HiDALGO success story: Assisting decision makers to solve Global Challenges with HPC applications – Covid-19 modelling

HIGHLIGHTED CENTRE OF EXCELLENCE

HiDALGO is the Center of Excellence in HPC and Big Data technologies for Global Systems funded by the European Commission. Understanding global challenges to assist decision making by addressing multi-dimensional problems is a real need nowadays. HiDALGO develops novel methods, algorithms and software for HPC and HPDA enabling highly accurate simulations, data analytics and data visualisation to accurately model and simulate these complex processes.

Organisations & Codes Involved:

The National Health Service (NHS) oversees offering public health services in United Kingdom. It is now dealing with the COVID-19 pandemic.

 

Brunel University London is a dynamic institution that plays a significant role in the higher education sector. It carries out applied research on different topics, such as software engineering, intelligent data analysis, human computer interaction, information systems, and systems biology.

CHALLENGE:​

The current pandemic situation has increased the NHS need of supporting tools to detect, predict and even prevent the virus spread behaviour. Knowing in advance this information will support them to take the appropriate decisions while considering health and care capabilities. In addition, the advance warning of new pandemic waves (or when they may subside) can help health authorities to rescale the capacity for non-urgent care, and ensure the timely arrangement of surge intensive-care capacity.

SOLUTION:​

To tackle these challenges HiDALGO developed a tool: FACS, the Flu and Coronavirus Simulator, which is an agent-based model that also incorporates SEIRDI (Susceptible-Exposed-Infectious-Recovered-Dead-Immunized) states for all agents. 

FACS approximates viral spread on the individual building level, and incorporates geospatial data sources from OpenStreetMap. In this way COVID-19 spread is modelled at local level, providing estimations of the spread of infections and hospital arrivals, given a range of public health interventions. Lastly, FACS supports the modelling of vaccination policies, as well as the introduction of new viral strains and changes in vaccine efficacy.

SOCIETAL & ECONOMIC IMPACT:

Although it is not a market topic itself, global challenges have been gaining importance in the last years, especially those related to climate change but also others such as peace and conflict. HiDALGO offers a set of tools, services, datasets and resources to define models that may predict situations under certain scenarios that can influence any decision to be taken.

More specifically, this tool FACS provides the needed information to decision makers so they can set up the appropriate measure and provide the necessary means at any time.

Indeed the tool helps the NHS to identify peaks of contagion in order to avoid sanitary collapses. Taking the appropriate decisions at the right moment represents a better investment of public resources and, what is more important, saving lives. Moreover, it supports to make better decisions and at appropriate time, to limit the problematic economic consequences of lockdowns and of the other measures taken in pandemic times.

BENEFITS FOR FURTHER RESEARCH:

  • Support for the preparatory efforts by the health service for the second and third waves of the pandemic in West London.
  • Better understanding of the nature of the current situation and the effect of different measures, such as lockdowns and vaccine efficacy levels.
  • Provide models and elements about the foreseen evolution to limit the problematic economic consequences of the lockdowns and the various limitations due to the pandemic.

IMAGES:

HiDALGO success story: Assisting decision makers to solve Global Challenges with HPC applications – Migration issues

HIGHLIGHTED CENTRE OF EXCELLENCE

HiDALGO is the Center of Excellence in HPC and Big Data technologies for Global Systems funded by the European Commission. Understanding global challenges to assist decision making by addressing multi-dimensional problems is a real need nowadays. HiDALGO develops novel methods, algorithms and software for HPC and HPDA enabling highly accurate simulations, data analytics and data visualisation to accurately model and simulate these complex processes.

Organisations & Codes Involved:

Save the Children is an NGO promoting policy changes to gain more rights for young people, especially by enforcing the UN Declaration of the Rights of the Child.

 

 

Brunel University London is a dynamic institution that plays a significant role in the higher education sector. It carries out applied research on different topics, such as software engineering, intelligent data analysis, human computer interaction, information systems, and systems biology.

CHALLENGE:​

At the same time of the current COVID-19 pandemic, other crises have not stopped like forced migration due to conflicts. In fact, the number of forcibly displaced people is still very high, with over 70 million persons being forced to leave their homes. Save the Children provides support in these countries and needs more accurate estimations on people flows and even destinations to send the appropriate amount of help to the right place.

