Detecting the outbreak of influenza based on the shortest path of dynamic city network

Chen, Yingqi; Yang, Kun; Xie, Jialiu; Xie, Rong‐Jun; Liu, Zhengrong; Li, Rui; Chen, Pei

doi:10.7717/peerj.9432

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…Moreover, this is the first time that the improved method based on DNB has been applied to predict the outbreak of COVID-19. Unlike previous studies that used the DNB based methods to predict influenza outbreaks ( Yang et al, 2020 ; Chen et al, 2019 ; Chen et al, 2020 ), our study is based on small time series samples rather than years of time series data. Therefore, it could be employed to describe and monitor emerging infectious diseases like COVID-19.…”

Section: Discussionmentioning

confidence: 99%

“…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 ).…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Zhang

Yang

et al. 2021

PeerJ

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Background 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. Methods 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. Results 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dong

Zhang

Yang

et al. 2021

PeerJ

Self Cite

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“…Nevertheless, it is still a tough task to confirm the tipping point. In some previous studies, the fold-change thresholds were used to detect the warning signal [ 20 , 21 ]. However, such empirical or tunable threshold is not a universal method for different data or network structures.…”

Section: Methodsmentioning

confidence: 99%

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

Yang

Xie

et al. 2020

BioMed Research International

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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.

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“…The response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) triggered new thinking on how to deal with respiratory infectious diseases (1), such as seasonal influenza, which has led to widespread morbidity and mortality worldwide (2)(3)(4). Intense interactions between individuals in megacities, such as those between adults working long hours in confined office buildings, often fostered and amplified the spread of influenza (5), threatening the health of people, particularly the elderly and children.…”

Section: Introductionmentioning

confidence: 99%

Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data

Zhang

Yin

Mao

et al. 2022

Front. Public Health

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The outbreak of COVID-19 stimulated a new round of discussion on how to deal with respiratory infectious diseases. Influenza viruses have led to several pandemics worldwide. The spatiotemporal characteristics of influenza transmission in modern cities, especially megacities, are not well-known, which increases the difficulty of influenza prevention and control for populous urban areas. For a long time, influenza prevention and control measures have focused on vaccination of the elderly and children, and school closure. Since the outbreak of COVID-19, the public's awareness of measures such as vaccinations, mask-wearing, and home-quarantine has generally increased in some regions of the world. To control the influenza epidemic and reduce the proportion of infected people with high mortality, the combination of these three measures needs quantitative evaluation based on the spatiotemporal transmission characteristics of influenza in megacities. Given that the agent-based model with both demographic attributes and fine-grained mobility is a key planning tool in deploying intervention strategies, this study proposes a spatially explicit agent-based influenza model for assessing and recommending the combinations of influenza control measures. This study considers Shenzhen city, China as the research area. First, a spatially explicit agent-based influenza transmission model was developed by integrating large-scale individual trajectory data and human response behavior. Then, the model was evaluated across multiple intra-urban spatial scales based on confirmed influenza cases. Finally, the model was used to evaluate the combined effects of the three interventions (V: vaccinations, M: mask-wearing, and Q: home-quarantining) under different compliance rates, and their optimal combinations for given control objectives were recommended. This study reveals that adults were a high-risk population with a low reporting rate, and children formed the lowest infected proportion and had the highest reporting rate in Shenzhen. In addition, this study systematically recommended different combinations of vaccinations, mask-wearing, and home-quarantine with different compliance rates for different control objectives to deal with the influenza epidemic. For example, the “V45%-M60%-Q20%” strategy can maintain the infection percentage below 5%, while the “V20%-M60%-Q20%” strategy can maintain the infection percentage below 15%. The model and policy recommendations from this study provide a tool and intervention reference for influenza epidemic management in the post-COVID-19 era.

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Detecting the outbreak of influenza based on the shortest path of dynamic city network

Cited by 7 publications

References 25 publications

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

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

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

Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data

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