The School aimed to provide mini-courses and talks related to programming on high-performance computers, such as parallel programming models, profiling tools, and libraries for developing optimized parallel algorithms for the SDumont user community and the high-performance computing programming community.

Due to the extensive territory of Brazil and the number of research projects, it is mandatory to provide regular HPC schools for the research community. According to Carla Osthoff, one of the organizers of this school, “SDumont is the only Brazilian supercomputer dedicated to the research community that is part of the TOP 500 list. The Brazilian Ministry of Science and Technology offers free access to all Brazilian research projects in the country and foreign collaborators. Currently, we have 238 research projects from 18 research areas.  This edition of the School received 350 registrations, but we also provided online YouTube access to the community.”

The event happened remotely, and all the sessions are available on Youtube.

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On November 7, 2022, LNCC researchers, Carla Osthoff and Kary Ocaña, participated in a seminar in collaboration with our partner Inria, with a presentation entitle “Developing Efficient Scientific Gateways for Bioinformatics in Supercomputer Environments Supported by Artificial Intelligence”. The seminar aimed at promoting the interaction between LNCC’s and Inria’s high-performance computing researchers.

On November 18, Carla Osthoff participated in a seminar organized by the University of Campinas, where she presented the Santos Dumont Supercomputer. The event aimed to promote the exchange of the best HPC practices, promoting the interaction between computer science researchers towards the definition of a coordinated policy and a concrete roadmap for the future.

On November 23, Carla Osthoff also presented the Santos Dumont Supercomputer, on an online lecture, organized by the Research Centre in Digitalization and Intelligent Robotics of the Polytechnic Institute of Bragança, promoting the knowledge exchange between both regions.

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Several factors contributed to the catastrophic outcomes of the Black Death. The crises was boosted by the lack of two important components: knowledge and technology. There was no clue about the spread dynamics of the disease, and containment policies were desperately based on assumptions or beliefs. Some opted for self-isolation to get away from the “bad air” that was believed to be the cause of the illness [2]. Others thought the plague was a divine punishment and persecuted the heretics in order to “appease the heavens” [3]. Though the first of these two strategies was actually very effective, the second one only increased the tragedy of that scenario. 

The bubonic plague of the 14th century is a great example of how unfortunate ignorance can be in the context of epidemics. If the transmission mechanisms are not well-understood, we are not able to design productive measures against them. We may end up −such as our medieval predecessors− making things much more worse. Fortunately, the advances in science and technology have provided humanity with powerful tools to comprehend infectious diseases and rapidly develop response plans. In this particular matter, epidemic models and simulations have become crucial. 

In the recent COVID-19 events, many public health authorities relied on the outcomes of models, so as to determine the most probable paths of the epidemic and make informed decisions regarding sanitary measures [4]. Epidemic models have been around for a long time, and have become more and more sophisticated. One reason is the fact that they feed on data that has to be collected and processed, and which has increased in quantity and variety.  

Data contains interesting patterns that give hints about the influence of apparently non-epidemiological factors such as mobility and interaction type [5]. This is how, in the 19th century, John Snow managed to discover the cause of a cholera epidemic in Soho. He plotted the registered cholera cases in a map and saw they clustered around a water pump that he presumed was contaminated [6]. Thanks to Dr. Snow’s findings, water quality started to be considered as an important component of public health. 

As models grow in intricacy, the demand for more powerful computing systems also increases. In advanced approaches such as agent-based [7] and network (graph) models [8], every person is represented inside a complex framework in which the infection spreads according to specific rules. These rules could be related to the nature of the relations between individuals, their number of contacts, the places they visit, disease characteristics, and even stochastic influences. Frameworks are commonly composed of millions of individuals too, because we often want to analyze countrywide effects. 

In brief, to unravel epidemic dynamics we need to process and produce a lot of accurate information, and we need to do it fast. High-performance computing (HPC) systems provide high-spec hardware and support advanced techniques such as parallel computing, which accelerate calculation by using several resources at a time to perform one or different tasks concurrently. This is an advantage for stochastic epidemic models that require hundreds of independent executions to deliver reliable outputs. Frameworks with millions of nodes or agents need several GB of memory to be processed, which is a requirement that can be met only by HPC systems. 