SOLUTION:​

To tackle these challenges HiDALGO provides the tool Flee 2.0, a forced migration model which includes refugees and internally displaced people. It places agents that represent displaced persons in areas of conflict and uses movement rules to mimic their behavior as they attempt to find safety. 

The code extracts location and route data from OpenStreetMap, and can be validated against UNHCR data for historical conflicts. As output, Flee provides a forecast of the amount (and location) of people that can be displaced given different conflict development scenarios.

SOCIETAL & ECONOMIC IMPACT:

Although it is not a market topic itself, global challenges have been gaining importance in the last years, especially those related to climate change but also others such as peace and conflict. HiDALGO offers a set of tools, services, datasets and resources to define models that may predict situations under certain scenarios that can influence any decision to be taken.

More specifically, Flee 2.0 provides the needed information to decision makers so they can set up the appropriate measure and provide the necessary means at any time.

For instance Flee 2.0 is used to estimate the expected number of refugee arrivals when conflicts occur in North Ethiopia, and to investigate how different conflict developments could affect the number of arrivals. The model development is partially guided by on-site experts and typically provides forecasts of approximately 3 month duration.  Although work is ongoing on this project and the models are still basic, the aim is to establish a systematically developed mathematical model to improve the understanding of Save the Children about the migration situation, and support the preparation to mitigate the humanitarian impact of a potential upcoming crisis.

BENEFITS FOR FURTHER RESEARCH:

  • Accurate predictions of where people may arrive in Sudan and how quicky, if new violence occurs in North
  • Estimate of the effect of different conflict developments on the expected number of arrivals.
  • Estimate of the effect of specific policy decisions on the expected number of arrivals.

Watch The Presentations Of The First CoE Joint Technical Workshop

22. February 2021

Watch the recordings of the presentations from the first technical CoE workshop. The virtual event was organized by the three HPC Centres of Excellence ChEESE, EXCELLRAT and HiDALGO. The agenda for the workshop was structured in these four session:

Session 1: Load balancing
Session 2: In situ and remote visualisation
Session 3: Co-Design
Session 4: GPU Porting

You can also download a PDF version for each of the recorded presentations. The workshop took place on January 27 – 29, 2021. 

Session 1: Load balancing

Title: Introduction by chairperson
Speaker: Ricard Borell (BSC)

Title: Intra and inter-node load balancing in Alya Speaker: Marta Garcia and Ricard Borell (BSC)

Title: Load balancing strategies used in AVBP
Speaker: Gabriel Staffelbach (CERFACS)

Title: Addressing load balancing challenges due to fluctuating performance and non-uniform workload in SeisSol and ExaHyPE
Speaker: Michael Bader (TUM)

Title: On Discrete Load Balancing with Diffusion Type Algorithms
Speaker: Robert Elsäßer (PLUS)

Session 2: In situ and remote visualisation

Title: Introduction by chairperson
Speaker: Lorenzo Zanon & Anna Mack (HLRS)

 

Title: An introduction to the use of in-situ analysis in HPC
Speaker: Miguel Zavala (KTH)

Title: In situ visualisation service in Prace6IP
Speaker: Simone Bnà (CINECA)

Title: Web-based Visualisation of air pollution simulation with COVISE
Speaker: Anna Mack (HLRS)

Title: Virtual Twins, Smart Cities and Smart Citizens
Speaker: Leyla Kern, Uwe Wössner, Fabian Dembski (HLRS)

Title: In-situ simulation visualisation with Vistle Speaker: Dennis Grieger (HLRS)

Session 3: Co-Design

Title: Introduction by chairperson, and Excellerat’s Co-Design Methodology
Speaker: Gavin Pringle (EPCC)

Title: Accelerating codes on reconfigurable architectures
Speaker: Nick Brown (EPCC)

Title: Benchmarking of Current Architectures for Improvements
Speaker: Nikela Papadopoulou (ICCS)

Title: Example Co-design Approach with the Seissol and Specfem3D Practical cases
Speaker: Georges-Emmanuel Moulard (ATOS)

Title: Exploitation of Exascale Systems for Open-Source Computational Fluid Dynamics by Mainstream Industry
Speaker: Ivan Spisso (CINECA)

Session 4: GPU Porting

Title: Introduction
Speaker: Giorgio Amati (CINECA)

