论文标题
COVID-19的接触图流行模型用于传播和干预策略
Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies
论文作者
论文摘要
近年来，2019年冠状病毒病（COVID-19）大流行已成为一场全球公共卫生危机。众所周知，人接触网络的结构在传播疾病的传播中起着重要作用。在这项工作中，我们研究了COVID-19 CGEM的结构意识模型。如果我们假设接触网络是Erdos-Renyi（ER）图，则该模型将与基于经典的隔室模型相似，即每个人都与其他所有人都有相同的概率接触。相比之下，CGEM具有更大的表现力，可以插入实际的联系网络，或更现实的代理。此外，CGEM可以对执行和释放不同的非药物干预（NPI）策略进行更精确的建模。通过一组广泛的实验，我们在假设不同的基础结构时表现出流行曲线之间的显着差异。更具体地说，我们证明，基于隔室的模型高估了感染的传播3倍，并且在某些对依从性因子的现实假设下，低估了一些NPI的有效性，从而误解了其他人（例如，预测后来的峰值），并低估了重新启动后的第二个高峰规模。
The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that the structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This model becomes similar to the classical compartment-based models in epidemiology if we assume the contact network is a Erdos-Renyi (ER) graph, i.e. everyone comes into contact with everyone else with the same probability. In contrast, CGEM is more expressive and allows for plugging in the actual contact networks, or more realistic proxies for it. Moreover, CGEM enables more precise modelling of enforcing and releasing different non-pharmaceutical intervention (NPI) strategies. Through a set of extensive experiments, we demonstrate significant differences between the epidemic curves when assuming different underlying structures. More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening.
