New Zealand tests Covid-19 blast detection technology at border

New Zealand border guards have started testing an app to detect Covid-19 before the first symptoms of the disease known as noise appear. The platform was developed by the artificial intelligence company Datamine (AI) and is connected to smartwatches and other wearable devices to measure values ​​such as heart rate, temperature or oxygen saturation.

According to Datamin, the app can detect the warning signs of Covid-19 up to three days before symptoms appear with an accuracy of up to 90%. The app creates a custom baseline for each user from the wearable device's data set and uses AI to detect physiological changes that can indicate the user is sick before they feel bad. The platform is not device specific and can run on many different pieces of hardware.

Up to 500 border guards can volunteer to participate in the process, which will run until early May, to see how the platform works in real-world environments. Given that new cases of Covid-19 in New Zealand are only noticed after international travelers arrive, cross-border commuters are likely at the greatest risk of exposure to the virus and could therefore benefit more from the warning.

“When ëlarm reaches its potential, it can notify our critical staff at our limits early on when they are improving. This means that they can take appropriate action, e.g. B. Self-isolation and testing for Covid-19, ”said the New Zealand MP. Health Director Shane Hunter.

Can a platform like ëlarm really work?

Data from multiple studies suggests that wearable devices can actually help predict the onset of illness before it occurs. Researchers at the Rockerfeller Institute of Neuroscience report that Aura RingIt's data is a wearable device for tracking sleep and activity that can be combined with an app that measures vital signs to predict the occurrence of Covid-19 symptoms . They found that the device was successful in predicting symptoms such as cough, fever, and shortness of breath for up to three days before they occurred.

Initial results from the Scripps Research Translations Institute's DETECT study found wearable fitness equipment can boost public health efforts to control Covid-19. DETECT researchers report that assessing changes in metrics such as heart rate, sleep, and activity level along with self-reported symptom data can help identify more successful cases than just observing symptoms alone.

“Early identification of people who are asymptomatic or even asymptomatic will be especially helpful as people are likely to be more contagious during this time. This is the ultimate goal, ”said Giorgio Coyer, Director of the Scripps Institute for Translational Research in Artificial Intelligence. .

Individual cases can affect data from mobile devices

A study published in January 2020 published a Lancet Digital Health Evaluation on the use of resting heart rate and sleep data from wearable devices to improve government surveillance of influenza-like diseases (ILI) in the United States. Using unspecified sensor data from 200,000 Fitbit users, the researchers found a strong correlation between measurements of abnormal data and weekly suspected flu rates.

They argued that this information could be critical to implementing timely outbreak response measures to prevent further transmission of influenza cases during seasonal outbreaks. If government agencies had access to these metrics, they could detect outbreaks of infectious respiratory disease, including Covid-19, before they spread dramatically.

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Implementing such a directive as a public health directive naturally raises the usual questions about patient privacy. If such a scheme were implemented, the data would have to be completely anonymous and used only with the consent of the user of the mobile device. However, if the right conditions are met and technologies like ëlarm continue to be tested to produce positive results, wearable devices could play an important role in the future of infection control.

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