Title: GPU Porting and strategies by Excellerat Speaker: Ivan Spisso (CINECA)

Title: GPU Porting and strategies by ChEESE
Speaker: Piero Lanucara (CINECA)

Title: GPU porting by third party library
Speaker: Simone Bnà (CINECA)

Title: The HySEA GPGPU development and its role in ChEESE project Speaker: Marc de la Asunción (UMA)
(c) Fusion Medical Animation on Unsplash

How EU projects work on supercomputing applications to help contain the corona virus pandemic

The Centres of Excellence in high-performance computing are working to improve supercomputing applications in many different areas: from life sciences and medicine to materials design, from weather and climate research to global system science. A hot topic that affects many of the above-mentioned areas is, of course, the fight against the corona virus pandemic.

There are rather obvious challenges for those EU projects that are developing HPC applications for simulations in medicine or in the life sciences, like CompBioMed (Biomedicine) BioExcel (Biomolecular Research), and PerMedCoE (Personalized Medicine). But also other projects from scientific areas, that you would, at first sight, not directly relate to research on the pandemic, are developing and using appropriate applications to model the virus and its spread, and support policy makers with computing-heavy simulations. For example, did you know that researchers can simulate the possible spread of the virus on a local level, taking into account measures like closing shops or quarantining residents?

This article gives an overview over the various ways in which EU projects are using supercomputing applications to tackle and support the global challenge of containing the pandemic.

Simulations for better and faster drug development

CompBioMed is an EU-funded project working on applications for computational biomedicine. It is part of a vast international consortium across Europe and USA working on urgent coronavirus research. “Modelling and simulation is being used in all aspects of medical and strategic actions in our fight against coronavirus. In our case, it is being harnessed to narrow down drug targets from billions of candidate molecules to a handful that can be clinically trialled”, says Peter Coveney from University College London (UCL) who is heading CompBioMed’s efforts in this collaboration. The goal is to accelerate the development of antiviral drugs by modelling proteins that play critical roles in the virus life cycle in order to identify promising drug targets.

Secondly, for drug candidates already being used and trialled, the CompBioMed scientists are modelling and analysing the toxic effects that these drugs may have on the heart, using supercomputing resources required to run simulations on such scales.  The goal is to assess the drug dosage and potential interactions between drugs to provide guidance for their use in the clinic.

Finally, the project partners analysed a model used to inform the UK Government’s response to the pandemic. It has been found to contain a large degree of uncertainty in its predictions, leading it to seriously underestimate the first wave. “Epidemiological modelling has been and continues to be used for policy-making by governments to determine healthcare interventions”, says Coveney. “We have investigated the reliability of such models using HPC methods required to truly understand the uncertainty and sensitivity of these models.” As a conclusion, a better public understanding of the inherent uncertainty of models predicting COVID-19 mortality rates is necessary, saying they should be regarded as “probabilistic” rather than being relied upon to produce a particular and specific outcome.

Image of SuperMUC-NG, supercomputer at Leibniz Supercomputing Centre of the Bavarian Academy of Sciences. (c)MMM/LRZ
Image of SuperMUC-NG, supercomputer at Leibniz Supercomputing Centre of the Bavarian Academy of Sciences, consortium member in the CompBioMed project. (c) MMM/LRZ

BioExcel is an EU-funded project developing some of the most popular applications for modelling and simulations of biomolecular systems. Along with code development, the project builds training programmes to address competence gaps in extreme-scale scientific computing for beginners, advanced users and system maintainers.

When COVID-19 struck, BioExcel launched a series of actions to support the community on SARS-CoV-2 research, with an extensive focus on facilitating collaborations, user support, and providing access to HPC resources at partner centers. BioExcel partnered with Molecular Sciences Software Institute to establish the COVID-19 Molecular Structure and Therapeutics Hub to allow researchers to deposit their data and review other group’s submissions as well.

During this period, there was an urgent demand for diagnostics and sharing of data for COVID-19 applications had become vital more than ever. A dedicated BioExcel-CV19 web-server interface was launched to provide access to study molecules involved in the COVID-19 disease. This allowed the project to be a part of open access initiative promoted by the scientific community to make research accessible.

Recently, BioExcel endorsed the EU manifesto for COVID-19 Research launched by European Commission as part of their response to the coronavirus outbreak.

