The Covid-19 outbreak of 2020 has required many governments to develop mathematical-statistical models of the outbreak for policy and planning purposes. This work provides a tutorial on building a compartmental model using Susceptibles, Exposed, Infected, Recovered, Deaths and Vaccinated (SEIRDV) status through time. A Bayesian Framework is utilized to perform both parameter estimation and predictions. This model uses interventions to quantify the impact of various government attempts to slow the spread of the virus. Predictions are also made to determine when the peak Active Infections will occur.
翻译：2020年的Covid-19爆发要求许多国家政府为政策和规划目的开发疫情的数学-统计模型,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应、暴露、感染、恢复、死亡和接种(SEIRDV)状态,利用贝叶斯框架进行参数估计和预测。该模型使用干预措施量化政府试图减缓病毒传播的各种尝试的影响。还进行预测,以确定何时会出现峰值活性感染。
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