Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China.

ABSTRACT: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013-2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms Autoregressive Integrated Moving Average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual ca

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10831807 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature