This illustration provided by the Centers for Disease Control and Prevention in January 2020 shows
The mysterious coronavirus is spreading at an alarming rate. There have been at least 305 deaths as more than 14,300 persons have been infected.
On Thursday, the World Health Organization (WHO) declared the coronavirus a global emergency. To put things into perspective, it has already exceeded the numbers infected during the 2002-2003 outbreak of SARS (Severe Acute Respiratory Syndrome) in China.
Many countries are working hard to quell the virus. There have been quarantines, lock-downs on major cities, limits on travel and accelerated research on vaccine development. However, could technologies like AI (Artificial Intelligence) help out? Well, interestingly enough, it already has.
Just look at BlueDot, which is a venture-backed startup. The company has built a sophisticated AI platform that processes billions of pieces data, such as from the world’s air travel network, to identity outbreaks.
In the case of the coronavirus, BlueDot made its first alert on December 31st. This was ahead of the US Centers for Disease Control and Prevention, which made its own determination on January 6th.
BlueDot is the mastermind of Kamran Khan, who is an infectious disease physician and professor of Medicine and Public Health at the University of Toronto. Keep in mind that he was a frontline healthcare worker during the SARS outbreak.
“We are currently using natural language processing (NLP) and machine learning (ML) to process vast amounts of unstructured text data, currently in 65 languages, to track outbreaks of over 100 different diseases, every 15 minutes around the clock,” said Khan. “If we did this work manually, we would probably need over a hundred people to do it well. These data analytics enable health experts to focus their time and energy on how to respond to infectious disease risks, rather than spending their time and energy gathering and organizing information.” But of course, BlueDot will probably not be the only organization to successfully leverage AI to help curb the coronavirus. In fact, here’s a look at what we might see:
Colleen Greene, the GM of Healthcare at DataRobot:
“AI could predict the number of potential new cases by area and which types of populations will be at risk the most. This type of technology could be used to warn travelers so that vulnerable populations can wear proper medical masks while traveling.”
Vahid Behzadan, the Assistant Professor of Computer Science at the University of New Haven:
“AI can help with the enhancement of optimization strategies. For instance, Dr. Marzieh Soltanolkottabi’s research is on the use of machine learning to evaluate and optimize strategies for social distancing (quarantine) between communities, cities, and countries to control the spread of epidemics. Also, my research group is collaborating with Dr. Soltanolkottabi in developing methods for enhancement of vaccination strategies leveraging recent advances in AI, particularly in reinforcement learning techniques.”
Dr. Vincent Grasso, who is the IPsoft Global Practice Lead for Healthcare and Life Sciences:
“For example, when disease outbreaks occur, it is crucial to obtain clinical related information from patients and others involved such as physiological states before and after, logistical information concerning exposure sites, and other critical information. Deploying humans into these situations is costly and difficult, especially if there are multiple outbreaks or the outbreaks are located in countries lacking sufficient resources. Conversational computing working as an extension of humans attempting to get relevant information would be a welcome addition. Conversational computing is bidirectional—it can engage with a patient and gather information, or the reverse, provide information based upon plans that are either standardized or modified based on situational variations. In addition, engaging in a multilingual and multimodal manner further extends the conversational computing deliverable. In addition to this ‘front end’ benefit, the data that is being collected from multiple sources such as voice, text, medical devices, GPS, and many others, are beneficial as datapoints and can help us learn to combat a future outbreak more effectively.”
Steve Bennett, the Director of Global Government Practice at SAS and former Director of National Biosurveillance at the U.S. Department of Homeland Security:
“AI can help deal with the coronavirus in several ways. AI can predict hotspots around the world where the virus could make the jump from animals to humans (also called a zoonotic virus). This typically happens at exotic food markets without established health codes. Once a known outbreak has been identified, health officials can use AI to predict how the virus is going to spread based on environmental conditions, access to healthcare, and the way it is transmitted. AI can also identify and find commonalities within localized outbreaks of the virus, or with micro-scale adverse health events that are out of the ordinary. The insights from these events can help answer many of the unknowns about the nature of the virus.
“Now, when it comes to finding a cure for coronavirus, creating antivirals and vaccines is a trial and error process. However, the medical community has successfully cultivated a number of vaccines for similar viruses in the past, so using AI to look at patterns from similar viruses and detect the attributes to look for in building a new vaccine gives doctors a higher probability of getting lucky than if they were to start building one from scratch.”
Don Woodlock, the VP of HealthShare at InterSystems:
“With ML approaches, we can read the tens of billions of data points and clinical documents in medical records and establish the connections to patients that do or do not have the virus. The ‘features’ of the patients that contract the disease pop out of the modeling process, which can then help us target patients that are higher risk.
“Similarly, ML approaches can automatically build a model or relationship between treatments documented in medical records and the eventual patient outcomes. These models can quickly identify treatment choices that are correlated to better outcomes and help guide the process of developing clinical guidelines.”
Prasad Kothari, who is the VP Data Science and AI for The Smart Cube:
“The coronavirus can cause severe symptoms such as pneumonia, severe acute respiratory syndrome, kidney failure etc. AI empowered algorithms such as genome based neural networks already built for personalized treatment can prove very helpful in managing these adverse events or symptoms caused by coronavirus, especially when the effect of virus depends on immunity and the genome structure of individual and no standard treatment can treat all symptoms an effects in the same way.
“In recent times, immunotherapy and Gene therapy empowered through AI algorithms such as boltzmann machines (entropy based combinatorial neural networks) have stronger evidence of treating such diseases which stimulate body’s immunity systems. For this reason, Abbvie’s Aluvia HIV drug is one possible treatment. If you look at data of affected patients and profile virus mechanics and cellular mechanism affected by the coronavirus, there are some similarities in the biological pathways and treatment efficacy. But this is yet to be tested.”: The Forbes