Based on the work of Cruz et al. [9], we developed a model that represents the spread dynamics of COVID-19 in Costa Rica [10]. This model consists of a contact network of five million nodes, in which every Costa Rican citizen has a family, school, work, or random connection with their neighbors. These relations impact the probability of getting infected, as well as the “infection status” of the neighbors. The infection status varies with time, as people evolve from not having symptoms to have mild, severe, or critical conditions. People may be asymptomatic as well. The model also addresses variations in location, school and workplace sizes, age, mobility, and vaccination rates. In addition, some of these inputs are stochastic. 

Such model takes only a few hours to be simulated in an HPC cluster, when normal systems would require much more time. We managed to evaluate scenarios in which different sanitary measures were changed or eliminated. This analysis brought interesting results, such as that going to a meeting with our family or friends could be as harmful as attending a concert with dozens of strangers, in terms of the additional infections that these activities would generate. Such findings are valuable inputs for health authorities, because they demonstrate that preventing certain behaviors in the population can delay the peak of infections and give them more time to save lives. 

Even though HPC has been fundamental in computational epidemiology to give key insights into epidemic dynamics, we still have to leverage this technology in some contexts. For example, we must first strengthen health and information systems in developing countries to get the maximum advantage of HPC and epidemic models. The above can be achieved through inter–institutional and international collaboration, but also through national policies that support research and development. If we encourage the study of infectious diseases, we benefit from this knowledge in a way that we can approach other pandemics better in the future. 

References

[1] Encyclopedia Britannica. n.d. Crisis, recovery, and resilience: Did the Middle Ages end?. [online] Available at: <https://www.britannica.com/topic/history-of-Europe/Crisis-recovery-and-resilience-Did-the-Middle-Ages-end> [Accessed 13 September 2022]. 

[2] Mellinger, J., 2006. Fourteenth-Century England, Medical Ethics, and the Plague. AMA Journal of Ethics, 8(4), pp.256-260. 

[3] Carr, H., 2020. Black Death Quarantine: How Did We Try To Contain The Deadly Disease?. [online] Historyextra.com. Available at: <https://www.historyextra.com/period/medieval/plague-black-death-quarantine-history-how-stop-spread/> [Accessed 13 September 2022]. 

[4] McBryde, E., Meehan, M., Adegboye, O., Adekunle, A., Caldwell, J., Pak, A., Rojas, D., Williams, B. and Trauer, J., 2020. Role of modelling in COVID-19 policy development. Paediatric Respiratory Reviews, 35, pp.57-60. 

[5] Pasha, D., Lundeen, A., Yeasmin, D. and Pasha, M., 2021. An analysis to identify the important variables for the spread of COVID-19 using numerical techniques and data science. Case Studies in Chemical and Environmental Engineering, 3, p.100067. 

[6] Bbc.co.uk. 2014. Historic Figures: John Snow (1813 – 1858). [online] Available at: <https://www.bbc.co.uk/history/historic_figures/snow_john.shtml> [Accessed 13 September 2022]. 

[7] Publichealth.columbia.edu. 2022. Agent-Based Modeling. [online] Available at: <https://www.publichealth.columbia.edu/research/population-health-methods/agent-based-modeling> [Accessed 13 September 2022]. 

[8] Keeling, M. and Eames, K., 2005. Networks and epidemic models. Journal of The Royal Society Interface, 2(4), pp.295-307. 

[9] Cruz, E., Maciel, J., Clozato, C., Serpa, M., Navaux, P., Meneses, E., Abdalah, M. and Diener, M., 2021. Simulation-based evaluation of school reopening strategies during COVID-19: A case study of São Paulo, Brazil. Epidemiology and Infection, 149. 

