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Review
. 2018 Feb 1;15(1):2.
doi: 10.1186/s12976-017-0074-5.

A review of influenza detection and prediction through social networking sites

Ali Alessa  1 , Miad Faezipour  2   3
Affiliations
Review

A review of influenza detection and prediction through social networking sites

Ali Alessa et al. Theor Biol Med Model. .

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.

Keywords: Flu trend; Illness Like Influenza (ILI); Social media data.

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Conflict of interest statement

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Articles selection process. The figure shows the general overview of the used methodology of article selection
Fig. 2
Fig. 2
A method to monitor ILI and identify communities in Social Media. The figure shows the general overview of a proposed framework which monitors Influenza-Like Illness (ILI) mentioned in social media. It employs different data mining methods: text mining, link (graphical) mining, and structural data mining methods
Fig. 3
Fig. 3
Health state transition diagram. The figure shows that the HFSTM model could learn the state transition between the three states (S, E, I)
Fig. 4
Fig. 4
A framework for influenza outbreak detection. The figure shows a framework of a model to detect influenza outbreaks by analyzing web search queries using a neural network approach
Fig. 5
Fig. 5
The process of Neural Networks based detection. The figure shows an overview of the steps of the detection model based on Neural Networks
Fig. 6
Fig. 6
SNEFT architecture. The figure shows the architecture of the SNEFT framework. It utilizes the ARMA model and the data obtained from CDC. Both are used in collaboration for better flu prediction trends
See this image and copyright information in PMC

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