Artificial intelligence, human brain and corona virus

The last 48 hours of 2019 were a critical moment in which the scope of the new virus was understood.

On December 30, the doctor at Wuhan Central Hospital Li Wenliang warned his friends about the virus on a social network, an attitude for which he was interrogated for

Did artificial intelligence knockdown the human brain by predicting a severe outbreak of coronavirus in China ?

But while humans perhaps did not do it with the same speed, they compensated for this with certain attitudes.

Early detection of an outbreak can help save lives.

At the end of 2019, a Boston artificial intelligence (AI) system issued the first global alert about an outbreak of a virus in China.

But it was human intelligence that realized the magnitude of the outbreak and sought answers from the medical community.

What’s more, mere mortals issued a similar alert just half an hour later than AI systems.

For now, AI disease alert systems seem more like car alarms: They make a noise about anything and are sometimes ignored.

A network of medical experts and detectives must analyze more material to get an accurate idea of what happened.

It is hard to say what impact the AI systems of the future can have, fueled by increasingly large databases, in terms of disease outbreaks.

The first public alert outside China about the novel coronavirus arrived on December 30, from the automated HealthMap system at Boston Children’s Hospital.

At 11.12 p.m., HealthMa issued an alert about unidentified pneumonia in the Chinese city of Wuhan .

The system, which analyzes online news and social media reports, gave its alert a category of three on a scale of five.

It took HealthMap researchers several days to realize the severity of the outbreak.

Four hours before the HealthMap alert, the New York epidemiologist Marjorie Pollack had started working on her own alert, motivated by a personal email she had received shortly before.

“This is being distributed through the internet here,” wrote his contact, who reprinted a post on a Pincong internet forum.

The post spoke of a warning from the body that manages health in Wuhan and said: Unexplained pneumonia?

Pollack, who is deputy director of the Program for the Monitoring of New Diseases, run by volunteers and is known as ProMed, promptly mobilized a team to analyze the matter.

A more detailed ProMed report circulated about 30 minutes after the brief HealthMap alert.

Emergency detection systems that analyze social networks, news on the internet and government reports for signs of infectious disease outbreaks help inform international agencies such as the World Health Organization

, allowing experts take the bull by the antlers early without tripping over bureaucratic and language obstacles.

Some systems, including ProMed, take advantage of the human experience.

And more than competing with each other, they often complement each other, as is the case with HealthMap and ProMed.

Li, who died on February 7 following the virus, told The New York Times that it would have been better if the authorities offered information about the epidemic before.

“They should be more open and transparent,” he said.

The effectiveness of the algorithms depends on the information they collect, said Nita Madhav, CEO of the San Francisco Metabiota disease monitoring company.

Madhav said that inconsistencies in the way each agency distributes medical information can affect algorithms and that to avoid confusion there is almost always a human being involved in the

Scientists are using databases to determine possible disease transmission routes.

The last 48 hours of 2019 were a critical moment in which the scope of the new virus was understood.

On December 30, the doctor at Wuhan Central Hospital Li Wenliang warned his friends about the virus on a social network, an attitude for which he was interrogated for

In early January, Isaac Bogoch, an infectious disease doctor and researcher at Toronto General Hospital, analyzed commercial flight information with Kamran Khan, founder of BlueDot, to see which cities outside

But by then 5 million people had escaped from the city, the mayor admitted.

“We showed that the most common destinations were Thailand, Japan and Hong Kong,” Bogoch said.

“It turns out that a few days later we started seeing cases in those places.”

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