Purpose: This paper aims to clarify the complex path of negative externalities in the sharing economy and proposes corresponding policy recommendations. Design/methodology/approach: This paper aims to establish an analytical framework for the negative externalities of the sharing economy and to extract the main factors that produce negative externalities, and then, through qualitative comparative analysis method find out how these factors interact to form a negative externality. Findings: Negative externalities in the sharing economy come from the joint effect of the sharing degree of the product or service and constraint mechanism, and the current main modes of the shared economy increase the possibility of negative externalities. Originality/value: The paper proposes a complex path resulting from negative externalities in the shared economy.

Syphilis, a sexually transmitted infectious disease caused by the bacterium treponema pallidum, has re-emerged as a global public health issue with an estimated 12 million people infected each year. Understanding the impacts of health awareness and behaviors on transmission dynamics of syphilis can help to establish optimal control strategy in different regions. In this paper, we develop a multiple-stage SIRS epidemic model taking into account the public health awareness and behaviors of syphilis. First, the basic reproduction number ${\mathcal{R}}_0$ is obtained, which determines the global dynamics behaviors of the model. We derive the necessary conditions for implementing optimal control and the corresponding optimal solution for mitigation syphilis by using Pontryagin's Maximum Principle. Based on the data of syphilis in Ningxia from 2006 to 2018, the parameterizations and model calibration are carried out. The fitting results are in good agreement with the data. Moreover, sensitivity analysis shows that the public awareness induced protective behaviors C_e, compliance of condom-induced preventability ε and treatment for the primary syphilis m₁ play an important role in mitigating the risk of syphilis outbreaks. These results can help us gain insights into the epidemiology of syphilis and provide guidance for the public health authorities to implement health education programs.

Syphilis remains a growing and resurging infectious disease in China. However, exploring the influence of environmental factors on the spatiotemporal distribution of syphilis remains under explore. This study aims to analyze the spatiotemporal distribution characteristics of syphilis in Ningxia, Northwest China, and its potential environmental influencing factors. Based on the standardized incidence ratio of syphilis for 22 administrative areas in Ningxia from 2004 to 2017, spatiotemporal autocorrelation and scan analyses were employed to analyze the spatial and temporal distribution characteristics of syphilis incidence, while a fixed-effect spatial panel regression model identified the potential factors affecting syphilis incidence. Syphilis incidence increased from 3.78/100,000 in 2004 to 54.69/100,000 in 2017 with significant spatial clustering in 2007 and 2009–2013. The “high–high” and “low–low” clusters were mainly distributed in northern and southern Ningxia, respectively. The spatial error panel model demonstrated that the syphilis incidence may be positively correlated with the per capita GDP and tertiary industry GDP and negatively correlated with the number of health facilities and healthcare personnel. Sex ratio and meteorological factors were not significantly associated with syphilis incidence. These results show that the syphilis incidence in Ningxia is still increasing and has significant spatial distribution differences and clustering. Socio-economic and health-resource factors could affect the incidence; therefore, strengthening syphilis surveillance of migrants in the economically developed region and allocating health resources to economically underdeveloped areas may effectively help prevent and control syphilis outbreaks in high-risk cluster areas of Ningxia.

This study adopts the textual network to describe the coordination among the interplay of words, where nodes represent words and nodes are connected if the corresponding words have co-occurrence pattern across documents. To study stock movements, we further proposed the sparse laplacian shrinkage logistic model (SLS_L) which can properly take into account the network connectivity structure. By using this approach, we investigated the relationship between Shenwan index and analysts' research reports. The securities analysts’ research reports are crawled by a famous financial website in China: EastMoney, and are then parsed into time-series textual data. The empirical results show that the proposed SLS_L model outperforms alternatives including Lasso-Logistics (L_L) and MCP-Logistic (MCP_L) models by having better prediction performance. Besides, we search published literature and find the identified keywords with more lucid interpretations. Our study unveils some interesting findings that the efficient use of textual network is important to improve the predictive power as well as the semantic interpretability in stock market analysis.

Population demography can change the network structure, which further plays an important role in the spreading of infectious disease. In this paper, we study the epidemic dynamics in temporal clustered networks where the local-world structure and clustering are incorporated into the attachment mechanism of new nodes. It is found that increasing the local-world size of new nodes has little influence on the clustering coefficient but increases the degree heterogeneity of networks. Besides, when the network evolves faster, increasing the local-world size of new nodes leads to a faster initial growth rate and a larger steady density of infectious nodes, while it has small impacts on the steady density of infectious disease when the network evolves slowly. Furthermore, if the average degree is fixed, increasing the probability of triad formation enlarges the clustering coefficient of a network, which reduces the initial growth rate and steady density of infectious nodes in the network. This work could provide a theoretical foundation for the control of infectious disease.