论文标题
建模网络流行病传播的强烈控制措施 - 案例研究
Modelling strong control measures for epidemic propagation with networks -- A COVID-19 case study
论文作者
论文摘要
我们表明，不需要对流行传播参数的精确知识来建立疾病传播的信息模型。我们提出了一个在各种外部控制制度下接触网络拓扑的详细模型，并证明这足以捕获显着的动态特征并为决策提供了信息。社区中的个体之间的接触为特征是接触图，该触点图的结构被选择以模仿社区控制措施。我们的城市水平传播模型传播剂（SEIR模型）通过（a）无标度接触网络（无控制）来表征传播； （b）随机图（消除质量聚会）； （c）小世界晶格（部分封锁 - “社会”距离）。该模型在2020年冠状病毒大流行扩散的数据之间表现出良好的定性一致性。获得了SEIR模型的相关速率参数的估计值，我们证明了这些估计值不确定的模型预测的鲁棒性。确定了这项工作的社会背景和效用，这有助于西澳大利亚州的大流行反应。
We show that precise knowledge of epidemic transmission parameters is not required to build an informative model of the spread of disease. We propose a detailed model of the topology of the contact network under various external control regimes and demonstrate that this is sufficient to capture the salient dynamical characteristics and to inform decisions. Contact between individuals in the community is characterised by a contact graph, the structure of that contact graph is selected to mimic community control measures. Our model of city-level transmission of an infectious agent (SEIR model) characterises spread via a (a) scale-free contact network (no control); (b) a random graph (elimination of mass gatherings); and (c) small world lattice (partial to full lockdown -- "social" distancing). This model exhibits good qualitative agreement between simulation and data from the 2020 pandemic spread of coronavirus. Estimates of the relevant rate parameters of the SEIR model are obtained and we demonstrate the robustness of our model predictions under uncertainty of those estimates. The social context and utility of this work is identified, contributing to a highly effective pandemic response in Western Australia.
