Mobile and AI surveillance systems to help control the virus spread

Mobile and AI surveillance systems to help health professionals control the virus spread

Mobile and artificial intelligence AI surveillance systems have proven to be effective for this purpose. Device-based contact tracing, wearables, computer vision systems, and facial recognition CCTV cameras are just a few examples of those in-demand AI surveillance technologies.

In 2020 billions of people in the world have become in the thick of the coronavirus (COVID-19) pandemic, and local governments in many cities started deploying their own methods of disease tracking and prevention. It’s important for everyone to understand how the local government and healthcare professionals in your city can use mobile and AI surveillance solutions to track the spread of the Coronavirus disease and help their citizens stay healthy.

Contact tracing and mobile data

In public health, contact tracing remains one of the fastest-growing disease surveillance practices. According to the CDC website, this practice provides the basis for ongoing virus control and prevention in the United States and is employed by local and state health department personnel for decades.

Interviewing infected patients is considered as the basic manual method of contact tracing. A trained public health professional speaks with an ill patient about individuals (including family members and colleagues) the patient has been in contact with over a given period (2-3 weeks for COVID-19/Coronavirus cases). Also, the interviewer provides education and support, while not revealing the identity of the original patient. This careful method has proven its efficiency in the past fifteen years.

In a global pandemic like COVID-19, however, that method of manual contact tracing cannot keep pace. Automated systems and AI can help address this issue at scale. An intelligent mobile-device-based method of contact tracing (usually via a smartphone or tablet) comes into play.

Mobile device-based contact tracing involves using a specialized app and the data from our smartphones/tablets to tell public health professionals about who has been in contact with whom — in a supermarket, on a bus station, even if it’s just a casual passing in the street — and to notify everyone who’s been exposed to coronavirus-infected people.

Contact tracing and mobile data Ai surveillance

Mobile tracker apps and wearables

Electronic wristbands have been long used in military and law enforcement, and since 2020 this surveillance method has been widely being adopted in the medical field to track specific individuals – Covid-19 patients.

GPS-enabled ankle monitors are paired with specialized tracker apps that are installed on patients’ mobile devices (smartphone, tablet) or wearables (smart watch, wristband) to let healthcare professionals to specifically identify the patient and track his/her movements in real-time. Those devices are equipped with an AI-enabled surveillance system to track the patient’s vitals and alert about a health crisis.

However, using mobile and AI technology for public health surveillance is not a big jump to track people under the COVID-19 quarantine. In some cases, AI-enabled patient surveillance systems failed to identify the person, according to a Reuters report. This technology isn’t perfect and requires continuous research and improvement to help effectively track and control the virus spread.

COVID-19 surveillance via AI technology

Remote monitoring of coronavirus patients via AI surveillance systems offers a potentially more attractive solution – the concept of an AI-powered home monitoring system, for example. This sort of virus tracking could help medical professionals keep an eye on the ongoing health of a positive or recovered COVID-19 patient remotely (with a layer of privacy, of course). Obviously, this technology offers a great value during quarantine – when healthcare professionals avoid unnecessary contact with vulnerable populations.

Another example is, using AI and machine learning for monitoring people in the streets. AI technology lets use mobile apps, real-time surveillance cameras, and video analytics systems to build a citywide public safety surveillance network. With this sort of AI surveillance in place, the local administration can monitor the whole city and report to the police and medical professionals about citizens willfully ignoring stay-at-home orders and quarantine – like hosting a large gathering or not wearing facemasks in essential places.

AI surveillance and data privacy

However, data privacy and abuse is the obvious concern around the use of mobile and AI surveillance. People want to be sure that their personal data is not exposed or misused by those who hold it.

In essence, surveillance experts and rights advocates debate about two different approaches to data collection and analysis: centralized or decentralized. Put simply, the centralized approach allows collecting all the data about a user/patient directly from his/her mobile device into a public health surveillance database, while in the decentralized approach the surveillance system collects and matches only anonymized identifiers and the matching is done on-device, not in the central database.

In fact, this extensive debate originates from the push/pull between local governments and private companies and how technical limitations and lack of public trust prevent mass adoption of AI and mobile surveillance systems for ongoing virus control.

But even with these pressing problems, the urgent need to control the virus spread means other methods of AI-powered contact tracing (like drones, facial recognition surveillance systems) have already been employed and proven their efficiency in the United States, Russia, China and other countries around the world.

Dmitro Bohdanov

Dmitro Bohdanov

A marketing professional with more than 7 years of combined IT marketing and business experience.

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