AI Can Give An Early Warning Of Coronavirus

As the deadly 2019-nCov coronavirus spreads, researchers are using Artificial Intelligence and other technologies to predict where the virus might appear next. These new technological virus-fighting initiatives have gone further than simply tracking existing outbreaks. 

BlueDot, a Toronto-based health surveillance company launched in 2014, gathers disease data from myriad online sources, then uses airline flight information to make predictions about where infectious diseases may appear next.  Air routes, after all, are a common disease vector.  

Scientists are still working to determine how infectious the virus is.  Current analysis suggests it may be somewhere between influenza and polio. UK and US-based researchers have published a preliminary paper estimating that the confirmed infected people in Wuhan only represent 5% of those who are actually infected. 

If the models are correct, 190,000 people in Wuhan will be infected by now, major Chinese cities are on the cusp of large-scale outbreaks and the virus will continue to spread to other countries.

BlueDot’s AI algorithm, a type of computer program that improves as it processes more data, brings together news stories in dozens of languages, reports from plant and animal disease tracking networks and airline ticketing data. The result is an algorithm that’s better at simulating disease spread than algorithms that rely on public health data, better enough to be able to predict outbreaks.

The company uses the technology to predict and track infectious diseases for its government and private sector customers.

Traditional epidemiology tracks where and when people contract a disease to identify the source of the outbreak and which populations are most at risk. AI systems like BlueDot’s model how diseases spread in populations, which makes it possible to predict where outbreaks will occur and forecast how far and fast diseases will spread. AI is not a silver bullet. The accuracy of AI systems is highly dependent on the amount and quality of the data they learn from. And how AI systems are designed and trained can raise ethical issues, which can be particularly troublesome when the technologies affect large swathes of a population about something as vital as public health.

More recent efforts using AI and data science have expanded to include many different data sources, which makes it possible to make predictions about outbreaks.

With the advent of Facebook, Twitter and other social and micro media sites, more and more data can be associated with a location and mined for knowledge about an event like an outbreak. Much of this data is highly unstructured, meaning that computers can’t easily understand it. The unstructured data can be in the form of news stories, flight maps, messages on social media, check ins from individuals, video and images. On the other hand, structured data, such as numbers of reported cases by location, is more tabulated and generally doesn’t need as much preprocessing for computers to be able to interpret it.

Newer techniques such as deep learning can help make sense of unstructured data. These algorithms run on artificial neural networks, which consist of thousands of small interconnected processors, much like the neurons in the brain. 

The processors are arranged in layers, and data is evaluated at each layer and either discarded or passed onto the next layer. By cycling data through the layers in a feedback loop, a deep learning algorithm learns how to, for example, identify cats in YouTube videos.

Researchers teach deep learning algorithms to understand unstructured data by training them to recognise the components of particular types of items. 

For example, researchers can teach an algorithm to recognise a cup by training it with images of several types of handles and rims. That way it can recognise multiple types of cups, not just cups that have a particular set of characteristics. Any AI model is only as good as the data used to train it. Data quality is critical. It can be particularly challenging to control the quality of unstructured data, including crowd-sourced data. This requires researchers to carefully filter the data before feeding it to their models. 

AI holds great promise for identifying where and how fast diseases are spreading. And AI is a tool to provide more advanced and more accurate warnings that can enable a rapid response to an outbreak. However, AI doesn’t eliminate the need for epidemiologists and virologists who are fighting the spread on the front lines. For example, BlueDot uses epidemiologists to confirm its algorithm’s results.  

In the case of AI models collating social media data, digital redlining can exclude entire populations with limited Internet access. These populations might not be posting to social media or otherwise creating the digital fingerprints many AI models rely on. This could lead AI systems to make flawed recommendations about where resources are needed.

While researchers are continuously creating new AI algorithms, some of the foundational issues like understanding what’s going on inside the models, minimising false positives and identifying and avoiding ethical issues are not well understood and require more research.

AI is a powerful tool for predicting and forecasting disease spread. However, it’s not likely to completely replace the tried-and-true combination of statistics and epidemiology. 

Meanwhile, understanding a virus doesn’t necessarily mean we’ll be able to stop it from spreading, Ebola was known for decades before an outbreak killed more than 11,000 people in western Africa between 2014 and 2016.  Advocates of these cutting-edge efforts argue that it’s wiser to prevent outbreaks in the first place rather than fighting them after they start. 

BlueDot was able to predict that the virus was at risk of spreading from Wuhan to Bangkok, Taipei, Singapore, Tokyo and Hong Kong. All of those places have since reported cases of the novel coronavirus, which has killed 2,000 people, almost all of them in China. 

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