[10] Abdalah, M., Soto, C., Arce, M., Cruz, E., Maciel, J., Clozato, C. and Meneses, E., 2022. Understanding COVID-19 Epidemic in Costa Rica Through Network-Based Modeling. Communications in Computer and Information Science, pp.61-75. 

By CeNAT

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Date: October 19, 2022 | 4 p.m. (UTC+1)

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The RISC2 consortium presented the work done so far, demonstrating achievements, and outputs, such as the creation of a strong collaborative network between the European and the Latin American research and industrial communities on advanced HPC application development, the HPC Observatory, the promotion of science, technology, and innovation to overcome different challenges, the project website and the White Paper, among other.

The European Commission was represented by Josiane Xavier Parreira, Laura Tosoratto and Lidia Yamamoto. The reviewers recognized and acknowledged the work done so far and provided advice and suggestions for future improvement. Official result of the review will be available in about one month.

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This first online workshop gathered 20 participants each day to discuss the main challenges for such a convergence and present ongoing related works.  The workshop also aimed to foster more focused cooperation between partners.

The event was moderated by Stéphane Lanteri, from Inria, and had the participation of Daniele Lezzi, from Barcelona Supercomputing Center, António Tadeu Gomes and Kary Ocaña, from LNCC, José Moríñigo, from CIEMAT, Alvaro Coutinho, from Federal University of Rio de Janeiro, Marta Mattoso, from COPPE/Federal University of Rio de Janeiro, and Patrick Valduriez, from Inria.

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GLOBAL CHALLENGES

Global Challenges I – Interactions and integration between the climate science, Social Sciences and Humanities (SSH) and other communities

Participating funding agencies from: Austria, Bolivia, Brazil (CONFAP), Dominican Republic, Germany, Panama, Poland, Spain (AEI), Turkey, Uruguay.

Global Challenges II – Cross-cutting digital research infrastructure

Participating funding agencies from: Austria, Bolivia, Brazil (CNPq, CONFAP), Dominican Republic, Germany, Panama, Spain (AEI), Turkey.

HEALTH

Health I – Personalised Medicine

Participating funding agencies from: Austria, Bolivia, Brazil (CNPq, CONFAP), Dominican Republic, Germany, Italy, Panama, Poland, Spain (AEI and ISCIII), Turkey.

Health II – EU-LAC Regional Hubs: Integrating research infrastructures for Health and Disease

Participating funding agencies from: Austria, Bolivia, Brazil (CONFAP), Dominican Republic, Germany, Italy, Panama, Peru, Portugal, Spain (AEI), Turkey, Uruguay.

BIODIVERSITY

Biodiversity and Ecosystem Services Research Infrastructures

Participating funding agencies from: Austria, Bolivia, Brazil (CNPq, CONFAP), Dominican Republic, Germany, Italy, Panama, Peru, Spain (AEI), Turkey.

ENERGY

Interoperability of energy data spaces for an optimized exploitation by producers and prosumers / Research Infrastructures

Participating funding agencies from: Austria, Bolivia, Brazil (CONFAP), Dominican Republic, Germany, Panama, Spain (AEI), Turkey

Applicants searching for potential European and / or Latin American & Caribbean partners are invited to register for free at the online ENRICH in LAC Matchmaking platform. This new platform enables virtual direct contacts and project initiation among RTI focussed researchers, start-ups, companies, soft landing hubs, and other organizations between the LAC region and Europe at any time.

Call documents are available here.

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Rafael Mayo Garcia joined the scientific committee at the Artificial Intelligence for Science, Industry and Society (AISIS) 2021.

AISIS is a conference that brings together scientists, industry representatives and policy makers and discusses the implementation of AI in a variety of areas and disciplines. This year’s edition had a great focus on how AI has facilitated the global response to the COVID-19 pandemic. Hosted online, the event took place at National Autonomous University of Mexico (UNAM).

According to Rafael Mayo Garcia, he worked “on the definition of the agenda and the review of contributions” with different members from around the world. The program and agenda in which RISC2’s partner had an important role in was composed by several keynote speakers, topics and convenors.

Learn more about this event and Rafael Mayo Garcia’s role in it here.

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