Real-time prediction of severe influenza epidemics using Extreme Value Statistics

Each year, seasonal influenza epidemics cause hundreds of thousands of deaths worldwide and put high loads on health care systems. A main concern for resource planning is the risk of exceptionnally severe epidemics. Taking advantage of the weekly influenza cases reporting in France, we use recent results on multivariate GP models in Extreme Value Statistics to develop methods for real-time prediction of the risk that an ongoing epidemic will be exceptionally severe and for real-time detection of anomalous epidemics. Quality of predictions is assessed on observed and simulated data

Managing the costs of CO2 abatement in the cement industry

This article investigates how the costs associated with deep reductions in CO2 emissions from the cement industry will influence the costs across the entire value chain from cement production to the eventual end-use, in this case a residential building. The work is motivated by the substantial difference between the pricing of CO2 emissions and the costs of mitigation at the production sites of energy-intensive industries, such as cement manufacture. By examining how CO2 trading and investments in low-carbon kiln systems affect costs and prices further up the supply chain of cement our analysis provides new perspectives on the costs of industry abatement of CO2 and on the question of who could or should pay the price of such abatement. The analysis reveals that the cost impacts decrease substantially at each transformation stage, from limestone to final end-uses. The increase in total production costs for the residential building used as the case study in this work is limited to 1%, even in the cases where the cement price is assumed to be almost doubled. Policy relevance With the price of emission allowances under the EU Emissions Trading System (EU ETS) currently far below the levels required to unlock investments in low-CO2 production processes in carbon-intensive industry (i.e. petroleum refining, iron and steel production and cement manufacturing), this article seeks to pave the way for a discussion on complementary policy options. The results from this study, using the supply of cement and concrete to a residential building as a case study, suggest that because cement and concrete typically account for a limited proportion of the total cost of most construction and civil engineering projects, a policy scheme designed to allocate more of the costs of CO2 abatement to the end-users (of cement) would neither (significantly) alter the cost structure nor (dramatically) increase overall project costs

CO2 emissions abatement in the Nordic carbon-intensive industry – an end-game in sight?

Analysing different future trajectories of technological developments we assess the prospects for Nordic carbon-intensive industries to significantly reduce direct CO2 emissions in the period 2010–2050. This analysis covers petroleum refining, integrated iron and steel production, and cement manufacturing in the four largest Nordic countries of Denmark, Finland, Norway, and Sweden. Our results show that the implementation of currently available abatement measures will not be enough to meet the ambitious emissions reduction targets envisaged for the Year 2050. We show how an extensive deployment of CCS could result in emissions reductions that are in line with such targets. However, large-scale introduction of CCS would come at a significant price in terms of energy use and the associated flows of captured CO2 would place high requirements on timely planning of infrastructure for the transportation and storage of CO2. Further the assessment highlights the importance of, especially in the absence of successful deployment of CO2 capture, encouraging increased use of biomass in the cement and integrated iron and steel industries, and of promoting the utilisation of alternative raw materials in cement manufacturing to complement efforts to improve energy efficiency

Predicting extremes: influenza epidemics in France

Influenza epidemics each year cause hundreds of thousands of deaths worldwide and put high loads on health care systems, in France and elsewhere. A main concern for resource planning in public health is the risk of an extreme and dangerous epidemic. Sizes of epidemics are measured by the number of visits to doctors caused by Influenza Like Illness (ILI), and health care planning relies on prediction of ILI rates. We use recent results on the multivariate Generalized Pareto (GP) distributions in Extreme Value Statistics to develop methods for real-time prediction of risks of exceeding very high levels and for detection of unusual and potentially very dangerous epidemics. Based on the observation of the two first weeks of the epidemic, the GP method for real-time prediction is employed to predict ILI rates of the third week and the total size of the epidemic for extreme influenza epidemics in France. We then apply a general anomaly detection framework to the ILI rates during the three first weeks of the epidemic for early detection of unusual extreme epidemics. As an additional input to resource planning we use standard methods from extreme value statistics to estimate risk of exceedance of high ILI levels in future years. The new methods are expected to be broadly applicable in health care planning and in many other areas of science and technology

Error distributions for random grid approximations of multidimensional stochastic integrals

This paper proves joint convergence of the approximation error for several stochastic integrals with respect to local Brownian semimartingales, for nonequidistant and random grids. The conditions needed for convergence are that the Lebesgue integrals of the integrands tend uniformly to zero and that the squared variation and covariation processes converge. The paper also provides tools which simplify checking these conditions and which extend the range for the results. These results are used to prove an explicit limit theorem for random grid approximations of integrals based on solutions of multidimensional SDEs, and to find ways to "design" and optimize the distribution of the approximation error. As examples we briefly discuss strategies for discrete option hedging.Comment: Published in at http://dx.doi.org/10.1214/12-AAP858 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Prospects for CO2 capture in European industry

Purpose – The aim of this study is to assess the role of CO2 capture and storage (CCS) technologies in the reduction of CO2 emissions from European industries. Design/methodology/approach – A database covering all industrial installations included in the EU ETS has been created. Potential capture sources have been identified and the potential for CO2 capture has been estimated based on branch- and plant-specific conditions. Emphasis is placed here on three branches of industry with promising prospects for CCS: mineral oil refineries, iron and steel, and cement manufacturers. Findings – A relatively small number (~270) of large installations (>500,000?tCO2/year) dominates emissions from the three branches investigated in this study. Together these installations emit 432?MtCO2/year, 8 percent of EU's total greenhouse gas emissions. If the full potential of emerging CO2 capture technologies was realized, some 270-330?MtCO2 emissions could be avoided annually. Further, several regions have been singled out as particularly suitable to facilitate integrated CO2 transport networks. The most promising prospects for an early deployment of CCS are found in the regions bordering the North Sea. Research limitations/implications – Replacement/retrofitting of the existing plant stock will involve large investments and deployment will take time. It is thus important to consider how the current industry structure influences the potential to reduce CO2 in the short- medium and long term. It is concluded that the age structure of the existing industry plant stock and its implications for the timing and deployment rate of CO2 capture and other mitigation measures are important and should therefore be further investigated. Practical implications – CCS has been recognized as a key option for reducing CO2 emissions within the EU. This assessment shows that considerable emission reductions could be achieved by targeting large point sources in some of the most emission-intensive industries. Yet, a number of challenges need to be resolved in all parts of the CCS chain. Efforts need to be intensified from all stakeholders to gain more experience with the technological, economical and social aspects of CCS. Originality/value – This study provides a first estimate of the potential role for CO2 capture technologies in lowering CO2 emissions from European heavy industry. By considering wider system aspects as well as plant-specific conditions the assessment made in this study gives a realistic overview of the prospects and practical limitations of CCS in EU industry