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Dengue imposes a heavy economic burden on families and society. We used surveillance data reported in 2019 to characterize the dengue epidemic in Zhejiang Province, China, which provided guidance for dengue prevention and control. Dengue epidemics mostly occurred in July to October. People aged 30–44 years, males, and commercial service workers were more likely to suffer from dengue. The epidemic areas were mainly in Hangzhou and Wenzhou. Meanwhile, we assessed the economic cost of dengue in the province from both family and organizational perspectives. The direct economic burden of dengue patients was estimated to be USD 405,038.25, and the indirect economic burden was USD 140,364.90, for a total economic burden of USD 543,213.00. The direct economic burden of dengue patients should be reduced by increasing the coverage and reimbursement of health insurance. Additionally, the total annual cost of dengue prevention and control for the government and organizational sectors was estimated to be USD 7075,654.83. Quantifying the dengue burden is critical for developing disease control strategies, allocating public health resources, and setting health policy priorities. [ABSTRACT FROM AUTHOR]

The link dynamic vehicle count is a spatial variable that measures the traffic state of road sections, which reflects the actual traffic demand. This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. There are four steps in this hybrid approach. First, the ICEEMDAN is employed to decompose the link dynamic vehicle count time series data into several intrinsic components. Second, the components are predicted by the GRU model. At the same time, the Bayesian optimization method is utilized to automatically optimize the hyperparameters of the GRU model. Finally, the predicted subcomponents are reconstructed to obtain the final prediction results. The proposed hybrid deep learning method is tested on two roads of Hangzhou, China. Results show that, compared with the 12 benchmark models, the proposed hybrid deep learning model achieves the best performance in link dynamic vehicle count forecasting. [ABSTRACT FROM AUTHOR]

Monitoring traffic states of urban road networks is essential to relieving traffic burdens and designing traffic management strategies. However, most traffic state estimation methods are data-driven and barely consider the traffic network topology characteristics. In this paper, we propose a model-driven approach to partition the network into several traffic clusters, providing a macro-perspective for traffic state monitoring and potential support for perimeter controls. Traffic state and network topology information are both considered in the approach. We obtain the traffic state index of each segment with license plate recognition data and traffic network topology information, considering the heterogeneity and direction of segments. The road graph is constructed based on graph theory, and we modify the RatioCut algorithm and turn hyper-parameters automatically to identify traffic clusters in the road graph. We verified the proposed approach by a large-scale urban network in Hangzhou, China. The visualization and comparison results with conventional spectral clustering, DBSCAN, and K-means demonstrate the superiority and application value of the proposed approach. • Present an accurate road graph construction framework considering the segment's hierarchy and topology. • Give out a new similarity function by optimizing the similarity between different states segments. • Propose an applicable traffic cluster identification approach with the integration of network topology and graph theory. [ABSTRACT FROM AUTHOR]

Objective To explore the mediating role of medical staff's autonomous motivation in the association between job requirements and job performance and to provide countermeasures and suggestions for improving medical staff's job performance. Methods By the convenience sampling method, 901 medical staff from 6 hospitals in Hangzhou were surveyed with the job demand scale, the autonomous motivation scale, and the job performance scale. The structural equation model was used to analyze the direct and indirect effects of job demand and autonomous motivation on job performance. Results Finally, 813 valid questionnaires were collected, with an effective recovery rate of 90.2%. In terms of job performance, the married medical staff scored higher, while those without a professional title or with working time less than 5 years scored lower (P < 0.05). The job requirements had a direct impact on job performance (β = 0.30, P < 0.01), which was indirectly affected through autonomous motivation (β = 0.069, P < 0.01), and the indirect effect percentage was 18.70%. Conclusions Autonomous motivation had a positive mediating effect on the association between job demands and job performance. Hospital managers should pay more attention to the working feelings of medical staff and give targeted guidance to medical staff with different levels of motivation to improve their autonomous motivation and, furthermore, to improve their work performance. [ABSTRACT FROM AUTHOR]