Assessing the dynamics and impact of COVID-19 vaccination on disease spread:A data-driven approach  

作　　者：Farhad Waseel George Streftaris Bhuvendhraa Rudrusamy Sarat C.Dass 

机构地区：[1]School of Mathematical and Computer Sciences,Heriot-Watt University Malaysia,Putrajaya,Malaysia [2]School of Mathematical and Computer Sciences,Heriot-Watt University,Edinburgh,United Kingdom [3]School of Engineering and Physical Sciences,Heriot-Watt University Malaysia,Putrajaya,Malaysia [4]Maxwell Institute for Mathematical Sciences,United Kingdom [5]Faculty of Mathematics,Kabul University,Kabul,Afghanistan

出　　处：《Infectious Disease Modelling》2024年第2期527-556,共30页传染病建模（英文）

基　　金：his work was funded by the James Watt PhD Scholarship program supported by Heriot-Watt University.

摘　　要：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

关 键 词：Bayesian inference SEIRV COVID-19 Vaccination policy Importance sampling MALAYSIA DATA-DRIVEN 

分 类 号：R563.1[医药卫生—呼吸系统] R186[医药卫生—内科学]