Modelling the electronic structure of the protease

MaX (MAterials design at the eXascale) is a European Centre of Excellence aiming at materials modelling, simulations, discovery and design on the exascale supercomputing architectures.

Though the main interest of the MaX flagship codes is then centered on materials science, the CoE is participating in the fight against SARS-CoV-2. Given the critical pandemic situation that the world is currently facing, an unprecedented effort is being devoted to the study of SARS-CoV-2 by researchers from different scientific communities and groups worldwide. From the biomolecular standpoint, particular focus is being devoted to the main protease, as well as to the spike protein. As such, it is an important potential antiviral drug target: if its function is inhibited, the virus remains immature and non-infectious. Using fragment-based screening, researchers have identified a number of small compounds that bind to the active site of the protease and can be used as a starting point for the development of protease inhibitors.

Sars-Cov-2 main protease monomer, in green, with the N3 3-mer peptide inhibitor bound in the enzyme’s active site.(from PDB crystal structure 6lu7). Structure like this ones can be simulated with a full DFT calculation and automatically decomposed into fragments whose interaction network can be characterized and analyzed.

Among other quantities, MaX researchers now have the possibility to model the electronic structure of the protease in contact with a potential docked inhibitor, and provide new insights on the interactions between them by selecting specific amino-acids that are involved in the interaction and characterizing their polarities. This new approach proposed by the MaX scientists is complementary to the docking methods used up to now and based on in-silico research of the inhibitor. Biological systems are naturally composed of fragments such as amino-acids in proteins or nitrogenous bases in DNA.

With this approach, it is possible to evaluate whether the amino acid-based fragmentation is consistent with the electronic structure resulting from the QM computation. This is an important indicator for the end-user, as it enables to evaluate the quality of the information associated with a given fragment. Then, QM observables on the system’s fragments can be obtained, which are based on a population analysis of electronic density of the system, projected on the amino-acid.

A novelty that this approach enables is the possibility of quantifying the strength of the chemical interaction between the different fragments. It is possible to select a target region and identify which fragments of the systems interact with this region by sharing electrons with it.

“We can reconstruct the fragmentation of the system in such a way as to focus on an active site in a specific portion of the protein”, says Luigi Genovese from CEA (Commissariat à l’énergie atomique et aux énergies alternatives) who is heading Max’s efforts on this topic. “We think this modelling approach could inform efforts in protein design by granting access to variables otherwise impervious to observation.”

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus, named Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019. The illness caused by this virus has been named coronavirus disease 2019 (COVID-19).
Various EU projects are using supercomputing applications to tackle and support the global challenge of containing the pandemic (c)CDC on Unsplash

Improving drug design and biosensors

The project E-CAM supports HPC simulations in industry and academia through software development, training and discussion in simulation and modeling. Project members are currently following two approaches to add to the research on the corona virus.

Firstly, the SARS-CoV-2 virus that causes COVID-19 uses a main protease to be functional. One of the drug targets currently under investigation is an inhibitor for this protease. While efforts on simulations of binding stability and dynamics are being conducted, not much is known of the dynamical transitions of the binding-unbinding reaction. Yet, this knowledge is crucial for improved drug design. E-CAM aims to shed light on these transitions, using a software package developed by project teams at the University of Amsterdam and the Ecole Normale Superieure in Lyon.

Secondly, E-CAM contributes to the development of the software required to design a protein-based sensor for the quick detection of COVID-19. The sensor, developed at the partner University College Dublin with the initial purpose to target influenza, is now being adapted to SARS-CoV-2. This adaptation needs DNA sequences as an input for suitable antibody-epitope pairs. High-performance computing is required to identify these DNA sequences to design and simulate the sensors prior to their expression in cell lines, purification and validation.

Studying COVID-19 infections on the cell level

The project PerMedCoE aims to optimise codes for cell-level simulations in high-performance computing, and to bridge the gap between organ and molecular simulations. The project started in October 2020.

“Multiscale modelling frameworks prove useful in integrating mechanisms that have very different time and space scales, as in the study of viral infection, human host cell demise and immune cells response. Our goal is to provide such a multiscale modelling framework that includes infection mechanisms, virus propagation and detailed signalling pathways,” says Alfonso Valencia, PerMedCoE project coordinator at the Barcelona Supercomputing Center.

