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Introduction Medical alliance refers to a group of medical institutions or joint organizations formed by the integration of horizontal or vertical medical resources in different regions and types of medical institutions within a certain region, the medical institutions are mutually beneficial and responsible communities [1]. According to the closeness of the contact, the medical alliance can be divided into three types: compact, semicompact and loose [1]. A compact medical alliance is an operational management mode that forms a community of responsibilities and interests among various health service organizations within a medical alliance, and all people and property implement unified operational management; The semi-compact medical alliance refers to an operational management mode formed by the contract or agreement from the core hospital to the primary health center on the basis that the nature of the assets of various medical service institutions does not change in the medical alliance; Loose medical alliance means that all kinds of health service organizations in the medical alliance only cooperate in medical technology, personnel training, equipment and other aspects. However, various medical and health institutions within the medical alliance are not affiliated. The medical alliance is an important carrier of hierarchical medical system. Its establishment is an effective exploration and practice mode to optimize the allocation of medical resources, improve the service level of primary medical institutions, control the cost of medical and pharmaceutical services, improve the patient experience, and comprehensively improve the health management level and chronic disease management level [2, 3]. The structure, process, and outcome of medical services involve multiple subjects such as patients, doctors, hospitals, government, and society. This places high demands on the allocation of medical resources. This is a comprehensive governance process. However, as the medical service environment becomes more and more complex, the traditional medical service organization mode can no longer meet the requirements of modern medical services for efficiency and social responsibility [4]. Due to the increase in medical risks and the pressure on the medical service market, integrated delivery networks INTEGRATED CARE CASE

With the expansion of the global novel coronavirus disease (COVID-19) pandemic, unprecedented interventions have been widely implemented in many countries, including China. In view of this scenario, this research aims to explore the effectiveness of population mobility restriction in alleviating epidemic transmission during different stages of the outbreak. Taking Shenzhen, a city with a large immigrant population in China, as a case study, the real-time reproduction number of COVID-19 is estimated by statistical methods to represent the dynamic spatiotemporal transmission pattern of COVID-19. Furthermore, migration data between Shenzhen and other provinces are collected to investigate the impact of nationwide population flow on near-real-time dynamic reproductive numbers. The results show that traffic flow control between populated cities has an inhibitory effect on urban transmission, but this effect is not significant in the late stage of the epidemic spread in China. This finding implies that the government should limit international and domestic population movement starting from the very early stage of the outbreak. This work confirms the effectiveness of travel restriction measures in the face of COVID-19 in China and provides new insight for densely populated cities in imposing intervention measures at various stages of the transmission cycle.

The scarcity of medical resources is a fundamental problem worldwide; the development of information technology and the Internet has given birth to online health care, which has alleviated the above problem. The survival and sustainable development of the online health community requires users to continuously disclose their health and privacy. Therefore, it is a great practical significance to find out the factors and mechanisms that promote users' self-disclosure in the online health community. From the perspective of individual and situation interaction, this study constructed influencing factors model of health privacy information self-disclosure. Finally, we collected 264 valid samples from the online health community through online and offline questionnaire surveys and then use the SPSS20.0 and AMOS21.0 to conduct exploratory factor analysis, confirmatory factor analysis, scale reliability and validity analysis, and structural equation model analysis. The main findings are as follows: trust in websites and trust in doctors reduce the privacy concern. The privacy trade-off will not occur when trust is enough to offset the privacy concerns caused by personalized services, reciprocity norms, and other factors. Second, reciprocity norms are inevitably compulsive, which will increase privacy concerns. However, based on voluntariness, reciprocity norms can enhance user trust. Third, service quality caused by personalized services not only enhance the social rewards of users but also eliminate the privacy concern. Fourth, users' health privacy attention and information sensitivity are too high to decrease the influence of user' privacy concerns on personal health privacy information disclosure. The conclusions of this paper will help us to supplement privacy calculus theory and the application scope of the attention-based view. The proposed strategy of this article can be used to stimulate the information contribution behavior of users and improve the medical service capabilities in online health community.

Abstract. Accomplishing the regional equalization of basic public service supply among the provinces in China is an important objective that can promote the people's livelihood construction. In order to measure the problem which is about the non-equalization of basic public service supply, this paper takes these aspects as the first index, such as the infrastructure construction, basic education services, public employment services, public health service and social security service. At the same time, it cooperates with 16 index as the second index to construct the performance evaluation systems, and then use the Theil index to evaluate the performance in provinces that using the panel data from the year 2000 to 2012.