Prioritizing Emerging Zoonoses in The Netherlands

Background: To support the development of early warning and surveillance systems of emerging zoonoses, we present a general method to prioritize pathogens using a quantitative, stochastic multi-criteria model, parameterized for the Netherlands. Methodology/Principal Findings: A risk score was based on seven criteria, reflecting assessments of the epidemiology and impact of these pathogens on society. Criteria were weighed, based on the preferences of a panel of judges with a background in infectious disease control. Conclusions/Significance: Pathogens with the highest risk for the Netherlands included pathogens in the livestock reservoir with a high actual human disease burden (e.g. Campylobacter spp., Toxoplasma gondii, Coxiella burnetii) or a low current but higher historic burden (e.g. Mycobacterium bovis), rare zoonotic pathogens in domestic animals with severe disease manifestations in humans (e.g. BSE prion, Capnocytophaga canimorsus) as well as arthropod-borne and wildlife associated pathogens which may pose a severe risk in future (e.g. Japanese encephalitis virus and West-Nile virus). These agents are key targets for development of early warning and surveillance.Infrastructures, Systems and ServicesTechnology, Policy and Managemen

The Eco-Costs of Material Scarcity, a Resource Indicator for LCA, Derived from a Statistical Analysis on Excessive Price Peaks

The availability of resources is crucial for the socio-economic stability of our society. For more than two decades, there was a debate on how to structure this issue within the context of life-Cycle assessment (LCA). The classical approach with LCA is to describe “scarcity” for future generations (100−1000 years) in terms of absolute depletion. The problem, however, is that the long-term availability is simply not known (within a factor of 100−1000). Outside the LCA community, the short-term supply risks (10−30 years) were predicted, resulting in the list of critical raw materials (CRM) of the European Union (EU), and the British risk list. The methodology used, however, cannot easily be transposed and applied into LCA calculations. This paper presents a new approach to the issue of short-term material supply shortages, based on subsequent sudden price jumps, which can lead to socio-economic instability. The basic approach is that each resource is characterized by its own specific supply chain with its specific price volatility. The eco-costs of material scarcity are derived from the so-called value at risk (VAR), a well-known statistical risk indicator in the financial world. This paper provides a list of indicators for 42 metals. An advantage of the system is that it is directly related to business risks, and is relatively easy to understand. A disadvantage is that “statistics of the past” might not be replicated in the future (e.g., when changing from structural oversupply to overdemand, or vice versa, which appeared an issue for two companion metals over the last 30 years). Further research is recommended to improve the statistics

Regular vines : generation algorithm and number of equivalence classes

This article explores the topic of generation algorithm and number of equivalence classe

Modeling railway disruption lengths with Copula Bayesian Networks

Decreasing the uncertainty in the lengths of railway disruptions is a major help to disruption management. To assist the Dutch Operational Control Center Rail (OCCR) during disruptions, we propose the Copula Bayesian Network method to construct a disruption length prediction model. Computational efficiency and fast inference features make the method attractive for the OCCR’s real-time decision making environment. The method considers the factors influencing the length of a disruption and models the dependence between them to produce a prediction. As an illustration, a model for track circuit (TC) disruptions in the Dutch railway network is presented in this paper. Factors influencing the TC disruption length are considered and a disruption length model is constructed. We show that the resulting model’s prediction power is sound and discuss its real-life use and challenges to be tackled in practice

Probabilistic inversion in priority setting of emerging zoonoses

This article presents methodology of applying probabilistic inversion in combination with expert judgment in priority setting problem. Experts rank scenarios according to severity. A linear multi-criteria analysis model underlying the expert preferences is posited. Using probabilistic inversion, a distribution over attribute weights is found that optimally reproduces the expert rankings. This model is validated in three ways. First, consistency of expert rankings is checked, second, a complete model fitted using all expert data is found to adequately reproduce observed expert rankings, and third, the model is fitted to subsets of the expert data and used to predict rankings in out-of-sample expert data

Mining and visualising ordinal data with non-parametric continuous BBNs

Data mining is the process of extracting and analysing information from large databases. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Belief Net (BBN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of conditional independence statements in the form of a directed acyclic graph. Specifying the structure of the model is one of the most important design choices in graphical modelling. Notwithstanding their potential, there are only a limited number of applications of graphical models on very complex and large databases. A method for mining ordinal multivariate data using non-parametric BBNs is presented. The main advantage of this method is that it can handle a large number of continuous variables, without making any assumptions about their marginal distributions, in a very fast manner. Once the BBN is learned from data, it can be further used for prediction. This approach allows for rapid conditionalisation, which is a very important feature of a BBN from a user's standpoint. © 2008 Elsevier B.V. All rights reserved

Development of a Causal Model for Air Transport Safety

The development of the Dutch international airport Schiphol has been the subject of fierce political debate for several decades. One of the considerations has been the safety of the population living around the airport. In the debate about the acceptability of the risks associated with the air traffic above The Netherlands the need has arisen to gain a more thorough understanding of the accident genesis in air traffic, with the ultimate aim of improving the safety situation in air traffic in general and around Schiphol in particular. To this end a research effort has been started in order to develop causal models for air traffic risks in the expectation that these will ultimately give the necessary insights. The first step is to organise the information available in accident databases.Copyright © 2005 by ASM