Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.

ABSTRACT:

Background

Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators.

Methods

We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training s

SUBMITTER: Lv CX 

PROVIDER: S-EPMC8377883 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature