Construction and Application Effect Evaluation of Infectious Disease Monitoring and Early Warning System Based on Big Data

In recent years, the frequent occurrence of infectious diseases has posed a serious challenge to global public health security, highlighting the importance of building an efficient infectious disease monitoring and early warning system. This study focuses on an infectious disease monitoring and early warning system based on big data technology, aiming to improve the accuracy and timeliness of early warning through comprehensive and real-time collection and analysis of epidemic data. By collecting and preprocessing multiple data sources in real-time, we utilized machine learning techniques to construct a warning model based on time series analysis and support vector machines, and proposed a new warning algorithm that comprehensively considers multiple dimensions and indicators of epidemic data. After the system implementation, it has been rigorously tested and verified, demonstrating good stability and scalability. In the application effectiveness evaluation stage, we established an evaluation index system that includes warning accuracy, timeliness, system stability, and user experience. Through comparative analysis of actual epidemic data and system warning data, the results showed that the system performed excellently in warning accuracy and timeliness, and the warning signals were highly consistent with actual epidemic data. In addition, the system also demonstrates advantages such as rich data sources and intelligent push of warning signals. Compared with traditional monitoring and warning methods, it can more effectively detect epidemic risks, improve warning efficiency and convenience.