10
ways AI is leading the fight against COVID-19
In the wake of the COVID-19 pandemic, innovators, high tech companies, startups and researchers have turned to artificial intelligence to find solutions to fight the virus.
AI SPECIALIST and former IBM executive Andrew Quixley opines the technology, along with high performance computing (HPC), is one of the most potent weapons in our arsenal as the world combats the corona virus. Here are 10 ways Quixley believes AI is leading the fight against COVID-19.
Outbreak identificatio
2ND QUARTER 2020
Early awareness of a communicable disease outbreak is critical to the efforts to stop it before it really gets established. But who knew that a small handful of flu-like cases emanating from somewhere in China in December were actually the faint signals of something much larger looming? The answer is Kamran Khan, founder and CEO of BlueDot. Khan’s algorithm is trained to look for disease epidemics. It uses AI’s power to make sense of unstructured information, scanning foreign language news sources for anomalies that signal an outbreak, and using global airline ticketing data to predict where the outbreak will go next.
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by Andrew Quixley AI Specialist and former IBM executive
According to this report from CNBC, BlueDot was able to infer a disease outbreak in Wuhan and warn its clients on 31 December, fully six days before the US Center for Disease Control and nine days before the World Health Organisation.
Tracking and predicting the spread of the disease
A major disease outbreak is a highly complex system of systems with potentially trillions of moving parts and millions of variables. Figuring out which of the variables are the main deterministic factors and measuring their likely impact in such a complex system requires immense computing resources. Think multivariate analysis on steroids, on steroids. Machine Learning is particularly suited to modelling such complex scenarios to produce algorithms that are a virtual reflection of reality. The algorithms can be used to predict outcomes when the variables change. Arguably, only machinelearning can do this, and its importance in the toolkit for epidemiology cannot be overstated.
Tracing people who could be infected
The early containment stage for COVID-19 involves meticulously tracing anyone who might have been in contact with an infected person, or been in the vicinity of the pathogen, and isolating them. According to this report from Reuters, China’s exceptional advances in state-wide surveillance technology and their national database of faces enabled them to track one individual from Hangzhou, first through Automated Number Plate Recognition (ANPR) and later through Automated Facial Recognition (AFR), when he breached his own self-isolation before the end of the two weeks. Was this a proportionate and ethical use of AI-powered facial recognition? No doubt this man’s neighbours would have a point of view.
Communicating with citizens
The first infrastructure casualty in a major outbreak will be the government helplines. The same day that the first case is confirmed in any country, the public and private health call centres are likely to be swamped with calls from anxious citizens. Even countries with highly developed healthcare infrastructure are vulnerable to overload, as evidenced with
