Abstract
The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control’s (CDC’s) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed. The IPSO-SVR was trained by the independent and dependent variables of the three models (models 1-3) as input and output. The trained IPSO-SVR method was used to predict the regional unweighted percentage ILI (%ILI) events in the US. The prediction results of each model are analyzed and compared. The results show that the IPSO-SVR method (model 3) demonstrates excellent performance in real-time prediction of ILIs, and further highlights the benefits of using real-time twitter data, thus providing an effective means for the prevention and control of flu.
Highlights
Influenza is a stealthy killer that threatens human health with its widespread contagion [1, 2]
Important novelty of our work is: the impact of flu transmission between geographical regions are analyzed and verifies whether the CDC Influenza-Like Illness (ILI) are complementary to the twitter data; we develop a correction to the existing Particle Swarm Optimization (PSO) algorithm that optimizes a penalty parameter C and kernel function parameter σ of an Support Vector Regression (SVR)-based model that improves the prediction for %ILI
By comparing the IPSO-SVR, PSO-SVR, Genetic Algorithms (GA)-SVR, and Cross Validation (CV)-SVR prediction results of model 2, we find that the prediction effect of the IPSO-SVR was better than other three methods in most regions (1 and 4-7)
Summary
Influenza (flu) is a stealthy killer that threatens human health with its widespread contagion [1, 2]. The flu refers to a viral acute respiratory infection caused by the common flu virus. If the flu is not effectively controlled, it can cause wide-ranging flu outbreaks that pose a threat to social stability and development. If we can predict a flu trend in some areas before the outbreak of flu, and take effective measures to mitigate the contagion ahead of time, we can control the spread of disease and reduce the loss of life to a certain extent. To prevent and control flu pandemic, the current worldwide Flu Surveillance System (FSS) relies on the collaboration of medical institutions (at all levels), e.g., centers for disease
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