Abdin Adam F, Fang Yi-Ping, Caunhye Aakil, Alem Douglas, Barros Anne, Zio Enrico
Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, 3 Rue Joliot Curie, Gif-sur-Yvette 91190, France.
Chair on Risk and Resilience of Complex Systems.
Eur J Oper Res. 2023 Jan 1;304(1):308-324. doi: 10.1016/j.ejor.2021.10.062. Epub 2021 Nov 6.
The global health crisis caused by the coronavirus SARS-CoV-2 has highlighted the importance of efficient disease detection and control strategies for minimizing the number of infections and deaths in the population and halting the spread of the pandemic. Countries have shown different preparedness levels for promptly implementing disease detection strategies, via mass testing and isolation of identified cases, which led to a largely varying impact of the outbreak on the populations and health-care systems. In this paper, we propose a new pandemic resource allocation model for allocating limited disease detection and control resources, in particular testing capacities, in order to limit the spread of a pandemic. The proposed model is a novel epidemiological compartmental model formulated as a non-linear programming model that is suitable to address the inherent non-linearity of an infectious disease progression within the population. A number of novel features are implemented in the model to take into account important disease characteristics, such as asymptomatic infection and the distinct risk levels of infection within different segments of the population. Moreover, a method is proposed to estimate the vulnerability level of the different communities impacted by the pandemic and to explicitly consider equity in the resource allocation problem. The model is validated against real data for a case study of COVID-19 outbreak in France and our results provide various insights on the optimal testing intervention time and level, and the impact of the optimal allocation of testing resources on the spread of the disease among regions. The results confirm the significance of the proposed modeling framework for informing policymakers on the best preparedness strategies against future infectious disease outbreaks.
由冠状病毒SARS-CoV-2引发的全球健康危机凸显了高效疾病检测和控制策略对于减少人群感染和死亡数量以及遏制大流行传播的重要性。各国在通过大规模检测和隔离确诊病例迅速实施疾病检测策略方面表现出不同的准备水平，这导致疫情对人群和医疗系统产生了很大不同的影响。在本文中，我们提出了一种新的大流行资源分配模型，用于分配有限的疾病检测和控制资源，特别是检测能力，以限制大流行的传播。所提出的模型是一种新颖的流行病学 compartmental 模型，被制定为非线性规划模型，适用于解决人群中传染病传播固有的非线性问题。该模型中实施了许多新颖的特征，以考虑重要的疾病特征，如无症状感染以及人群不同部分内不同的感染风险水平。此外，还提出了一种方法来估计受大流行影响的不同社区的脆弱性水平，并在资源分配问题中明确考虑公平性。该模型针对法国COVID-19疫情案例研究的实际数据进行了验证，我们的结果提供了关于最佳检测干预时间和水平以及检测资源的最优分配对疾病在各地区传播的影响的各种见解。结果证实了所提出的建模框架对于为政策制定者提供有关应对未来传染病爆发的最佳准备策略的信息的重要性。
