The Covid-19 outbreak of 2020 has required many governments to develop and adopt mathematical-statistical models of the pandemic for policy and planning purposes. To this end, this work provides a tutorial on building a compartmental model using Susceptible, Exposed, Infected, Recovered, Deaths and Vaccinated (SEIRDV) status through time. The proposed model uses interventions to quantify the impact of various government attempts made to slow the spread of the virus. Furthermore, a vaccination parameter is also incorporated in the model, which is inactive until the time the vaccine is deployed. A Bayesian framework is utilized to perform both parameter estimation and prediction. Predictions are made to determine when the peak Active Infections occur. We provide inferential frameworks for assessing the effects of government interventions on the dynamic progression of the pandemic, including the impact of vaccination. The proposed model also allows for quantification of number of excess deaths averted over the study period due to vaccination.
翻译:2020年的Covid-19疫情爆发要求许多国家政府为政策和规划目的制定和采用该流行病的数学统计模型,为此,这项工作为建立条块模型提供了指导,该模型将随着时间的推移使用可感知、暴露、感染、再生、死亡和接种(SEIRDV)状态;拟议模型采用干预措施,量化政府各种努力减缓病毒传播速度的影响;此外,该模型还纳入了疫苗接种参数,该参数在疫苗部署之前是静止的;使用巴耶斯框架进行参数估计和预测;预测以确定何时会出现高峰发病;我们提供推断框架,以评估政府干预措施对大流行病动态发展的影响,包括疫苗接种的影响;拟议模型还允许量化在疫苗接种研究期间避免超额死亡的人数。