Zheng Wei, who is in charge of a property management company in the eastern Chinese city of Hangzhou, just received a text message.

It read that there was a householder, whose place of residence is registered in virus-hit Hubei Province, just used his access card at the entrance of his apartment building.

“He might have just returned home from Hubei, so I have to go and check if he is feeling alright or needs some help,” Zheng said.

The message was automatically sent by a real-time pre-warning system that enables data exchange between the intelligent security system of the residential compound with Hangzhou’s “City Brain,” an AI-powered platform developed by Alibaba which aims at improving city management.

Data from nearly 80 government agencies and businesses run into Hangzhou’s City Brain every day. The city promptly launched a prevention and control system using the City Brain in the wake of the novel coronavirus epidemic.

By cooperating with the three major mobile operators, the newly launched system can send notices not only to Hubei natives in Hangzhou but citizens or travelers who recently visited Wuhan, reminding them to take quarantine measures as soon as they arrive in Hangzhou.

A greater challenge to the anti-virus campaign lies ahead with a larger flow of personnel as the New Year holiday ends and work resumes.

The National Health Commission Monday said it received reports of 3,062 new confirmed cases of novel coronavirus infection on Sunday from 31 provincial-level regions and the Xinjiang Production and Construction Corps, bringing the overall confirmed cases on the Chinese mainland to 40,171 by the end of Sunday.

Fortunately, many Chinese cities are busy building defensive lines using big data and AI technologies.

Pudong New Area in Shanghai obtains accurate positions of individuals who are under observation and is able to carry out dynamic management of their movements using data from telecommunication base stations and GPS.

A tech firm in Chengdu, capital of southwest China’s Sichuan Province, collected public data and developed a search app whereby users can check whether they have traveled on the same flight as an infected passenger.

Xiangshan County of Ningbo in coastal Zhejiang Province initiated a big data system that collects the number of workers that have returned to work and their physical conditions reported by local enterprises and private businesses.

“Using big data and AI is a race against time in the epidemic fight, and we need to find high-risk groups before they get infected,” said Kong Wanfeng, a Hangzhou government official.

The post Big data, AI build defensive line amid virus outbreak appeared first on Artificial Intelligence.

An AI-driven health monitoring and disease detection platform was able to catch the signs of the Wuhan viral outbreak approximately a week before government agencies warned the public, providing a look at how AI can be used to catch disease outbreaks in a timely fashion.

While the official World Health Organization notification of the Wuhan virus went out on January ninth and the US Center for Disease Control and Prevention (CDC) received word of the outbreak on January sixth, the first warning signs of the outbreak were picked up by a Canadian health monitoring system almost a week prior. As Wired reported, the AI-driven health system BlueDot warned its clients about the possible outbreak on December 31st. Bluedot uses AI algorithms to monitor different global news sources and detect patterns in health reports. It also takes into account information on plant and animal disease networks. Using the information it collects, BlueDot epidemiologists then delivers warnings and predictions about possible health risks and outbreaks to its subscribers.

When dealing with an outbreak of disease, early detection is always better. The earlier the detection, the more time health officials have to respond. In the case of the Wuhan virus and other disease outbreaks in China, the Chinese government has often been slow in sharing information with global public health officials. This possesses a problem as the CDC and WHO rely on communications from other government agencies to plan their own responses. However, if an AI system like BlueDot can make accurate predictions based on the information that leaks through across many individual news reports, blogs, and forums, this could potentially enable health organizations to act quicker in response to outbreaks.

According to Kamran Khan, the found of BlueDot, the company doesn’t use social media data when predicting the spread of diseases because the data is too variable and messy to be of use. Instead, news reports, data on known animal disease networks, and airline ticketing data is combined to create a model that predicts where infections begin and where infected people may travel next. BlueDot was correctly able to predict that the Wuhan virus would spread to Taipei, Tokyo, Seoul, and Bangkok within a few days of its manifestation.

BlueDot was launched by Khan in 2014, and the company currently has 40 employees, including data scientists, physicians, and programmers who work together to create the disease surveillance and prediction models. Machine learning algorithms and natural language processing techniques are used to mine data from news reports spanning the globe and covering 65 different languages. Khan said to Wired:

“What we have done is use natural language processing and machine learning to train this engine to recognize whether this is an outbreak of anthrax in Mongolia versus a reunion of the heavy metal band Anthrax.”

After the automated data collection and initial analysis are complete, human analysts double-check the data and ensure that the model’s conclusions seem sound. Finally, a report is generated and sent out to the clients of the application.

BlueDot’s system is far from the first attempt by the AI field to predict the spread of diseases. Data scientists have been using big data and machine learning models to track the spread of various diseases like for some time now, with some attempts being more successful than others. Google tried its own hand at tracking the spread of disease with Google Flu Trends, but its attempts to predict the severity of the 2013 flu seasons were reportedly off by about 140%. Only time will tell if BlueDot can consistently predict the spread of diseases, but if it can it could pave the way for faster, more accurate estimates of disease outbreaks.

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