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In less than two months, the coronavirus has spread from the Chinese city of Wuhan to 25 countries, prompting the World Health Organization to declare a global health emergency. In reaction, the Chinese Communist Party swiftly restricted travel and shut down major transportation systems a feat many believe possible due to the authoritarian control the government has over its citizens.

While these efforts are aggressive and undemocratic, they fall within the scope of fairly well-known epidemic planning. However, many new data-driven techniques that could have stemmed, or at least slowed, the spread of the coronavirus has not been deployed. One reason for this is these methods rely on crowd-sourcing data, which to be accurate, often depends on public trust in the governments use of citizen information. Something absent in China.

For example, New York University professor Daniel B. Neill, and MIT Lincoln Labs researcher, Mallory Noble, have developed a new tool called pre-syndromic surveillance that uses machine learning to comb through the de-identified emergency room and social media data to discover outbreaks that do not correspond with known illnesses. Recently, the team successfully piloted the technique in New York Citys Department of Health and Mental Hygiene.

Neill refers to the system as a public health safety net because by aggregating and analyzing the actual description of new symptoms from patients, first responders, and citizens, they are better able to identify and manage new disease patterns.

Traditional epidemiologists rely on past cases and patterns to make public health decisions, but some new diseases behave differently, comments Neill. This technique allows us to identify and respond to something new.

Similarly, tools like Flu Near You and Pandemic Pulse developed by Boston Childrens Hospital integrate Twitter and Google Search data to detect biothreats. Like Neill and Nobles pre-syndromic surveillance approach, these tools utilize natural language processing engineering lingo that just means reading what people actually say or write down.

Both teams won the Department Homeland Securitys 2018 Hidden Signals Challenge to use public data to identify emerging biothreats.

But for any of this to work, citizens must trust these digital platforms wont be used to harm them.

If a patient believes what they tell an emergency room attendee or what they say on social media may lead to aggressive quarantines or other harmful actions by their government, its less likely they will be honest. And without reliable information, none of these new systems work.

The "New York Times" reported that Chinese authorities went to great lengths to hide the disease from the public by silencing doctors and others for raising red flags and even closed a food market thought to be the source of the outbreak but told residents it was due to renovations.

While those activities surely slowed authorities ability to work with citizens to respond to the disease, the new machine learning techniques outlined by Neill and others work illuminates an even more important flaw in the Chinese strategy the possibility that some new technologies may be democracy-oriented in that they simply work better in open societies.

At a minimum, the demand for trust and transparency with citizen data represents an irony for the Chinese Communist Party. The governments expansive powers are often seen as a scary but effective vehicle to monitor citizens, such as through new facial recognition technologies.

But there is another way to look at it: If advances in crowd-sourced, citizen communication depend on accurate and complete information, which in turn relies on public trust, then places like China will struggle to fully take advantage of these technologies.

The coronavirus spread so quickly because it is a new virus that behaves differently than what weve seen before and known methods of disease control were caught off-guard. But new crowd-sourced technologies can help us adapt quicker. Lack of public trust hurts those efforts.

Coronavirus is the latest global epidemic, but it wont be the last. The lack of greater trust and transparency from the government of the worlds most populated country risks lives in China and across the globe.

Scott Andes is the executive director of theBlock Center for Technology and Societyat Carnegie Mellon University. Scott was a Fellow at the Brookings Institution and his research focuses on the economic and social impact of new technology.

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Public trust in data could have helped China contain the coronavirus | TheHill - The Hill