Automatic Warning Practice of Multi-Source Surveillance and Multi-Point Trigger for Infectious Diseases - Yuhang District, Hangzhou City, Zhejiang Province, China, January-April 2024.

Abstract

Introduction: This study presents empirical evidence from the implementation of an automated infectious disease warning system utilizing multi-source surveillance and multi-point triggers in Yuhang District, Hangzhou City, Zhejiang Province, so as to provide reference for more extensive practice of infectious disease surveillance and early warning in the future.

Methods: The data were obtained from the Health Emergency Intelligent Control Platform of Yuhang District from January 1 to April 30, 2024, encompassing warning signal issuance and response documentation. Descriptive epidemiological method was used to analyze the early warning signals.

Results: From January 1 to April 30, 2024, the Health Emergency Intelligent Control Platform in Yuhang District generated 4,598 valid warning signals, with a warning signal positive rate of 36.43%. The early warning system detected 71 infectious disease outbreaks reported through the Intelligent Control Platform, including 24 single-source early warning and 47 multi-source early warning. The sensitivity was 78.02%, demonstrating improved performance compared to existing infectious disease surveillance and warning systems.

Conclusions: This represents the first domestic publication evaluating an automated multi-source surveillance and multi-point trigger warning system. By integrating and correlating multi-source data, the system can efficiently and accurately detect warning signals of infectious disease incidents, which has significant practical implications for early surveillance, warning, and management of infectious diseases.