Use of Artificial Intelligence during the COVID19 Pandemic

Here are some of the projects using AI to address the coronavirus outbreak:

AI in Drug Discovery

A number of research projects are using AI to identify drugs that were developed to fight other diseases but which could now be repurposed to take on coronavirus. By studying the molecular setup of existing drugs with AI, companies want to identify which ones might disrupt the way COVID-19 works. 

BenevolentAI, a London-based drug-discovery company, began turning its attentions towards the coronavirus problem in late January. The company's AI-powered knowledge graph can digest large volumes of scientific literature and biomedical research to find links between the genetic and biological properties of diseases and the composition and action of drugs. 

The company had previously been focused on chronic disease, rather than infections, but was able to retool the system to work on COVID-19 by feeding it the latest research on the virus. "Because of the amount of data that's being produced about COVID-19 and the capabilities we have in being able to machine-read large amounts of documents at scale, we were able to adapt [the knowledge graph] so to take into account the kinds of concepts that are more important in biology, as well as the latest information about COVID-19 itself," says Olly Oechsle, lead software engineer at BenevolentAI. 

While a large body of biomedical research has built up around chronic diseases over decades, COVID-19 only has a few months' worth of studies attached to it. But researchers can use the information that they have to track down other viruses with similar elements, see how they function, and then work out which drugs could be used to inhibit the virus. 

"The infection process of COVID-19 was identified relatively early on. It was found that the virus binds to a particular protein on the surface of cells called ACE2. And what we could with do with our knowledge graph is to look at the processes surrounding that entry of the virus and its replication, rather than anything specific in COVID-19 itself. That allows us to look back a lot more at the literature that concerns different coronaviruses, including SARS, etc. and all of the kinds of biology that goes on in that process of viruses being taken in cells," Oechsle says. 

The system suggested a number of compounds that could potentially have an effect on COVID-19 including, most promisingly, a drug called Baricitinib. The drug is already licensed to treat rheumatoid arthritis. The properties of Baricitinib mean that it could potentially slow down the process of the virus being taken up into cells and reduce its ability to infect lung cells. More research and human trials will be needed to see whether the drug has the effects AI predicts.

Shedding light on the structure of COVID-19

Human epidemiologists at ProMed, an infectious-disease-reporting group, published their own alert just half an hour after HealthMap, and Brownstein also acknowledged the importance of human virologists in studying the spread of the outbreak. 
"What we quickly realised was that as much it's easy to scrape the web to create a really detailed line list of cases around the world, you need an army of people, it can't just be done through machine learning and webscraping," he said. HealthMap also drew on the expertise of researchers from universities across the world, using "official and unofficial sources" to feed into the line list
The data generated by HealthMap has been made public, to be combed through by scientists and researchers looking for links between the disease and certain populations, as well as containment measures. The data has already been combined with data on human movements, gleaned from Baidu, to see how population mobility and control measures affected the spread of the virus in China. 
HealthMap has continued to track the spread of coronavirus throughout the outbreak, visualising its spread across the world by time and location

Spotting signs of a COVID-19 infection in medical images

Canadian startup DarwinAI has developed a neural network that can screen X-rays for signs of COVID-19 infection. While using swabs from patients is the default for testing for coronavirus, analysing chest X-rays could offer an alternative to hospitals that don't have enough staff or testing kits to process all their patients quickly.
DarwinAI released COVID-Net as an open-source system, and "the response has just been overwhelming", says DarwinAI CEO Sheldon Fernandez. More datasets of X-rays were contributed to train the system, which has now learnt from over 17,000 images, while researchers from Indonesia, Turkey, India and other countries are all now working on COVID-19. "Once you put it out there, you have 100 eyes on it very quickly, and they'll very quickly give you some low-hanging fruit on ways to make it better," Fernandez said.
The company is now working on turning COVID-Net from a technical implementation to a system that can be used by healthcare workers. It's also now developing a neural network for risk-stratifying patients that have contracted COVID-19 as a way of separating those with the virus who might be better suited to recovering at home in self-isolation, and those who would be better coming into hospital. 

Monitoring how the virus and lockdown is affecting mental health

Johannes Eichstaedt, assistant professor in Stanford University's department of psychology, has been examining Twitter posts to estimate how COVID-19, and the changes that it's brought to the way we live our lives, is affecting our mental health. 
Using AI-driven text analysis, Eichstaedt queried over two million tweets hashtagged with COVID-related terms during February and March, and combined it with other datasets on relevant factors including the number of cases, deaths, demographics and more, to illuminate the virus' effects on mental health.
The analysis showed that much of the COVID-19-related chat in urban areas was centred on adapting to living with, and preventing the spread of, the infection. Rural areas discussed adapting far less, which the psychologist attributed to the relative prevalence of the disease in urban areas compared to rural, meaning those in the country have had less exposure to the disease and its consequences.
There are also differences in how the young and old are discussing COVID-19. "In older counties across the US, there's talk about Trump and the economic impact, whereas in young counties, it's much more problem-focused coping; the one language cluster that stand out there is that in counties that are younger, people talk about washing their hands," Eichstaedt said.
"We really need to measure the wellbeing impact of COVID-19, and we very quickly need to think about scalable mental healthcare and now is the time to mobilise resources to make that happen," Eichstaedt told the Stanford virtual conference. 

Forecasting how coronavirus cases and deaths will spread across cities – and why

Google-owned machine-learning community Kaggle is setting a number of COVID-19-related challenges to its members, including forecasting the number of cases and fatalities by city as a way of identifying exactly why some places are hit worse than others. 

"The goal here isn't to build another epidemiological model… there are lots of good epidemiological models out there. Actually, the reason we have launched this challenge is to encourage our community to play with the data and try and pick apart the factors that are driving difference in transmission rates across cities," Kaggle's CEO Anthony Goldbloom told the Stanford conference.
Currently, the community is working on a dataset of infections in 163 countries from two months of this year to develop models and interrogate the data for factors that predict spread. 
Most of the community's models have been producing feature-importance plots to show which elements may be contributing to the differences in cases and fatalities. So far, said Goldbloom, latitude and longitude are showing up as having a bearing on COVID-19 spread. The next generation of machine-learning-driven feature-importance plots will tease out the real reasons for geographical variances. 
"It's not the country that is the reason that transmission rates are different in different countries; rather, it's the policies in that country, or it's the cultural norms around hugging and kissing, or it's the temperature. We expect that as people iterate on their models, they'll bring in more granular datasets and we'll start to see these variable-importance plots becoming much more interesting and starting to pick apart the most important factors driving differences in transmission rates across different cities. This is one to watch," Goldbloom added.

~ Jai Krishna Ponnappan