Author Contribution Correction: An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

Yang, Cheng Kuang; Chen, Ray Jade; Chou, Wan-Lin; Lee, Yuarn Jang; Lo, Yu-Sheng

doi:10.2196/13699

J Med Internet Res

2019

DOI: 10.2196/13699

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Author Contribution Correction: An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

Cheng Kuang Yang

Ray Jade Chen

Wan-Lin Chou

et al.

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Influenza trend prediction method combining Baidu index and support vector regression based on an improved particle swarm optimization algorithm

Xue,

Zhang,

Liang

et al. 2023

MATH

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<abstract> <p>Web-based search query data have been recognized as valuable data sources for discovering new influenza epidemics. However, selecting search and query keywords and adopting prediction methods pose key challenges to improving the effectiveness of influenza prediction. In this study, web search data were analyzed and excavated using big data and machine learning methods. The flu prediction model for the southern region of China, considering the impact of influenza transmission across regions and based on various keywords and historical influenza-like illness percentage (ILI%) data, was built (models 1–4) to verify the factors affecting the spread of the flu. To improve the accuracy of the influenza trend prediction, a support vector regression method based on an improved particle swarm optimization algorithm was proposed (IPSO-SVR), which was applied to the influenza prediction model to forecast ILI% in southern China. By comparing and analyzing the prediction results of each model, model 4, using the IPSO-SVR algorithm, exhibited higher prediction precision and more effective results, with its prediction indexes including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) being 0.0596, 0.2441 and 0.1884, respectively. The experimental results show that the prediction precision significantly increased when the IPSO-SVR method was applied to the constructed ILI% model. A new theoretical basis and implementation strategy were provided for achieving more accurate influenza prevention and control in southern China.</p> </abstract>

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Influenza trend prediction method combining Baidu index and support vector regression based on an improved particle swarm optimization algorithm

Xue,

Zhang,

Liang

et al. 2023

MATH

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show abstract

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Author Contribution Correction: An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

Cited by 1 publication

References 0 publications

Influenza trend prediction method combining Baidu index and support vector regression based on an improved particle swarm optimization algorithm

Influenza trend prediction method combining Baidu index and support vector regression based on an improved particle swarm optimization algorithm

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