Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran

BMC Research Notes

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[1] 0000 0004 0482 9174, grid.459564.f, Department of Science, Hamedan University of Technology, 65155, Hamedan, Iran;0000 0004 0611 9280, grid.411950.8, Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran;0000 0004 0611 9280, grid.411950.8, Department of Epidemiology, School of Public Health, Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran;Office of Information Technology, Hamedan Electrical Power Distribution Company, Hamedan, Iran;

关键词: Influenza;    Outbreak;    Public health surveillance;    Support vector machine;    Neural network;    Random Forest;   

DOI  :  10.1186/s13104-019-4393-y

来源: publisher

【 摘 要 】

ObjectiveForecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria.ResultsIt was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.

【 授权许可】

CC BY