Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China.

ABSTRACT:

Background

Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends.

Methods

Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-ad

SUBMITTER: Su K 

PROVIDER: S-EPMC6796527 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature