Development and Comparison of Time Series Models in Predicting Severe Fever with Thrombocytopenia Syndrome Cases - Hubei Province, China, 2013-2020.

ABSTRACT:

Introduction

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus, which has a high mortality rate. Predicting the number of SFTS cases is essential for early outbreak warning and can offer valuable insights for establishing prevention and control measures.

Methods

In this study, data on monthly SFTS cases in Hubei Province, China, from 2013 to 2020 were collected. Various time series models based on seasonal auto-regressive integrated moving average (SARIMA), Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) were developed using these historical data to predict SFTS cases. The established models were evaluated and compared using mean absolute error (MAE) and root mean squared error (RMSE)

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11427339 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature