Interaction Between Humans and Poultry, Rural Cambodia

Because avian influenza H5N1 infection risks are associated with exposure to infected poultry, we conducted a knowledge, attitudes, and practices survey of poultry-handling behavior among villagers in rural Cambodia. Despite widespread knowledge of avian influenza and personal protection measures, most rural Cambodians still have a high level of at-risk poultry handling

An algorithm applied to national surveillance data for the early detection of major dengue outbreaks in Cambodia

Dengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) project’s knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008–2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks during 2008–2015. Depending on the province, sensitivity and specificity ranged from 50% to 100% and 75% to 100%, respectively. The final algorithm meets clinicians’ and decisionmakers’ needs, is cost-free and is easy to implement at the provincial level

Low Frequency of Poultry-to-Human H5N1 Transmission, Southern Cambodia, 2005

Transmission is low despite extensive human contact with poultry

Brainhack: a collaborative workshop for the open neuroscience community

International audienceBrainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science

Using seasonal forecasts for a climate service for the power sector in the CLIM2POWER Project

International audienceClimate and weather conditions not only strongly influence energy demand but-with the strong development of the renewable energies-also increasingly electricity generation. The changes of the European energy mix together with ongoing climate change raises several questions on the adaptation of the energy supply system to its environment. To address these issues, the CLIM2POWER project aims at translating the latest scientific findings on the medium (seasonal forecast) to long term (climate projections) evolution of the climate into usable information for end-users. For this purpose, we are developing a web-based Climate Service (CS) enabling a visualisation of how climate impacts the electricity system. The web-service will connect climate data, hydrological models and power generation and energy system models in an interactive and user-friendly layout. The CS covers the whole interconnected European electric system complemented with four case-studies reflecting various EU contexts regarding climate, hydrology, socioeconomic settings, electricity generation portfolios and energy markets in Portugal, Sweden, Germany-Austria and France. In each case study, the wind, solar and hydro power generation as well as the demand will be simulated from climate data and their effect on the energy system will be analysed. Since the effect of climate is expected to have a major impact on hydropower, a special attention will be paid to the modelling of three river basins: the Douro river basin in Portugal, the Lule älv river basin in Sweden and the Danube river basin in Germany and Austria. In this context this paper has a twofold objective: 1) to present the used approach to develop an EU-wide CS while considering the regional specificities across Europe, and 2) to describe the approach used to generate seasonal forecasts. The seasonal climate data used in this project is produced by the German Climate Forecast System (GCFS2.0). It is based on the Max-Planck-Institute Earth System Model (MPI-ESM). To apply the data for European and regional case studies, the model output has to be downscaled from the global scale to a higher spatial resolution. Our approach is to use a statistical-dynamical downscaling method that will relate analysed forecasted weather regimes on the global scale to the regional scale. From a regional climate model (COSMO-CLM) driven by ERA-Interim we estimate the recent climate. As a first step, this climatological information shall be used as an input to the impact model chain in order to derive products for the typical seasonal climate. If the downscaled seasonal forecasts can be considered reliable, the climatological products can be replaced by predictions. In order to estimate uncertainties and to enhance the forecast skill we will use a wide range of ensemble seasonal forecasts as will be available in the upcoming C3S climate data store

From hype to reality: data science enabling personalized medicine

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.status: publishe