Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.

ABSTRACT:

Background

Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the periodic variations in time series. However, these models cannot handle the nonlinear trends correctly. Recurrent neural networks can address problems that involve nonlinear time series data. In this study, we intended to build prediction models for human brucellosis in mainland China with Elman and Jordan neural networks. The fitting and forecasting accuracy of the neural networks were compared with a traditional seasonal ARIMA model.

Methods

The reported human brucellosis cases were obtained from the website of the National Health and

SUBMITTER: Wu W 

PROVIDER: S-EPMC6518525 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature