The human race is facing one of the most meaningful public health emergencies in the modern era caused by the COVID-19 pandemic. This pandemic introduced various challenges, from lock-downs with significant economic costs to fundamentally altering the way of life for many people around the world. The battle to understand and control the virus is still at its early stages yet meaningful insights have already been made. The uncertainty of why some patients are infected and experience severe symptoms, while others are infected but asymptomatic, and others are not infected at all, makes managing this pandemic very challenging. Furthermore, the development of treatments and vaccines relies on knowledge generated from an ever evolving and expanding information space. Given the availability of digital data in the modern era, artificial intelligence (AI) is a meaningful tool for addressing the various challenges introduced by this unexpected pandemic. Some of the challenges include: outbreak prediction, risk modeling including infection and symptom development, testing strategy optimization, drug development, treatment repurposing, vaccine development, and others.

While people may be thinking that AI is still in a research and development stage, they don't actually realize that this isn't true for a lot of cases. In this article, I am going to demonstrate how AI actually helped many organizations to fight the Covid, which I consider to be "indirect help". On another hand, I will also show that it directly helped to develop the actual Covid vaccine. If you haven't checked out my recent blog post on Stanford's Covid mRNA vaccine degradation prediction competition on Kaggle where tons of people from the data science community were actually working on improving the vaccine models, check this out: Although IBM didn't actually come up with the final vaccine model, they were heavily working on it. The IBM team is using a computational model of the spike (S-protein) of SARS-CoV-2 to model its interaction with the human ACE2 receptor .

Early this spring as the pandemic began accelerating, AJ Venkatakrishnan took genetic data from 10,967 samples of the novel coronavirus and fed it into a machine. The Stanford-trained data scientist did not have a particular hypothesis, but he was hoping the artificial intelligence would pinpoint possible weaknesses that could be exploited to develop therapies. He was awed when the program reported back that the new virus appeared to have a snippet of DNA code -- "RRARSAS" -- distinct from its predecessor coronaviruses. This sequence, he learned, mimics a protein that helps the human body regulate salt and fluid balance. Venkatakrishnan, director of scientific research and partnerships at AI start-up Nference, wondered whether this change might allow the virus to act as a kind of Trojan horse. Could this explain its high infection and transmission rates?

While the human brain can process only so much information at a time, machines are whizzes at finding subtle patterns in huge amounts of data, and they are being deployed against covid-19 -- the disease caused by the coronavirus -- in ways only imagined in the past. Data scientists are aiming AI at some of the coronavirus's biggest mysteries -- why the disease looks so different in children vs. adults, what makes some people "superspreaders" while others don't transmit the virus at all -- and other, lesser questions we have made little headway in understanding.

That was really interesting, thank you Patrick for joining us. Patrick Brennan: It was a pleasure, thank you. Norman Swan: Professor Patrick Brennan, who is Professor of Diagnostic Imaging at the University of Sydney. I'm Norman Swan, this has been the Health Report on RN. And don't forget the Coronacast, our daily podcast on all things to do with the coronavirus that Tegan Taylor and I present. You can download it by going to Apple Podcasts, the ABC Listen app, or wherever you get your podcasts. I'll see you next week.