The recent pandemic triggered by the outbreak of the new COVID-19 has pointed out the increasing vulnerability of supply chain networks, prompting companies (and governments) to implement specific policies and actions to control and reduce the spread of the disease across the network, and to cope with exogenous shocks. In this paper, we present a stochastic Susceptible-Infected-Susceptible (SIS) framework to model the spread of new epidemics across different distribution networks and determine social distancing/treatment policies in the case of local and global networks. We highlight the relevance of adaptability and flexibility of decisions in unstable and unpredictable scenarios.

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Analysing of Tuberculosis in Turkey through SIR, SEIR and BSEIR Mathematical Models

Cited by 24 publications

References 47 publications

Solving SEIR Model Using Symmetrized Runge Kutta Methods

Solving SEIR Model Using Symmetrized Runge Kutta Methods

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

Modeling Shock Propagation on Supply Chain Networks: A Stochastic Logistic-Type Approach

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