Department of Computer Science, University of Torino, Corso Svizzera 185, Torino, 10149, Italy.
Cancer Epidemiology Unit, Department of Medical Sciences, University of Torino - CPO Piemonte, Via Santena 7, Torino, 10126, Italy.
BMC Infect Dis. 2020 Oct 28;20(1):798. doi: 10.1186/s12879-020-05490-w.
Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21, 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21 until May 4 when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic.
This study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model.
The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities.
Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.
严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)是导致 2019 年冠状病毒病(COVID-19)的病原体,它具有高度传染性。自 2020 年 2 月 21 日意大利首次报告人与人之间传播 SARS-CoV-2 以来,感染 SARS-CoV-2 的人数迅速增加,主要集中在意大利北部地区,包括皮埃蒙特。3 月 21 日实施了严格的封锁,直到 5 月 4 日开始逐步放宽限制。在这种情况下,计算模型和计算机模拟是流行病学家可以利用的研究工具之一,用于了解疾病的传播,并评估社会措施以对抗、减轻或延迟疫情的传播。
本研究提出了一种扩展的易感-暴露-感染-移除-易感(SEIRS)模型,考虑了人口年龄结构。感染人群分为三个亚群:(i)未检测到的感染个体,(ii)隔离的感染个体和(iii)住院的感染个体。此外,模型中明确表示了政府限制措施的力度以及相关人群对这些措施的反应。
所提出的模型允许我们研究 COVID-19 在皮埃蒙特的传播的不同情况以及实施不同的感染控制措施和检测方法。结果表明,实施的控制措施已被证明在遏制疫情方面是有效的,减轻了大量未检测到的病例的潜在危险影响。我们还预测了在重新开放工作和社会活动后,为控制新一波 COVID-19 传播而实施的个体措施和社区监测的最佳组合。
我们的模型是一种有效的工具,可用于研究不同的情况,并为决策者提供不同控制策略的潜在影响的信息。这在未来几个月将至关重要,届时需要对放松控制措施做出非常关键的决定。