Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects

The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs. These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets. The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk. These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges

Enternal nutrition in the community setting

THESIS 6175The practice of tube feeding patients outside the acute hospital setting has existed in Ireland for over a decade. Anecdotal evidence (mainly from hospital dietitians) suggested that the care services for these patients were inadequate and the subject of home enteral tube feeding required examination. A small pilot survey of patients on tube feeding in nursing homes in Dublin suggested that there was a lack of co-ordination and organisation of the practice of community-based enteral tube feeding. The aim of this work was to investigate enteral tube feeding in Dublin in the community setting from the perspective of patients? and the health care professions

Reservoir Imaging Using Ambient Noise Correlation From a Dense Seismic Network

International audienceIn September 2014, a dense temporary seismic network (EstOF) including 288 single-component geophones was deployed during 1 month in the Outre-Forêt region of the Upper Rhine Graben (France), where two deep geothermal projects (Soultz-sous-Forêts and Rittershoffen) are currently in operation. We apply ambient seismic noise correlation to estimate the empirical Green’s function of the medium between the ~41,200 station pairs in the network. The noise correlation functions obtained are comparable to those from previous studies based on the sparse long-term networks settled in the area mostly to monitor the induced seismic activity. However, the dense spatial coverage of the EstOF network improves our ability toidentify the main phases of the Green’s function. Both the fundamental mode and the first overtone of the Rayleigh waves are identified between most station pairs. P waves are also evidenced. We analyze the statistical distribution of the Rayleigh wave group velocity between station pairs as a function of the period (between 0.8 and 5 s), the station pair orientation, the distance over wavelength ratio and the signal-to-noise ratio. From these observations, we build a high-resolution three-dimensional S wave velocity model of the upper crust (down to 3 km deep) around the regional deep geothermal reservoirs. This model is consistent with local geological structures but also evidences nonlithological variations, particularly at depth in thebasement. These variations are interpreted as large-scale temperature anomalies related to deep hydrothermal circulation

Electron track structure simulations in a gold nanoparticle using Geant4-DNA

Gold Nanoparticles (GNPs) have recently gained a lot of attention due to their potential benefit to improve the efficacy of X-ray radiotherapy. Owing to their high atomic number, GNPs are able to absorb higher quantities of incident radiation with respect to the surrounding tissue, producing, in particular, photoelectrons and low energy Auger electrons. These additional low energy electrons increase the local energy deposition in the region surrounding the GNP. Monte Carlo simulations play a key role in the investigation of GNP radio-enhancement and it is widely recognised that track structure physics models are the state-of-the-art for nano-scale studies.In 2016, we have developed track structure physics models for the Geant4-DNA toolkit allowing electron transport for microscopic bulk gold (Geant4_DNA_AU_2016) and we have recently improved them in the low energy domain (Geant4_DNA_AU_2018).In this paper, we report the benchmarking of these newly developed physics models when calculating the physical dose and the Dose Enhancement Factor (DEF) around a GNP. We demonstrate that Geant4_DNA_AU_2018 models give similar azimuthal distribution of two dimensional absorbed dose around a single GNP, but result in larger absorbed dose and DEF than Geant4_DNA_AU_2016 models. In parallel, we investigated the performance of a newly developed multiple scattering model in Geant4 based on the Goudsmit-Saunderson (GS) model, when used together with the electromagnetic physics models with the Geant4 Livermore condensed-history approach. Our results show that the GS model does not affect the results of the simulations when studying GNP radio-enhancement with a condensed-history approach