Innovative in silico approaches to address avian flu using grid technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target

Life course building epidemiology: An alternative approach to the collection and analysis of carbon emission data

Developing policy for the reduction of the carbon emissions due to buildings requires models for energy usage that incorporate social, behavioural, and environmental factors in addition to the physical properties and technical specifications of the buildings. Marked parallels exist with some of the more intractable public health issues, such as rising levels of obesity. Recently, health researchers have recognized the importance of taking a broader life-course approach to epidemiology in order to examine the degree that long-term health outcomes are set in early life and the extent that these may be mediated or mitigated by subsequent growth and development, as well as by intervention strategies. Life course epidemiology as applied in building science, where energy usage is treated as analogous to poor health outcomes, provides an alternative approach for the construction of causal models that allow for complex interactions between social and technical factors as well as long term effects. It can provide a useful framework for the successful management and analysis of longitudinal studies and may prove particularly effective in identifying the type, timing, and targeting of intervention strategies to produce optimal outcomes in terms of absolute reductions of carbon emissions and resilience of building performance to external stresses, such as those imposed by climate change. An example based on a study in Milton Keynes (London), which is currently in progress, is used to illustrate the way causal models may help elucidate the complex interactions between factors that influence energy usage

Disease Surveillance Networks Initiative Africa: Final Evaluation

The overall objective of the Foundation's Disease Surveillance Networks (DSN) Initiative is to strengthen technical capacity at the country level for disease surveillance and to bolster response to outbreaks through the sharing of technical information and expertise. It supports formalizing collaboration, information sharing and best practices among established networks as well as trans-national, interdisciplinary and multi-sectoral efforts, and is experienced in developing and fostering innovative partnerships. In order to more effectively address disease threats, the DSN has four key outcome areas:(1) forming and sustaining trans-boundary DSN;(2) strengthening and applying technical and communication skills by local experts and institutions;(3) increasing access and use of improved tools and methods on information sharing, reporting and monitoring; and(4) emphasizing One Health and transdisciplinary approaches to policy and practice at global, regional and local levels

Towards elimination of dog-mediated human rabies: experiences from implementing a large-scale demonstration project in Southern Tanzania

No abstract available

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Veterinary practitioners have extensive knowledge of animal health from their day-to-day observations of clinical patients. There have been several recent initiatives to capture these data from electronic medical records for use in national surveillance systems and clinical research. In response, an approach to surveillance has been evolving that leverages existing computerized veterinary practice management systems to capture animal health data recorded by veterinarians. Work in the United Kingdom within the VetCompass program utilizes routinely recorded clinical data with the addition of further standardized fields. The current study describes a prototype system that was developed based on this approach. In a 4-week pilot study in New Zealand, clinical data on presentation reasons and diagnoses from a total of 344 patient consults were extracted from two veterinary clinics into a dedicated database and analyzed at the population level. New Zealand companion animal and equine veterinary practitioners were engaged to test the feasibility of this national practice-based health information and data system. Strategies to ensure continued engagement and submission of quality data by participating veterinarians were identified, as were important considerations for transitioning the pilot program to a sustainable large-scale and multi-species surveillance system that has the capacity to securely manage big data. The results further emphasized the need for a high degree of usability and smart interface design to make such a system work effectively in practice. The geospatial integration of data from multiple clinical practices into a common operating picture can be used to establish the baseline incidence of disease in New Zealand companion animal and equine populations, detect unusual trends that may indicate an emerging disease threat or welfare issue, improve the management of endemic and exotic infectious diseases, and support research activities. This pilot project is an important step toward developing a national surveillance system for companion animals and equines that moves beyond emerging infectious disease detection to provide important animal health information that can be used by a wide range of stakeholder groups, including participating veterinary practices

Approaches to canine health surveillance

Effective canine health surveillance systems can be used to monitor disease in the general population, prioritise disorders for strategic control and focus clinical research, and to evaluate the success of these measures. The key attributes for optimal data collection systems that support canine disease surveillance are representativeness of the general population, validity of disorder data and sustainability. Limitations in these areas present as selection bias, misclassification bias and discontinuation of the system respectively. Canine health data sources are reviewed to identify their strengths and weaknesses for supporting effective canine health surveillance. Insurance data benefit from large and well-defined denominator populations but are limited by selection bias relating to the clinical events claimed and animals covered. Veterinary referral clinical data offer good reliability for diagnoses but are limited by referral bias for the disorders and animals included. Primary-care practice data have the advantage of excellent representation of the general dog population and recording at the point of care by veterinary professionals but may encounter misclassification problems and technical difficulties related to management and analysis of large datasets. Questionnaire surveys offer speed and low cost but may suffer from low response rates, poor data validation, recall bias and ill-defined denominator population information. Canine health scheme data benefit from well-characterised disorder and animal data but reflect selection bias during the voluntary submissions process. Formal UK passive surveillance systems are limited by chronic under-reporting and selection bias. It is concluded that active collection systems using secondary health data provide the optimal resource for canine health surveillance