South Korea’s telecoms company KT Corporation has announced a three-year research study funded by the Bill & Melinda Gates Foundation for information and communication technology (ICT) based global epidemic response using artificial intelligence (AI) and big data
The US$10mn project stems from the recognition of South Korea’s advanced ICT as well as its proven ability to cope with the ongoing coronavirus pandemic.
The three-year research collaboration builds on discussions between the Gates Foundation and KT in 2019, and the research will be carried out in South Korea, which has a high penetration rate for mobile phones and 5G infrastructure.
Jeon Hongbeom, KT’s head of AI/DX Business Unit, said, “With this contribution from the Gates Foundation, KT hopes to enhance our opportunity to lead the global effort toward outbreak prevention and response by taking advantage of AI and Big Data.”
With funding from the Gates Foundation, KT will develop big-data algorithms to improve AI-based early diagnosis during an epidemic and prediction of viral infections spread using mobile data.
Andrew Trister, deputy director of digital health innovation at the Bill & Melinda Gates Foundation, said, “The use of mobile technology and sensors paired with smart data analysis can help address some of the challenges countries face in a timely and effective response to disease outbreaks. Knowing where a disease is moving and being able to predict spread can help save time and save lives.”
The project calls on KT to first develop a mobile app to input symptoms typically associated with viral infections automatically. Among the tools will be an Internet of Things ( IoT) body temperature sensor. Symptoms and body temperature will be analysed with AI to create an algorithm that will calculate the probability of infection.
In addition, KT will use mobile data to examine human mobility patterns and epidemic regions to determine the direction of the spread of infection. At the same time, it will analyse region-by-region virus trends and develop predictive models for each region to predict seasonal outbreak.
