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

ABSTRACT:

Objective

Forecasting 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.

Results

It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequenc

SUBMITTER: Tapak L 

PROVIDER: S-EPMC6591835 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature