Real‐Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree

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.

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

“…The spread of infectious diseases in a region is described as the dynamic evolution of a nonlinear system, while the outbreak of COVID-19 is regarded as a qualitative state transition of a dynamic system ( Yang et al, 2020 ). From the perspective of dynamic modelling, the stage before the COVID-19 outbreak is regarded as a pre-outbreak stage, immediately after which the system undergoes a critical transition.…”

“…In previous studies, machine learning methods, i.e., logistic regression ( Yang et al, 2020 ), have been applied to identify the appearance of critical points based on years of high-dimensional data. However, for COVID-19 which originated at the beginning of 2020, the time series data obtained is of quite small scale, which is difficult for machine learning algorithm to learn the appropriate parameters and features.…”

“…Meanwhile, the study of mathematical models of epidemiology is helpful to understand the dynamics of epidemics, being an important tool to evaluate the potential effects of preventive and controlled measures, especially when their characteristics are still unclear ( Chatterjee et al, 2020 ; Khan et al, 2019 ; Cheng et al, 2020 ). Under such circumstances, by exploring the dynamical information from region networks and time-series data, we employed a combined model, the minimum-spanning-tree-based dynamical network marker (MST-DNM) ( Yang et al, 2020 ), to quantitatively describe the dynamics of COVID-19 transmission and thus identify the early-warning signal of a new wave of COVID-19 pandemic in Italy. This is the first time that this model has been applied to the study of the emerging infectious disease like COVID-19.…”

“…This model is improved from our recently proposed concept, the so-called dynamical network biomarker (DNB), which determines the critical state of complex diseases by analyzing the dynamics of driven biomolecules (i.e., a group of genes and proteins that are the leading factors to the critical state transition) ( Chen et al, 2012 ). The DNB based methods have been applied to a number of biological progresses and obtained remarkable results, including identifying the critical points of cell fate decision ( Mojtahedi et al, 2016 ) and cellular differentiation ( Richard et al, 2016 ), detecting the critical period during various biological processes ( Liu et al, 2014 ; Liu et al, 2019 ; Chen et al, 2015 ; Chen et al, 2017 ), and predicting the warning signals of influenza outbreaks ( Yang et al, 2020 ; Chen et al, 2019 ; Chen et al, 2020 ).…”

“…When applied to the region networks constructed based on geographical location and traffic conditions, the MST-DNM model has the ability to monitor the whole process of epidemic spread in Italy, and successfully identify the early-warning signals about two weeks in advance. It is worth noting that in the previous research, the key role of the minimum spanning tree in this model has been described in detail ( Yang et al, 2020 ), which can avoid issuing wrong warning signals due to the appearance of local abnormal correlation. Therefore, we pay more attention to the interpretability and practical significance of the MST-DNM model in our study, as detailed in the Materials & Methods section and in the Results section.…”