When we talk about the 14th century, we probably are making reference to one of the most adverse periods of human history. It was an era of regular armed conflicts, declining social systems, famine, and disease. It was the time of the bubonic plague pandemics, the Black Death, that wiped out millions of people in Europe, Africa, and Asia [1].<\/p>\n

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 <\/span>\u201c<\/span>bad air<\/span>\u201d <\/span>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 <\/span>\u201c<\/span>appease the heavens<\/span>\u201d <\/span>[3]. Though the first of these two strategies <\/span>was<\/span> actually very effective, the second one only increased the tragedy of <\/span>th<\/span><\/span>at<\/span><\/span> scenario.<\/span><\/span>\u00a0<\/span><\/p>\n

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 <\/span><\/span>\u2212<\/span><\/span>such as our medieval predecessors<\/span><\/span>\u2212<\/span><\/span> making things much <\/span>more worse<\/span>. Fortunately, the advances in science and technology have provided humanity with powerful tools to comprehend infectious diseases and rapidly develop response plans. In this <\/span>particular matter<\/span>, epidemic models and simulations have become crucial.<\/span><\/span>\u00a0<\/span><\/p>\n

In the recent COVID-19 events, many public health authorities relied on the outcomes of models, <\/span>so as to<\/span> determine the most probable paths of the epidemic and make informed decisions regarding sanitary measures [4]. Epidemic models have been around for a long <\/span>time, and<\/span> have become more and more sophisticated. One reason is the fact that they feed on data that <\/span>has to<\/span> be collected and processed, and which has increased in quantity and variety.\u00a0<\/span><\/span>\u00a0<\/span><\/p>\n

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\u2019s findings, water quality started to be considered as an important component of public health.<\/span>\u00a0<\/span><\/p>\n

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.<\/span>\u00a0<\/span><\/p>\n

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.<\/span>\u00a0<\/span><\/p>\n

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 <\/span>\u201c<\/span>infection status<\/span>\u201d <\/span>of the neighbors. The infection status varies with time, as people evolve from not having symptoms to <\/span><\/span>have<\/span> <\/span>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.<\/span><\/span>\u00a0<\/span><\/p>\n

Such <\/span>model<\/span> 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.<\/span><\/span>\u00a0<\/span><\/span><\/p>\n

Even though HPC has been fundamental in computational epidemiology to give key insights into epidemic dynamics, we still <\/span>have to<\/span> 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<\/span><\/span>–<\/span><\/span>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.<\/span><\/span>\u00a0<\/span><\/p>\n

 <\/p>\n

References<\/h5>\n

[1] Encyclopedia Britannica. n.d.\u202f<\/span>Crisis, recovery, and resilience: Did the Middle Ages end?<\/span><\/i>. [online] Available at: <https:\/\/www.britannica.com\/topic\/history-of-Europe\/Crisis-recovery-and-resilience-Did-the-Middle-Ages-end> [Accessed 13 September 2022].<\/span>\u00a0<\/span><\/p>\n

[2] Mellinger, J., 2006. Fourteenth-Century England, Medical Ethics, and the Plague.\u202f<\/span>AMA Journal of Ethics<\/span><\/i>, 8(4), pp.256-260.<\/span>\u00a0<\/span><\/p>\n

[3] Carr, H., 2020.\u202f<\/span>Black Death Quarantine: How Did We Try To Contain The Deadly Disease?<\/span><\/i>. [online] Historyextra.com. Available at: <https:\/\/www.historyextra.com\/period\/medieval\/plague-black-death-quarantine-history-how-stop-spread\/> [Accessed 13 September 2022].<\/span>\u00a0<\/span><\/p>\n

[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.\u202f<\/span>Paediatric Respiratory Reviews<\/span><\/i>, 35, pp.57-60.<\/span>\u00a0<\/span><\/p>\n

[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.\u202f<\/span>Case Studies in Chemical and Environmental Engineering<\/span><\/i>, 3, p.100067.<\/span>\u00a0<\/span><\/p>\n

[6] Bbc.co.uk. 2014.\u202f<\/span>Historic Figures: John Snow (1813 – 1858)<\/span><\/i>. [online] Available at: <https:\/\/www.bbc.co.uk\/history\/historic_figures\/snow_john.shtml> [Accessed 13 September 2022].<\/span>\u00a0<\/span><\/p>\n

[7] Publichealth.columbia.edu. 2022.\u202f<\/span>Agent-Based Modeling<\/span><\/i>. [online] Available at: <https:\/\/www.publichealth.columbia.edu\/research\/population-health-methods\/agent-based-modeling> [Accessed 13 September 2022].<\/span>\u00a0<\/span><\/p>\n

[8] Keeling, M. and Eames, K., 2005. Networks and epidemic models.\u202f<\/span>Journal of The Royal Society Interface<\/span><\/i>, 2(4), pp.295-307.<\/span>\u00a0<\/span><\/p>\n

[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 S<\/span>\u00e3<\/span>o Paulo, Brazil.\u202f<\/span>Epidemiology and Infection<\/span><\/i>, 149.<\/span>\u00a0<\/span><\/p>\n

[10] Abdalah, M., Soto, C., Arce, M., Cruz, E., Maciel, J., Clozato, C. and Meneses, E., 2022. Understanding COVID-19 Epidemic in\u202fCosta Rica Through Network-Based Modeling.\u202f<\/span>Communications in Computer and Information Science<\/span><\/i>, pp.61-75.<\/span>\u00a0<\/span><\/p>\n

 <\/p>\n

By CeNAT<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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