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A Review of Influenza Detection and Prediction Through Social Networking Sites

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dc.contributor.author Alessa, Ali
dc.contributor.author Faezipour, Miad
dc.date.accessioned 2018-08-27T14:27:57Z
dc.date.available 2018-08-27T14:27:57Z
dc.date.issued 2018-02-01
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2278
dc.description.abstract Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions. en_US
dc.description.uri https://doi.org/10.1186/s12976-017-0074-5
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.subject Flu trend en_US
dc.subject Social media data en_US
dc.subject Illness like influenza en_US
dc.title A Review of Influenza Detection and Prediction Through Social Networking Sites en_US
dc.type Article en_US
dc.publication.issue 2 en_US
dc.publication.name Theoretical Biology and Medical Modelling en_US
dc.publication.volume 15 en_US

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