The Netherlands eScience Center has awarded funding to four innovative research proposals related to the Covid-19 pandemic. The selected projects will run between 3 and 12 months and receive in-kind research engineering support. With these projects, the eScience Center will use its expertise in research software development to help address the current pandemic.
The projects span several domains and focus on monitoring and analysing public sentiment to government measures and announcements, the relation between COVID-19 and heart disease, the development of a tailored model to inform public health interventions for infection diseases in the Netherlands, and the refinement of a platform to enable the development and deployment of machine learning algorithms for the automated scoring of CT scans to detect and assess the severity of COVID-19.
‘I am extremely pleased with the quality and depth of the proposals we’ve received’, says Dr. Frank Seinstra, the eScience Center’s program director. ‘The projects are interdisciplinary in nature and allow us to use our full range of expertise for a clear and urgent goal. As the national center for the development and application of research software, our engineers have the skills and the tools to help accelerate novel research outcomes. In that sense, I am especially proud of the speed and dedication with which our engineers have worked to prepare and present such excellent proposals in such a short space of time. The crisis demands quick, decisive and concerted action from the entire research community.’
The projects will kick-off as soon as possible. To stay informed on the latest results, please visit the eScience Center projects page.
Real Time National Policy Adjustment and Evaluation on the Basis of a Computational Model for COVID-19 (Retina COVID19)
Principal Investigator: Prof. Martin Bootsma (UMC Utrecht)
The current COVID-19 pandemic presents an unprecedented challenge for policymakers. Although the major consequences from the uninhibited spread of the COVID-19 virus in Western European countries have abated due to far reaching social distancing measures, these measures carry enormous economic and social costs. Furthermore, basic epidemiological mechanics dictate that some form of containment policy will be necessary for the foreseeable future in order to prevent a recurrent outbreak and keep the impact of COVID-19 manageable. The challenge then is to design public policy interventions informed by epidemiological models. However, these models suffer from what has been termed in other fields the curse of locality – while the basic biology of the virus is the same everywhere, the outcomes will differ according to the local circumstances. For example, the host population in each country is different, societal norms and customs vary and spatial patterns governing movement of people in their daily lives differ. This means that Dutch policy must be informed by a model that is tailored to circumstances in the Netherlands. In this project, work will continue on developing an epidemiological model that can be used to inform public health interventions in the Netherlands.
Research team: Prof. Marc Bonten (UMC Utrecht), Prof. Jason Frank (UU), Prof. Mirjam Kretzschmar (UMC Utrecht, RIVM)
eScience Research Engineers: Inti Pelupessy, Lourens Veen, Ben van Werkhoven, Rena Bakhshi
FAIR Data for CAPACITY
Principal Investigator: Andre Dekker (Maastricht University, Personal Health Train – PHT)
Diagnostic information and data on occurrence of cardiovascular complications in COVID-19 patients are rapidly growing but distributed over different clinical locations. In order to provide the most accurate insights about the relation between cardiovascular history and related complications in COVID-19 patients, statistical analyses and machine learning models need to be kept up to date in real time. This will not be possible by continuously collecting data manually from different locations. This project will build FAIR data stations and automatic data extraction pipelines for defined sets of clinical data as part of a distributed learning infrastructure. This will provide insight into the incidence of cardiovascular complications in patients with COVID-19 and into the vulnerability and clinical course of COVID-19 in patients with an underlying cardiovascular disease.
Research Team: Rick van Nuland (Lygature, HealthRI), Folkert Asselbergs (UMC Utrecht, Dutch Cardiovascular Alliance – DCVA), Mira Staphorst (Hartstichting, DCVA)
eScience Research Engineers: Djura Smits, Lars Ridder
COVID-19 Grand Challenge
Principal Investigator: Dr James Meakin (Radboud UMC)
Diagnostic imaging with computed tomography (CT) and chest X-ray are proving increasingly important in detecting and assessing disease severity of COVID-19. To aid in the clear communication between radiologists and clinicians, the Radiological Society of the Netherlands (NVvR) has proposed a standardised reporting system, CO-RADS, for assessing the suspicion and severity of COVID-19 in a CT scan. In this project, an existing platform, grand-challenge.org (with elements of the the EYRA benchmark platform, where suitable), will be furthered to enable the development and deployment of machine learning algorithms for automated scoring of CT scans using the CO-RADS system.
Research team: Paul Gerke, Mike Overkamp and Miriam Groeneveld (Radboud UMC), Prof. Bram van Ginneken (Radboud UMC)
eScience Research Engineers: Maarten van Meersbergen, Pushpanjali Pawar, Jesus Garcia González
Dutch Public Reaction on Governmental COVID-19 Measures and Announcements (PuReGoMe)
Principal investigator: Shihan Wang (Utrecht University)
Public sentiment (the opinion, attitude or feeling that the public expresses) always attracts the attention of government, as it directly influences the implementation of policies. In the current pandemic, the timely understanding of general public opinion becomes even more important. However, the ‘stay-at-home’ policy makes face-to-face interactions and interviews challenging. Meanwhile, about 2.8 million users in the Netherlands use Twitter to share their opinions, making it a valuable platform for tracking and analysing public sentiment. To understand the variation of Dutch public sentiment during the COVID-19 outbreak period, this project will analyse real-time Twitter data using machine learning and natural language processing approaches. Data collection will be based on COVID-19 related keywords and users. The aim is to provide a cost-effective and efficient way to access public reactions in a timely manner. For instance, instead of waiting for physical behaviours (like taking a walk outside) of people, the latter’s sentiment and intended behaviour could already be gleaned from Twitter data.
Research team: Marijn Schraagen, Mehdi Dastani
eScience Research Engineer: Erik Tjong Kim Sang