Propelling fast responses during the pandemic: Data, AI, and machine learning in a time of crisis

In the middle of an unpredictable pandemic, more than ever, we need to use our collective knowledge and expertise to augment human ability to manage the response to COVID-19. Because of the dominance of the ongoing pandemic in our collective consciousness, I’ll get straight to the point: this article must turn to the current and potential use of robotics, artificial intelligence (AI), and machine learning in augmenting human ability in the time of COVID-19.

Infectious diseases, like the COVID-19 caused by the novel coronavirus, are spread through three main vectors: air, water, and objects that we touch. Despite the surge in antibiotics and other advanced disease-fighting molecules and methods since around the mid-20th century, a few of these infectious agents have broken through modern health defences, causing epidemics or, more rarely, pandemics. When this occurs, it becomes paramount for governments and multilateral agencies to manage their response in a way that is as optimally beneficial for the well-being of their constituents as possible. A key component of this response is managing the load on health infrastructure to ensure that lives are not endangered because the public cannot access health services when they most need them.

Minimising this adverse impact should include ensuring the effective dissemination of information to avoid a panic of healthy patients rushing to get tested after receiving incorrect information. An example of how AI and Machine Learning can prove useful here is the deployment of a coronavirus self-checker bot  by the U.S. Centers for Disease Control and Prevention (CDC). AI-powered contact centre solutions can also help decrease lengthy wait-times encountered by a worried populace trying to access COVID-19 hotlines.

The ability to track cases as they proliferate is also of standout importance in curtailing a pandemic – by separating those individuals or groups who have been exposed to the coronavirus from the rest of the general population. To successfully prevent or de-escalate a health crisis, authorities need to be able to identify a patient quickly, isolate/quarantine and treat that patient and their condition post-discharge. Related interventions may include tracing and contacting friends, relatives, and co-workers who may be affected, and acting with them in accordance with the medical protocol that is in place. The use of effective data and AI techniques, when supplemented with a simple user interface, can help in this situation – and has already been of significant help during the current pandemic. ‘TraceTogether’ is an app that is a successful community-driven example. It is built on the BlueTrace protocol, designed by the Government Digital Services team at the Government Technology Agency of Singapore.

Technologies including AI, machine learning, robotics, internet of things (IoT) devices, and video analytics have come into their own during this crisis, augmenting human capabilities in truly innovative ways. The use of UBTECH's AIMBOT in China is a great example of how these technologies can be used to automate simple patient screening to free up medical specialists for more valuable work. The robots are equipped with infrared thermal imaging cameras that can carry out multi-point parallel measurement in indoor environments with a considerable number of moving people, such as hospitals, grocery stores, train stations and citizen agencies. They can measure the body temperature of multiple people simultaneously and within a large range, screen out those with a body temperature higher than 37.3 degrees Celsius, and provide an alert. The general temperature screening programs require human contact, one person at a time, and average around 10 people per minute. The robots can do this at a distance of 2.5m to 3.5m, with 15 people covered with one unit and a measuring efficiency of 200 people per minute.

We live in a digitally connected era, and we continue to generate vast amounts of data, enabling real-time situational intelligence for authorities to mitigate the burgeoning public health and economic threats. The deployment of publicly available datasets and/or application program interfaces (APIs) allows the data and AI community to release public assets like dashboards and applications for situational intelligence.

In the throes of an unpredictable pandemic, we are first and foremost a human community waiting for effective and timely intervention by the authorities to protect our loved ones and shield our futures from economic devastation. We call on the government, researchers, and businesses to record and release all the relevant data that they can to the data and AI community. This allows us to generate assets most needed in our collective time of need.

Our commitment is our time, capability, knowledge, and ethical practices in ensuring that the information reaches the decision makers and the public when most needed

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Author: Nikita Dhami
Head of AI & Machine Learning
DXC Technology Australia and New Zealand
ndhami@dxc.com
linkedin.com/in/nikitadhami/

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