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Register a new userBayesian networks for enterprise risk assessment
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According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. In general risk is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover qualitative data must be converted in numerical values to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Network is a useful tool to integrate different information and in particular to study the risks joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a Bayesian networks in the particular case in which only prior probabilities of node states and marginal correlations between nodes are available, and when the variables have only two states.
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The spread of an infection on a real-world social network is determined by the interplay of two processes: the dynamics of the network, whose structure changes over time according to the encounters between individuals, and the dynamics on the network, whose nodes can infect each other after an encounter. Physical encounter is the most common vehicle for the spread of infectious diseases, but detailed information about encounters is often unavailable because expensive, unpractical to collect or privacy sensitive. We asks whether the friendship ties between the individuals in a social network successfully predict who is at risk. Using a dataset from a popular online review service, we build a time-varying network that is a proxy of physical encounter between users and a static network based on reported friendship. Through computer simulations, we compare infection processes on the resulting networks and show that, whereas distance on the friendship network is correlated to epidemic risk, friendship provides a poor identification of the individuals at risk if the infection is driven by physical encounter. Such limit is not due to the randomness of the infection, but to the structural differences of the two networks. In contrast to the macroscopic similarity between processes spreading on different networks, the differences in local connectivity determined by the two definitions of edges result in striking differences between the dynamics at a microscopic level. Despite the limits highlighted, we show that periodical and relatively infrequent monitoring of the real infection on the encounter network allows to correct the predicted infection on the friendship network and to achieve satisfactory prediction accuracy. In addition, the friendship network contains valuable information to effectively contain epidemic outbreaks when a limited budget is available for immunization.
An asteroid impact is a low probability event with potentially devastating consequences. The Asteroid Risk Mitigation Optimization and Research (ARMOR) software tool calculates whether a colliding asteroid experiences an airburst or surface impact and calculates effect severity as well as reach on the global map. To calculate the consequences of an impact in terms of loss of human life, new vulnerability models are derived that connect the severity of seven impact effects (strong winds, overpressure shockwave, thermal radiation, seismic shaking, ejecta deposition, cratering and tsunamis) with lethality to human populations. With the new vulnerability models ARMOR estimates casualties of an impact under consideration of the local population and geography. The presented algorithms and models are employed in two case studies to estimate total casualties as well as the damage contribution of each impact effect. The case studies highlight that aerothermal effects are most harmful except for deep water impacts, where tsunamis are the dominant hazard. Continental shelves serve a protective function against the tsunami hazard caused by impactors on the shelf. Furthermore, the calculation of impact consequences facilitates asteroid risk estimation to better characterize a given threat and the concept of risk as well as its applicability to the asteroid impact scenario are presented.
The quantitative analysis of viral transmission risk in public places such as schools, offices, university lecture halls, hospitals, museums, theaters or shopping malls makes it possible to identify the effective levers for a proactive policy of health security and to evaluate the reduction in transmission thus obtained. The contribution to the epidemic propagation of SARS-CoV-2 in such public spaces can be reduced in the short term to a level compatible with an epidemic decline, i.e. with an overall epidemic reproduction rate below one. Here, we revisit the quantitative assessment of indoor and outdoor transmission risk. We show that the long range aerosol transmission is controlled by the flow rate of fresh air and by the mask filtering quality, and is quantitatively related to the CO2 concentration, regardless the room volume and the number of people. The short range airborne transmission is investigated experimentally using dedicated dispersion experiments performed in two French shopping malls. Exhaled aerosols are dispersed by turbulent draughts in a cone, leading to a concentration inversely proportional to the squared distance and to the flow velocity. We show that the average infection dose, called the viral quantum, can be consistently determined from epidemiological and biological experimental data. Practical implications. The results provide a rational design of sanitary policies to prevent the dominant routes of viral transmission by reinforced ventilation, air purification, mechanical dispersion by fans and incentives for correct wearing of quality masks (surgical mask, possibly covered by a fabric mask, or non-medical FFP2 masks). Combined, such measures significantly reduce the airborne transmission risk of SARS-CoV-2, with a quantitative assessment.
Many complex networks exhibit vulnerability to spreading of epidemics, and such vulnerability relates to the viral strain as well as to the network characteristics. For instance, the structure of the network plays an important role in spreading of epidemics. Additionally, properties of previous epidemic models require prior knowledge of the complex network structure, which means the models are limited to only well-known network structures. In this paper, we propose a new epidemiological SIR model based on the continuous time Markov chain, which is generalized to any type of network. The new model is capable of evaluating the states of every individual in the network. Through mathematical analysis, we prove an epidemic threshold exists below which an epidemic does not propagate in the network. We also show that the new epidemic threshold is inversely proportional to the spectral radius of the network. In particular, we employ the new epidemic model as a novel measure to assess the vulnerability of networks to the spread of epidemics. The new measure considers all possible effective infection rates that an epidemic might possess. Next, we apply the measure to correlated networks to evaluate the vulnerability of disassortative and assortative scalefree networks. Ultimately, we verify the accuracy of the theoretical epidemic threshold through extensive numerical simulations. Within the set of tested networks, the numerical results show that disassortative scale-free networks are more vulnerable to spreading of epidemics than assortative scale-free networks.
In order to model volatile real-world network behavior, we analyze phase-flipping dynamical scale-free network in which nodes and links fail and recover. We investigate how stochasticity in a parameter governing the recovery process affects phase-flipping dynamics, and find the probability that no more than q% of nodes and links fail. We derive higher moments of the fractions of active nodes and active links, $f_n(t)$ and $f_{ell}(t)$, and define two estimators to quantify the level of risk in a network. We find hysteresis in the correlations of $f_n(t)$ due to failures at the node level, and derive conditional probabilities for phase-flipping in networks. We apply our model to economic and traffic networks.
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