School of Mathematics, South China University of technology, Guangzhou, China.
Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.
J Cell Mol Med. 2019 Jan;23(1):395-404. doi: 10.1111/jcmm.13943. Epub 2018 Oct 19.
The seasonal outbreaks of influenza infection cause globally respiratory illness, or even death in all age groups. Given early-warning signals preceding the influenza outbreak, timely intervention such as vaccination and isolation management effectively decrease the morbidity. However, it is usually a difficult task to achieve the real-time prediction of influenza outbreak due to its complexity intertwining both biological systems and social systems. By exploring rich dynamical and high-dimensional information, our dynamic network marker/biomarker (DNM/DNB) method opens a new way to identify the tipping point prior to the catastrophic transition into an influenza pandemics. In order to detect the early-warning signals before the influenza outbreak by applying DNM method, the historical information of clinic hospitalization caused by influenza infection between years 2009 and 2016 were extracted and assembled from public records of Tokyo and Hokkaido, Japan. The early-warning signal, with an average of 4-week window lead prior to each seasonal outbreak of influenza, was provided by DNM-based on the hospitalization records, providing an opportunity to apply proactive strategies to prevent or delay the onset of influenza outbreak. Moreover, the study on the dynamical changes of hospitalization in local district networks unveils the influenza transmission dynamics or landscape in network level.
季节性流感爆发会在全球范围内引发呼吸道疾病，甚至导致各年龄段人群死亡。鉴于流感爆发前存在预警信号，通过及时采取疫苗接种和隔离管理等干预措施，可以有效降低发病率。然而，由于流感的复杂性涉及生物系统和社会系统的交织，因此实现流感爆发的实时预测通常是一项艰巨的任务。通过探索丰富的动态和高维信息，我们的动态网络标志物/生物标志物（DNM/DNB）方法为识别流感大流行灾难性转变之前的临界点开辟了新途径。为了通过应用 DNM 方法在流感爆发前检测预警信号，从日本东京和北海道的公共记录中提取并汇集了 2009 年至 2016 年因流感感染而住院的历史信息。DNM 基于住院记录提供了平均提前 4 周的预警信号，为季节性流感爆发前提供了机会，可以应用主动策略来预防或延迟流感爆发的发生。此外，对本地区域网络中住院动态变化的研究揭示了网络层面上的流感传播动力学或景观。
