The COVID-19 pandemic has significantly impacted global health,social,and economic situations since its emergence in December 2019.The primary focus of this study is to propose a distinct vaccination policy and assess...The COVID-19 pandemic has significantly impacted global health,social,and economic situations since its emergence in December 2019.The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach,concentrating on the year 2021.We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated(SEIRV)model,incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis(EDA)approach.While no vaccine guarantees total immunity against the disease,and vaccine immunity wanes over time,it is critical to include and accurately estimate vaccine efficacy,as well as a constant vaccine immunity decay or wane factor,to better simulate the dynamics of vaccine-induced protection over time.Based on the distribution and effectiveness of vaccines,we integrated a data-driven estimation of vaccine efficacy,calculated at 75%for Malaysia,underscoring the model's realism and relevance to the specific context of the country.The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters.The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy.Our findings reveal that this distinct vaccination policy,which emphasizes an accelerated vaccination rate during the initial stages of the program,is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections.The study found that vaccinating 57–66%of the population(as opposed to 76%in the real data)with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections.The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination,offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies,particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy.While the methodology used in this study is specifically applied to national data from Malaysia,its successful application to local regions within Malaysia,such as Selangor and Johor,indicates its adaptability and potential for broader application.This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes,implying its usefulness for similar datasets from various geographical regions.展开更多
This paper considers stability analysis of a Susceptible-Exposed-Infected-Recovered-Virus(SEIRV)model with nonlinear incidence rates and indicates the severity and weakness of control factors for disease transmission....This paper considers stability analysis of a Susceptible-Exposed-Infected-Recovered-Virus(SEIRV)model with nonlinear incidence rates and indicates the severity and weakness of control factors for disease transmission.The Lyapunov function using Volterra-Lyapunov matrices makes it possible to study the global stability of the endemic equilibrium point.An optimal control strategy is proposed to prevent the spread of coronavirus,in addition to governmental intervention.The objective is to minimize together with the quantity of infected and exposed individuals while minimizing the total costs of treatment.A numerical study of the model is also carried out to investigate the analytical results.展开更多
基金his work was funded by the James Watt PhD Scholarship program supported by Heriot-Watt University.
文摘The COVID-19 pandemic has significantly impacted global health,social,and economic situations since its emergence in December 2019.The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach,concentrating on the year 2021.We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated(SEIRV)model,incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis(EDA)approach.While no vaccine guarantees total immunity against the disease,and vaccine immunity wanes over time,it is critical to include and accurately estimate vaccine efficacy,as well as a constant vaccine immunity decay or wane factor,to better simulate the dynamics of vaccine-induced protection over time.Based on the distribution and effectiveness of vaccines,we integrated a data-driven estimation of vaccine efficacy,calculated at 75%for Malaysia,underscoring the model's realism and relevance to the specific context of the country.The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters.The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy.Our findings reveal that this distinct vaccination policy,which emphasizes an accelerated vaccination rate during the initial stages of the program,is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections.The study found that vaccinating 57–66%of the population(as opposed to 76%in the real data)with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections.The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination,offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies,particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy.While the methodology used in this study is specifically applied to national data from Malaysia,its successful application to local regions within Malaysia,such as Selangor and Johor,indicates its adaptability and potential for broader application.This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes,implying its usefulness for similar datasets from various geographical regions.
基金Jose Francisco Gomez Aguilar acknowledges the support provided by CONACyT:Catedras CONACyT para jovenes investigadores 2014 and SNI-CONACyT.
文摘This paper considers stability analysis of a Susceptible-Exposed-Infected-Recovered-Virus(SEIRV)model with nonlinear incidence rates and indicates the severity and weakness of control factors for disease transmission.The Lyapunov function using Volterra-Lyapunov matrices makes it possible to study the global stability of the endemic equilibrium point.An optimal control strategy is proposed to prevent the spread of coronavirus,in addition to governmental intervention.The objective is to minimize together with the quantity of infected and exposed individuals while minimizing the total costs of treatment.A numerical study of the model is also carried out to investigate the analytical results.
