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arXiv 2005.13519q-bio.PEphysics.soc-ph
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瑞典SARS-CoV-2感染者比例的估计值

Estimates of the proportion of SARS-CoV-2 infected individuals in Sweden

Henrik Hult, Martina Favero

摘要:

本文研究了贝叶斯SEIR模型,以估计感染SARS-CoV-2的人口比例,该病毒是COVID-19的罪魁祸首。为了捕捉人口的异质性和干预措施的效果,以减少流行病的传播速度,该模型使用了随时间变化的接触率,其对数具有高斯过程先验。泊松点过程用于模拟COVID-19导致的死亡发生率,并使用每日死亡计数数据结合3月下旬在斯德哥尔摩进行的活动性感染个体比例快照对模型进行校准。该方法适用于瑞典的地区。结果显示,到2020年5月15日,斯德哥尔摩感染人口的估计比例约为13.5%,其他调查地区的比例在2.5% - 15.6%之间。在斯德哥尔摩,每日死亡人数的峰值可能已经过去,参数不确定性不会严重影响预期的每日死亡人数,也不会严重影响预期的累积死亡人数。然而,它确实影响了估计的累计感染人数。在其他区域,无法对活动性感染人数进行随机抽样,因此采用参数共享来改进估计数,但参数的不确定性仍然很大。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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