Breaking Infection Chains
Within a year since the first reported COVID-19 case in December 2019, nearly 250 million cases were recorded, with Asia accounting for over a fifth of that number. Key to stemming the virus’ spread is tracing the chains of infection, including asymptomatic cases, so that health officials can promptly identify and reach out to those who come into contact with potential carriers.
Supercomputers have a crucial role to play in stamping out these transmission chains, thanks to their ability to streamline contact tracing processes.
In the earlier stages of the pandemic when case counts were surging in South Korea, the Korea Institute of Science and Technology (KIST) and the Korean Ministry of Science and ICT jointly established the KIST’s Individual- based Simulation Toolkit for Transfer phenomena. The tool leverages HPC to simulate the movement of people across the country, collating data on the location of almost 50 million individuals in South Korea.
By tracing infections stemming from specific ‘nodes’ or points of contact, health officials can notify suspected COVID-19 cases to undergo testing or self-quarantine protocols, helping to snap potential transmission chains.
In Hong Kong, meanwhile, the Department of Health worked with the police to deploy the Major Incident Investigation and Disaster Support System, using a supercomputer to map out the relations among infected patients. Through these visualizations, authorities easily identified clusters where cases were flaring up, leading to data-driven policy decisions for mobility restrictions and other interventions.
Peering into the SARS-CoV-2 crystal ball
As a virus spreads, it amasses mutations that can give rise to new variants—ones that can be more contagious and deadly. SARS-CoV-2 is no different, with the Delta variant and now Omicron variant evolving to become at least twice as contagious as the original strain.
To ensure that the emerging variants are accurately detected and that vaccines and treatments still remain effective, scientists are rushing to grasp the changing characteristics of the virus, such as its genetic composition and the expression of proteins essential for host cell invasion.
At CMKL University, established as a collaboration between Carnegie Mellon University in the US and King Mongkut’s Institute of Technology Ladkrabang in Thailand, researchers are using the Apex-Goliath supercomputer to stay ahead of SARS-CoV-2’s mutational curve. With a processing speed around a million times faster than a typical household computer, Apex-Goliath can sift through viral genomic data collected from patients and identify mutation points.
Moreover, the AI-powered system automatically raises an alert if the genetic codes show a propensity for harboring dangerous mutations. By predicting possible mutations and variants quicker than ever before, healthcare experts and state authorities can take proactive measures to stop COVID-19’s spread.
As supercomputers empower novel solutions from detection to drug development, scientists and governments are striving to realize a future where the pandemic is finally under control. In the same way that the effects of COVID-19 are widespread, the interventions against the viral disease have arisen from multidisciplinary efforts bridging fundamental science and technological innovation.
This article was first published in the print version of Supercomputing Asia, January 2022.
Click here to subscribe to Asian Scientist Magazine in print.
———
Copyright: Asian Scientist Magazine. Illustration: Danyel Maxin Santos/Asian Scientist Magazine.
Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.
