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AI is on the frontline of the COVID19 response

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Our lives have been fundamentally changed by the rise of COVID-19.

The current outbreak has led to unprecedented shutdowns around the world, and billions of people are isolating to help reduce the spread. It has been more than a century since the world has seen an outbreak on this scale. Although COVID-19 has created countless unforeseen challenges, we now have a much more powerful set of tools with our current technology with which to fight the virus — including artificial intelligence.

The tools with which we make predictions, research treatments, and distribute much-needed supplies are powered by the world’s most advanced algorithms. Here are what some of the responses look like, how AI is impacting the front lines, and the role AI is playing in the home.

Fighting the COVID-19 pandemic is going to take data — lots of it — and that data will need to be shared easily and seamlessly. These are the recommendations that our clients and candidates are suggesting to:

  • Create a single data repository
  • Clean the data
  • Normalize and layer the data
  • Create work teams
  • Revisit regulations
  • Build an information repository

With those steps in mind, we can dive deeper into how AI can effectively help the fight against COVID-19.

Machine learning to help predict the spread

Epidemiology relies almost entirely on data to predict the potential spread of disease.

The first indications of the outbreak were delivered by BlueDot’s infectious disease tracking algorithms when they identified a string of pneumonia cases in Wuhan, China. The company uses both machine learning and natural language processing to identify patterns from hundreds of data sources — everything from flight patterns to weather reports. Similar technology is being used now to process huge volumes of data that could help identify hot spots and treatment options.

Roni Rosenfeld, a computer science professor at Carnegie Mellon University who runs the Delphi research group, was recently tasked by the Center for Disease Control (CDC) to help predict the spread of COVID-19 using the team’s machine learning algorithms. Their technology is designed to improve disease forecasting and put it more on par with weather forecasts that we routinely rely on. They’ve used “wisdom of crowds,” which aggregates the feedback of dozens of individuals to make specific forecasts.

In China, machine vision algorithms and hardware are being used to screen hundreds of people at a time for raised temperatures in Beijing’s railway stations.

AI to flatten the curve

AI is being used to help flatten the curve and limit the number of concurrent cases of the disease so we don’t overwhelm hospitals. One example is the work of CloudMedx, drawing data from payers, providers, and patients to make targeted predictions about the demand for medical equipment, staff, and the flow of patients both geographically and by duration.

Other tools are being leveraged to identify high-risk candidates and protect them proactively from exposure. Medical Home Network, for example, is using an AI system to identify their highest risk patients and target their outreach efforts to those individuals.

Vaccines using data science to speed the process

Chinese researchers were able to decode the genetic sequence of the disease and upload it to a public database by January 10, and researchers around the globe are now using computer algorithms to generate potential vaccine designs. San Diego-based biotech firm Inovio Pharmaceuticals was able to use its machine learning system to develop a potential vaccine candidate in just three hours.

COVID-19 Impact on data jobs

Although data and analytics professionals may be working harder, another respondent in the transportation industry noted that the team was getting more visibility, “Data and analytics has become more high profile across the company in helping to assist in making business decisions in a fast-changing environment.” This is a positive impact for the future of AI use within companies, therefore a great hiring argument for future employment.

Lessons learned from Data and our preparedness for COVID-19

Agility is key and requires a strong foundation. Collaborative data features accelerate analytics to help stop the pandemic and are a great tool for future preparedness.






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