The project team has developed a use case that focusses on studying COVID-19 infections using single-cell data. The work was presented to the research community at a specialized virtual conference in November, the Disease Map Community Meeting. “This use case is a priority in the first months of the project”, says Valencia.

On the technical level, disease maps networks will be converted to models of COVID-19 and human cells from the lung epithelium and the immune system. Then, the team will use omics data to personalise models of different patients’ groups, differentiated for example by age or gender. These data-tailored models will then be incorporated into a COVID-focussed version of the open source cell-level simulator PhysiCell.

Supporting policy makers and governments

The HiDALGO project focusses on modelling and simulating the complex processes which arise in connection with major global challenges. The researchers have developed the Flu and Coronavirus Simulator (FACS) with the objective to support decision makers to provide an appropriate response to the current pandemic situation taking into account health and care capabilities.

FACS is guided by the outcomes of SEIR (Susceptible-Exposed-Infectious-Recovered) models operating at national level. It uses geospatial data sources from Openstreet Map to approximate the viral spread in crowded places, while trading the potential routes to reach them.

In this way, the simulator can model the COVID-19 spread at local level to provide estimations of infections and hospital arrivals, given a range of public health interventions, going from no interventions to lockdowns. Public authorities can use the results of the simulations to identify peaks of contagion, set appropriate measures to reduce spread and provide necessary means to hospitals to prevent collapses. “FACS has enabled us to forecast the spread of COVID-19 in regions such as the London Borough of Brent. These forecasts have helped local National Health Service Trusts to more effectively plan out health and care services in response to the pandemic.” says Derek Groen from the HiDALGO project partner Brunel University London.

Scientists from the HiDALGO project use simulations to predict the spread of the Corona virus in certain areas of London. (c)HiDALGO
Scientists from the HiDALGO project use simulations to predict the spread of the Corona virus in certain areas of London. (c)HiDALGO

EXCELLERAT is a project that is usually focussing on supercomputing applications in the area of engineering. Nevertheless, a group of researchers from EXCELLERAT’s consortium partner SSC-Services GmbH, an IT service provider in Böblingen, Germany and the High-Performance Computing Center Stuttgart (HLRS) are also providing measures to contain the pandemic by supporting the German Federal Institute for Population Research (Bundesinstitut für Bevölkerungsforschung, BiB).

The scientists have developed an intelligent data transfer platform, which enables the BiB to upload data, perform computing-heavy simulations on the HLRS’ supercomputer Hawk, and download the results. The platform supports the work of BiB researchers in predicting the demand for intensive care units during the COVID-19 pandemic. “Nowadays, organisations face various issues while dealing with HPC calculations, HPC in general or even the access to HPC resources,” said Janik Schüssler, project manager at SSC Services. “In many cases, calculations are too complex and users do not have the required know-how with HPC technologies. This is the challenge that we have taken on. The BiB’s researchers had to access HLRS’s Hawk in a very complex way. With the help of our new platform, they can easily access Hawk from anywhere and run their simulations remotely.”

“This platform is part of EXCELLERAT’s overall strategy and tools development, which not only addresses the simulation part of engineering workflows, but provides users the necessary means to optimise their work”, said Bastian Koller, Project Coordinator of EXCELLERAT and HLRS’s Managing Director. “Extending the applicability of this platform to further use cases outside of the engineering domain is a huge benefit and increases the impact of the work performed in EXCELLERAT.”

ETP4HPC handbook 2020 released

6. November 2020

The 2020 edition of the ETP4HPC Handbook of HPC projects is available. It offers a comprehensive overview over the European HPC landscape that currently consists of around 50 active projects and initiatives. Amongst these are the 14 Centres of Excellence and FocusCoE, that are also represented in this edition of the handbook.

>> Read here

HPC Centres of Excellence @ Supercomputing '20

4. November 2020

Due to restrictions caused by the global COVID-19 pandemic, the SC20 conference – the world’s leading HPC event – will take place online this year from November 9-19. 

Find below the CoE’s contributions to the 2020 edition of the Supercomputing Conference.

HiDALGO Support Team

October 13, 2020

Looking for help or individual support? HiDALGO CoE provides a customer support and a community forum for users that want to dive a little deeper or need help while using HiDALGO technologies in the Global System Science HPC applications

>> Read More