Abstract
BACKGROUND: Past reviews of infectious disease early warning systems have encountered some limitations, such as a focus on specific diseases or regions in certain studies and constraints imposed by time delays in others. This study conducts a comprehensive analysis of infectious disease early warning systems, with a particular emphasis on assessing the development of these systems in the recent five years (2019 to 2023). The goal is to provide insights for future related research. METHODS: A comprehensive retrospective review was undertaken, utilizing data sourced from four prominent databases: WanFang Data, China National Knowledge Infrastructure (CNKI), Web of Science, and PubMed. Following a meticulous classification process, a total of 49 articles aligning with our inclusion criteria were identified. To streamline the data collection and organization process, standardized extraction forms were employed, and data were efficiently organized using Microsoft Excel spreadsheet. RESULTS: This study uncovered various warning systems, including health departments, hospitals, social media platforms, statistical bureaus, meteorological departments, and wastewater monitoring systems. Drawbacks of traditional manual and statistical models included slow responsiveness and a surge in suspected cases. In contrast, hospital-based systems utilizing blockchain and smart contract technologies efficiently shared patient data, facilitating precise disease identification. Social media systems harnessed sentiment analysis for outbreak prediction, while statistical bureau systems integrated economic and population data for a novel perspective. Meteorological systems served as valuable complements, particularly for locally transmitted diseases. Wastewater monitoring systems added support by detecting crucial biological markers. CONCLUSION: This article conducts an in-depth analysis of infectious disease early warning systems, including systems based on various data sources. Future efforts should integrate new technologies, along with healthcare and social data, to enhance the capabilities of early warning and prediction.