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International audience ; AbstractEuropean Union legislation requires member states to keep national databases of all bovine animals. This allows for disease spread models that includes the time-varying contact network and population demographic. However, performing data-driven simulations with a high degree of detail are computationally challenging. We have developed an efficient and flexible discrete-event simulator SimInf for stochastic disease spread modelling that divides work among multiple processors to accelerate the computations. The model integrates disease dynamics as continuous-time Markov chains and livestock data as events. In this study, all Swedish livestock data (births, movements and slaughter) from July 1st 2005 to December 31st 2013 were included in the simulations. Verotoxigenic Escherichia coli O157:H7 (VTEC O157) are capable of causing serious illness in humans. Cattle are considered to be the main reservoir of the bacteria. A better understanding of the epidemiology in the cattle population is necessary to be able to design and deploy targeted measures to reduce the VTEC O157 prevalence and, subsequently, human exposure. To explore the spread of VTEC O157 in the entire Swedish cattle population during the period under study, a within- and between-herd disease spread model was used. Real livestock data was incorporated to model demographics of the population. Cattle were moved between herds according to real movement data. The results showed that the spatial pattern in prevalence may be due to regional differences in livestock movements. However, the movements, births and slaughter of cattle could not explain the temporal pattern of VTEC O157 prevalence in cattle, despite their inherently distinct seasonality.

Abstract European Union legislation requires member states to keep national databases of all bovine animals. This allows for disease spread models that includes the time-varying contact network and population demographic. However, performing data-driven simulations with a high degree of detail are computationally challenging. We have developed an efficient and flexible discrete-event simulator SimInf for stochastic disease spread modelling that divides work among multiple processors to accelerate the computations. The model integrates disease dynamics as continuous-time Markov chains and livestock data as events. In this study, all Swedish livestock data (births, movements and slaughter) from July 1st 2005 to December 31st 2013 were included in the simulations. Verotoxigenic Escherichia coli O157:H7 (VTEC O157) are capable of causing serious illness in humans. Cattle are considered to be the main reservoir of the bacteria. A better understanding of the epidemiology in the cattle population is necessary to be able to design and deploy targeted measures to reduce the VTEC O157 prevalence and, subsequently, human exposure. To explore the spread of VTEC O157 in the entire Swedish cattle population during the period under study, a within- and between-herd disease spread model was used. Real livestock data was incorporated to model demographics of the population. Cattle were moved between herds according to real movement data. The results showed that the spatial pattern in prevalence may be due to regional differences in livestock movements. However, the movements, births and slaughter of cattle could not explain the temporal pattern of VTEC O157 prevalence in cattle, despite their inherently distinct seasonality.

Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.

Increasing complexity and volume of research data pose increasing challenges for scientists to manage their data efficiently. At the same time, availability and reuse of research data are becoming more and more important in modern science. The German government has established an initiative to develop research data management (RDM) and to increase accessibility and reusability of research data at the national level, the Nationale Forschungsdateninfrastruktur (NFDI). The NFDI Neuroscience (NFDI-Neuro) consortium aims to represent the neuroscience community in this initiative. Here, we review the needs and challenges in RDM faced by researchers as well as existing and emerging solutions and benefits, and how the NFDI in general and NFDI-Neuro specifically can support a process for making these solutions better available to researchers. To ensure development of sustainable research data management practices, both technical solutions and engagement of the scientific community are essential. NFDI-Neuro is therefore focusing on community building just as much as on improving the accessibility of technical solutions.

International audience ; The European Union funded project MICEV aims at improving the traceability of electrical and magnetic measurement at charging stations and to better assess the safety of this technology with respect to human exposure. The paper describes some limits of the instrumentation used for electrical measurements in the charging stations, and briefly presents two new calibration facilities for magnetic field meters and electric power meters. Modeling approaches for the efficiency and human exposure assessment are proposed. In the latter case, electromagnetic computational codes have been combined with dosimetric computational codes making use of highly detailed human anatomical phantoms in order to establish human exposure modeling real charging stations. Detailed results are presented for light vehicles, where the human exposure does not cause any concern. Currently, the project has reached half way point (about 18 months) and will end in August 2020.

International audience ; The European Union funded project MICEV aims at improving the traceability of electrical and magnetic measurement at charging stations and to better assess the safety of this technology with respect to human exposure. The paper describes some limits of the instrumentation used for electrical measurements in the charging stations, and briefly presents two new calibration facilities for magnetic field meters and electric power meters. Modeling approaches for the efficiency and human exposure assessment are proposed. In the latter case, electromagnetic computational codes have been combined with dosimetric computational codes making use of highly detailed human anatomical phantoms in order to establish human exposure modeling real charging stations. Detailed results are presented for light vehicles, where the human exposure does not cause any concern. Currently, the project has reached half way point (about 18 months) and will end in August 2020.

International audience ; The European Union funded project MICEV aims at improving the traceability of electrical and magnetic measurement at charging stations and to better assess the safety of this technology with respect to human exposure. The paper describes some limits of the instrumentation used for electrical measurements in the charging stations, and briefly presents two new calibration facilities for magnetic field meters and electric power meters. Modeling approaches for the efficiency and human exposure assessment are proposed. In the latter case, electromagnetic computational codes have been combined with dosimetric computational codes making use of highly detailed human anatomical phantoms in order to establish human exposure modeling real charging stations. Detailed results are presented for light vehicles, where the human exposure does not cause any concern. Currently, the project has reached half way point (about 18 months) and will end in August 2020.