Background With the rapid development of the food delivery industry, take-away deliverymen become an essential component of urban logistics. However, high labor intensity, unstable income, and extended working hours place them at considerable risk of occupational burnout. Available studies have paid insufficient attention to the mental health of this population, and effective predictive or preventive approaches remain limited. Objective To understand the status of occupational burnout among take-away deliverymen, identify influencing factors based on restricted cubic spline analysis, and develop a predictive model to provide a theoretical basis for improving their mental health. Methods A cross-sectional survey was conducted among full-time take-away deliverymen registered to the "Ele.me" and "Meituan" platforms in Hangzhou between September 1 and November 30, 2024, using both online and offline approaches. A questionnaire covered sociodemographic, household, and occupational information, and the Maslach Burnout Inventory–General Survey were used in this survey. Univariate analyses and logistic regression were used to identify factors associated with burnout and to construct a predictive model. Model performance was evaluated using receiver operating characteristic (ROC) curve and calibration curve. Furthermore, restricted cubic spline was used to further explore the relationship between age, working hours, and occupational burnout. Results Among the 1192 participants, 735 (61.66%) experienced occupational burnout. The logistic regression model indicated that older age (OR=1.021, P=0.010), educational attainment of college level or above compared to junior high school or below, (OR=2.837, P<0.001), and longer daily working hours (OR=1.081, P=0.001) were positively associated with occupational burnout. In contrast, work experience of 3−5 years (OR=0.666, P=0.016) and more than 5 years (OR=0.382, P<0.001) compared to those with <3 years, as well as providing three social insurances (OR=0.655, P=0.006) or five social insurances (OR=0.478, P<0.001) compared to those without any social insurances coverage, were negatively associated with occupational burnout. The predictive model based on these variables achieved an area under the curve (AUC) of 0.783 (95% CI: 0.756~0.809), and good agreement between calibration curves. The restricted cubic spline analysis revealed that the risk of occupational burnout was highest among delivery workers aged 33−46 years. Those with daily working hours between 3.5 and 8.1 h had a lower risk, while the risk significantly increased when working hours exceeded 8.1 h per day. Conclusion The prevalence of reporting occupational burnout among take-away deliverymen in Hangzhou is high. The developed predictive model provides valuable insights for the prevention and control of burnout in this population. Measures such as optimizing social security systems, rational work schedule allocation, and enhancing psychological support may help reduce occupational burnout among take-away deliverymen.

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.

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.