Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories.
Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories.
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DOI：
10.1016/j.epidem.2021.100449
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发表时间：
2021-06
期刊：
影响因子：
3.8
通讯作者：
Hens N
中科院分区：
文献类型：
--
作者：
Abrams S;Wambua J;Santermans E;Willem L;Kuylen E;Coletti P;Libin P;Faes C;Petrof O;Herzog SA;Beutels P;Hens N
随着2019冠状病毒病（COVID-19）大流行在全球范围内爆发，全球很大一部分人口正在或一直处于严格的保持距离和隔离措施之下，许多国家处于部分或全面封锁状态。采取这些措施是为了减少疾病的传播并减轻卫生保健系统的压力。评估这些干预措施的影响以及监测这些严格措施的逐步放松，对于了解如何在未来控制COVID-19流行病的卷土重来至关重要。在本文中，我们使用随机年龄结构离散时间区隔模型来描述COVID-19在比利时的传播。我们的模型通过整合社会接触数据，明确考虑了年龄结构，以(i)评估2020年3月13日实施的封锁对比利时新住院人数的影响；（ii）进行情景分析，估计可能的退出战略对未来潜在的COVID-19浪潮的影响。更具体地说，上述模型适用于住院数据、每日COVID-19死亡人数数据和告知人群（血清）疾病患病率的系列血清学调查数据，同时依赖于贝叶斯MCMC方法。我们的年龄结构随机模型很好地描述了观察到的疫情数据，无论是在比利时人口中的住院率还是与COVID-19相关的死亡人数。尽管根据遵守保持身体距离措施的影响以及放松严格的封锁措施可能导致接触增加的影响，对疫情未来发展的各种预测进行了广泛探讨，但未来几个月疫情的演变仍有很多不确定因素。
Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.
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