Epidemic risk has great uncertainty and harmfulness, which poses a potential threat to public health in a certain region. Establishing a special risk assessment system to assess and predict the potential epidemic risk of a region can effectively avoid or reduce the impact of epidemic risk. Therefore, this paper combs the related factors that affect the epidemic risk, and proposes an epidemic risk assessment model based on 12 indicators by combining Markov chain and AHP. The model can assess the epidemic situation in a certain region from four aspects: the probability of risk occurrence, the probability of loss, the possibility of risk disappearance and risk duration, so as to provide detailed data for the risk management and control of epidemic in the region, and help the epidemic prevention work to be carried out in a targeted way. Finally, the case analysis and method comparison are carried out,and the results show that the model proposed in this paper is reasonable and feasible.
The massive cloud service market is full of various services with uneven quality. Even the services that have passed the platform detection will have unknown trustworthiness problems in the actual use process. The risk environment of the cloud service determines that its trustworthiness is random. The static trustworthiness assessment results can only reflect the cloud service trustworthiness at a certain time, not enough to reflect the real trustworthiness of the cloud service. To objectively reflect the trustworthiness of the cloud service, it is necessary to further assess the cloud service trustworthiness state and its changes on the basis of trustworthiness level measurement. To solve this problem, this paper combs the trustworthiness indicators of the cloud service, puts forward an effective assessment method of cloud service trustworthiness level based on D-S theory, and puts forward the representation method of cloud service trustworthiness state and its transition state combined with Markov chain, so as to realize the effective assess of cloud service trustworthiness state and its changes. Finally, through case analysis, it shows that the method proposed in this paper is feasible, can effectively assess the cloud service trustworthiness state and its changes, and provide users with detailed assessment results, so as to help users make reasonable service selection and trustworthiness management. This research has important research significance for ensuring the trustworthiness of the cloud service and improving the security of cloud service market.
In-host mutation of a cross-species infectious disease to a form that is transmissible between humans has resulted with devastating global pandemics in the past. We use simple mathematical models to describe this process with the aim to better understand the emergence of an epidemic resulting from such a mutation and the extent of measures that are needed to control it. The feared outbreak of a human-human transmissible form of avian influenza leading to a global epidemic is the paradigm for this study. We extend the SIR approach to derive a deterministic and a stochastic formulation to describe the evolution of two classes of susceptible and infected states and a removed state, leading to a system of ordinary differential equations and a stochastic equivalent based on a Markov process. For the deterministic model, the contrasting timescale of the mutation process and disease infectiousness is exploited in two limits using asymptotic analysis in order to determine, in terms of the model parameters, necessary conditions for an epidemic to take place and timescales for the onset of the epidemic, the size and duration of the epidemic and the maximum level of the infected individuals at one time. Furthermore, the basic reproduction number R is determined from asymptotic analysis of a distinguished limit. Comparisons between the deterministic and stochastic model demonstrate that stochasticity has little effect on most aspects of an epidemic, but does have significant impact on its onset particularly for smaller populations and lower mutation rates for representatively large populations. The deterministic model is extended to investigate a range of quarantine and vaccination programmes, whereby in the two asymptotic limits analysed, quantitative estimates on the outcomes and effectiveness of these control measures are established.
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