PropertyValue
?: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.
is ?:annotates of
?:creator
?:doi
  • 10.1155/2020/7351398
?:doi
?:externalLink
?:journal
  • Biomed_Res_Int
?:license
  • cc-by
?:pdf_json_files
  • document_parses/pdf_json/b2feff95c7dc1d3e522e9afb5a3450bc63b18fab.json
?:pmc_json_files
  • document_parses/pmc_json/PMC7547339.xml.json
?:pmcid
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • PMC
?:title
  • Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree
?:type
?:year
  • 2020-10-01

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