Objective: In order to analyze the characteristics of the outbreak of acute respiratory tract infection in children caused by respiratory syncytial virus(RSV). Methods: The field epidemiological investigations were conducted for the two outbreaks in kindergartens in Hangzhou. Data were analyzed by descriptive method. Samples with positive respiratory syncytial virus nucleic acid were sequenced using PCR. Results: The two outbreaks occurred in kindergartens. There were 21 cases in kindergarten A, lasting 11 days, and 43 cases in kindergarten B, lasting 33 days. The epidemic curve showed a proliferation pattern. The cases were concentrated in nurseries and K1 classes, primarily among children aged 2-4 years. The most common symptoms were fever and cough, mainly upper respiratory tract infection, and no severe cases were found. Upper respiratory tract samples were collected and detected as positive for RSV. Four samples were sequenced and identified as subgroup B. Conclusion During the outbreak of acute respiratory infection in kindergartens, respiratory syncytial virus should be given primary consideration in the process of identification of the outbreak caused by other respiratory infections, and strictly control measures should be taken to reduce the long term impact of the epidemic.

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan′s health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan′s health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.