论文标题
估计瑞典SARS-COV-2感染个体的比例
Estimates of the proportion of SARS-CoV-2 infected individuals in Sweden
论文作者
论文摘要
在本文中，研究了一个贝叶斯SEIR模型，以估计感染SARS-COV-2的人群的比例，SARS-COV-2是导致COVID-19的病毒。为了捕获人群中的异质性以及降低流行病率的干预措施的影响，该模型使用了随时间变化的接触率，其对数具有高斯的过程。泊松点过程用于模拟与19号共同死亡发生的死亡发生的发生，并使用每日死亡计数的数据与3月下旬在斯德哥尔摩进行的活跃感染的个体比例的快照一起校准模型。该方法应用于瑞典的地区。结果表明，到2020-05-15，被感染的人口的估计比例约为13.5％，在其他研究的地区，估计的人口比例为2.5％-15.6％。在斯德哥尔摩，每日死亡人数的高峰可能在我们身后，参数不确定性不会严重影响预期的每日死亡人数，也不会影响预期的累积死亡人数。但是，它确实会影响受感染个体的估计累积数量。在其他区域，如果没有可用的活动感染数量的随机抽样，则使用参数共享来改善估计值，但是参数不确定性仍然很大。
In this paper a Bayesian SEIR model is studied to estimate the proportion of the population infected with SARS-CoV-2, the virus responsible for COVID-19. To capture heterogeneity in the population and the effect of interventions to reduce the rate of epidemic spread, the model uses a time-varying contact rate, whose logarithm has a Gaussian process prior. A Poisson point process is used to model the occurrence of deaths due to COVID-19 and the model is calibrated using data of daily death counts in combination with a snapshot of the the proportion of individuals with an active infection, performed in Stockholm in late March. The methodology is applied to regions in Sweden. The results show that the estimated proportion of the population who has been infected is around 13.5% in Stockholm, by 2020-05-15, and ranges between 2.5% - 15.6% in the other investigated regions. In Stockholm where the peak of daily death counts is likely behind us, parameter uncertainty does not heavily influence the expected daily number of deaths, nor the expected cumulative number of deaths. It does, however, impact the estimated cumulative number of infected individuals. In the other regions, where random sampling of the number of active infections is not available, parameter sharing is used to improve estimates, but the parameter uncertainty remains substantial.
