Effects of heterogeneous self-protection awareness on resource-epidemic coevolution dynamics

A coupled awareness-epidemic model on a multi-layer time-varying network

Social interactions have become more complicated and changeable under the influence of information technology revolution. We, thereby, propose a multi-layer activity-driven network with attractiveness considering the heterogeneity of activated individual edge numbers, which aims to explore the role of heterogeneous behaviors in the time-varying network. Specifically, three types of individual behaviors are introduced: (i) self-quarantine of infected individuals, (ii) safe social distancing between infected and susceptible individuals, and (iii) information spreading of aware individuals. Epidemic threshold is theoretically derived in terms of the microscopic Markov chain approach and the mean-field approach. The results demonstrate that performing self-quarantine and maintaining safe social distance can effectively raise the epidemic threshold and suppress the spread of diseases. Interestingly, individuals' activity and individuals' attractiveness have an equivalent effect on epidemic threshold under the same condition. In addition, a similar result can be obtained regardless of the activated individual edge numbers. The epidemic outbreak earlier in a situation of the stronger heterogeneity of activated individual edge numbers.

Effect of local and global information on the dynamical interplay between awareness and epidemic transmission in multiplex networks

Recent few years have witnessed a growing interest in exploring the dynamical interplay between awareness and epidemic transmission within the framework of multiplex networks. However, both local and global information have significant impacts on individual awareness and behavior, which have not been adequately characterized in the existing works. To this end, we propose a local and global information controlled spreading model to explore the dynamics of two spreading processes. In the upper layer, we construct a threshold model to describe the awareness diffusion process and introduce local and global awareness information as variables into an individual awareness ratio. In the lower layer, we adopt the classical susceptible-infected-susceptible model to represent the epidemic propagation process and introduce local and global epidemic information into individual precaution degree to reflect individual heterogeneity. Using the microscopic Markov chain approach, we theoretically derive the threshold for epidemic outbreaks. Our findings suggest that the local and global information can motivate individuals to increase self-protection awareness and take more precaution measures, thereby reducing disease infection probability and suppressing the spread of epidemics.

The impact of the global and local awareness diffusion on epidemic transmission considering the heterogeneity of individual influences

In this paper, we propose a coupled awareness-epidemic spreading model considering the heterogeneity of individual influences, which aims to explore the interaction between awareness diffusion and epidemic transmission. The considered heterogeneities of individual influences are threefold: the heterogeneity of individual influences in the information layer, the heterogeneity of individual influences in the epidemic layer and the heterogeneity of individual behavioral responses to epidemics. In addition, the individuals' receptive preference for information and the impacts of individuals' perceived local awareness ratio and individuals' perceived epidemic severity on self-protective behavior are included. The epidemic threshold is theoretically established by the microscopic Markov chain approach and the mean-field approach. Results indicate that the critical local and global awareness ratios have two-stage effects on the epidemic threshold. Besides, either the heterogeneity of individual influences in the information layer or the strength of individuals' responses to epidemics can influence the epidemic threshold with a nonlinear way. However, the heterogeneity of individual influences in the epidemic layer has few effect on the epidemic threshold, but can affects the magnitude of the final infected density.

Effects of the information-driven awareness on epidemic spreading on multiplex networks

In this study, we examine the impact of information-driven awareness on the spread of an epidemic from the perspective of resource allocation by comprehensively considering a series of realistic scenarios. A coupled awareness-resource-epidemic model on top of multiplex networks is proposed, and a Microscopic Markov Chain Approach is adopted to study the complex interplay among the processes. Through theoretical analysis, the infection density of the epidemic is predicted precisely, and an approximate epidemic threshold is derived. Combining both numerical calculations and extensive Monte Carlo simulations, the following conclusions are obtained. First, during a pandemic, the more active the resource support between individuals, the more effectively the disease can be controlled; that is, there is a smaller infection density and a larger epidemic threshold. Second, the disease can be better suppressed when individuals with small degrees are preferentially protected. In addition, there is a critical parameter of contact preference at which the effectiveness of disease control is the worst. Third, the inter-layer degree correlation has a "double-edged sword" effect on spreading dynamics. In other words, when there is a relatively lower infection rate, the epidemic threshold can be raised by increasing the positive correlation. By contrast, the infection density can be reduced by increasing the negative correlation. Finally, the infection density decreases when raising the relative weight of the global information, which indicates that global information about the epidemic state is more efficient for disease control than local information.

Impact of occupational exposure on job satisfaction and overall happiness among Chinese physicians and nurses: A cross-sectional study

AIMS

To investigate the impact of occupational exposure on job satisfaction and overall happiness, and to identify related factors of job satisfaction and overall happiness among physicians and nurses.

BACKGROUND

Occupational exposure against physicians and nurses has become one of the most serious public health issues worldwide.

METHODS

A cross-sectional study was conducted among physicians and nurses from 14 public tertiary hospitals using purposive sampling. Propensity score matching was used to compare job satisfaction and overall happiness among physicians and nurses with and without occupational exposure. Furthermore, binary logistic regression analysis was used to identify and analyze the influencing factors of job satisfaction and overall happiness.

RESULTS

A total of 2139 physicians and nurses (55.59%) from 3791 participants had experienced occupational exposure hazards. Before matching, the job satisfaction and overall happiness among the physicians and nurses was 38.54% and 42.14%, respectively. Participants who experienced occupational exposure were more likely to develop job dissatisfaction (OR = 1.08, 95%CI: 0.90-1.28) and overall unhappiness (OR = 1.24, 95%CI: 1.05- 1.46) than those who did not. Participants' work experience, self-evaluated health status, satisfaction with the work environment, evaluation of doctor-patient relationship, and stress were common factors affecting job satisfaction and overall happiness.

CONCLUSIONS

Our findings suggest that physicians and nurses who experience occupational exposure are more likely to develop job dissatisfaction and overall unhappiness, especially if they have shorter work experience and a tense or neutral relationship with patients.

IMPLICATIONS FOR NURSING MANAGEMENT

It is necessary to pay attention to the occupational exposure. When physicians and nurses experience occupational exposure, managers could provide support to prevent job dissatisfaction and unhappiness.