Abstract
The influenza pandemic is a wide-ranging threat to people's health and property all over the world. Developing effective strategies for predicting the influenza outbreak which may prevent or at least get ready for a new influenza pandemic is now a top global public health priority. Owing to the complexity of influenza outbreaks that are usually involved with spatial and temporal characteristics of both biological and social systems, however, it is a challenging task to achieve the real-time monitoring of influenza outbreaks. In this study, by exploring the rich dynamical information of the city network during influenza outbreaks, we developed a computational method, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to identify the tipping point or critical stage prior to the influenza outbreak. With historical records of influenza outpatients between 2009 and 2018, the MST-DNM strategy has been validated by accurate predictions of the influenza outbreaks in three Japanese cities/regions, respectively, i.e., Tokyo, Osaka, and Hokkaido. These successful applications show that the early-warning signal was detected 4 weeks on average ahead of each influenza outbreak. The results show that our method is of considerable potential in the practice of public health surveillance.
Highlights
Introduction650,000 deaths annually are associated with respiratory diseases caused by seasonal influenza
Influenza, a seasonal, contagious, and widespread respiratory illness, has always been a huge threat to people’s health.According to the World Health Organization, up to650,000 deaths annually are associated with respiratory diseases caused by seasonal influenza
By exploring the rich dynamical information provided by high-dimensional records of clinic hospitalization data, we developed a practical computational method, i.e., the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively measure the dynamical change of a city network and detect the earlywarning signal of an influenza outbreak
Summary
650,000 deaths annually are associated with respiratory diseases caused by seasonal influenza. In the United States, the influenza pandemic leads to an average of 610,660 deaths per year and 3.1 million hospitalized days [1]. 81.7 billion US dollars each year [2] From both public health and economic perspective, it is crucial to detect the early-warning signal of imminent influenza outbreak so that timely preventive measures can be carried out to prevent a new influenza pandemic or at least reduce the magnitude of influenza outbreaks [3, 4]. The records of worldwide influenza pandemics showed that each outbreak differed from the others with respect to etiologic agents, epidemiology, and disease severity [5]. It is of great concern to develop a cost-effective computational method for predicting the outbreak of influenza only based on the available data
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