The state of veterinary epidemiology and economics

Recent developments in the fields of veterinary epidemiology and economics are critically reviewed and assessed. The impacts of recent technological developments in diagnosis, genetic characterisation, data processing and statistical analysis are evaluated. It is concluded that the acquisition and availability of data remains the principal constraint to the application of available techniques in veterinary epidemiology and economics, especially at population level. As more commercial producers use computerised management systems, the availability of data for analysis within herds is improving. However, consistency of recording and diagnosis remains problematic. Recent trends to the development of national livestock databases intended to provide reassurance to consumers of the safety and traceability of livestock products are potentially valuable sources of data that could lead to much more effective application of veterinary epidemiology and economics. These opportunities will be greatly enhanced if data from different sources, such as movement recording, official animal health programmes, quality assurance schemes, production recording and breed societies can be integrated. However, in order to realise such integrated databases, it will be necessary to provide absolute control of user access to guarantee data security and confidentiality. The potential applications of integrated livestock databases in analysis, modelling, decision-support, and providing management information for veterinary services and livestock producers are discussed. (c) 2004 Elsevier B.V. All rights reserved.

The business case development process in the delivery of NHS infrastructure

There is a lack of knowledge base in relation to experiences gained and lessons learnt from previously executed National Health Service (NHS) infrastructure projects in the UK. This is in part a feature of one-off construction projects, which typify healthcare infrastructure, and in part due to the absence of a suitable method for conveying such information. The complexity of infrastructure delivery process in the NHS makes the construction of healthcare buildings a formidable task. This is particularly the case for the NHS trusts who have little or no experience of construction projects. To facilitate understanding a most important aspect of the delivery process, which is the preparation of a capital investment proposal; steps taken in developing the business case for an NHS healthcare facility are examined. The context for such examination is provided by the planning process of a healthcare project, studied retrospectively. The process is analysed using a social science based method called ‘building stories’, developed at the University of California-Berkeley. By applying this method, stories or narratives are constructed around the data captured on the case study. The findings indicate that the business case process may be used to justify, rather than identify, trusts’ requirements. The study is useful for UK public sector clients as well as consultants and professionals who aim to participate in the delivery of healthcare infrastructure projects in the UK.

Evaluation of Earth system models using modern and palaeo-observations: the state-of-the-art

Earth system models are increasing in complexity and incorporating more processes than their predecessors, making them important tools for studying the global carbon cycle. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes, with coupled climate-carbon cycle models that represent land-use change simulating total land carbon stores by 2100 that vary by as much as 600 Pg C given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous model evaluation methodologies. Here we assess the state-of-the-art with respect to evaluation of Earth system models, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeo data and (ii) metrics for evaluation, and discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute towards the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but it is also a challenge, as more knowledge about data uncertainties is required in order to determine robust evaluation methodologies that move the field of ESM evaluation from "beauty contest" toward the development of useful constraints on model behaviour.

Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art

Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.