Dong Min, Zhang Xuhang, Yang Kun, Liu Rui, Chen Pei
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
School of Mathematics, South China University of Technology, Guangzhou, China.
PeerJ. 2021 Jun 29;9:e11603. doi: 10.7717/peerj.11603. eCollection 2021.
Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems.
By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study.
The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.
11月26日，意大利新冠肺炎确诊感染病例超过150万例，在第二波新冠肺炎疫情中，其死亡人数迅速上升，给医院带来沉重负担。因此，有必要对未来新冠肺炎可能的爆发进行预测和早期预警，以便及时采取适当的控制措施。然而，由于新冠肺炎传播涉及生物系统和社会系统的复杂性，对其传播和爆发进行实时预测通常具有挑战性。
通过挖掘区域网络中的动态信息和短期时间序列数据，我们开发了一种数据驱动模型，即基于最小生成树的动态网络标志物（MST-DNM），以定量分析和监测新冠肺炎传播的动态过程。具体而言，我们收集了意大利2020年2月24日至2020年11月28日期间新冠肺炎感染导致的每日病例历史信息。当应用于意大利的区域网络时，MST-DNM模型能够监测新冠肺炎传播的全过程，并成功识别早期预警信号。本研究详细解释了我们模型的可解释性和实际意义。
对意大利区域网络动态变化的研究揭示了网络层面新冠肺炎传播的动态情况。值得注意的是，MST-DNM的驱动力仅依赖于小样本，而非多年的时间序列数据。因此，它在新兴传染病的公共监测中具有巨大潜力。
