KT Shares Achievements of COVID-19 Spread Prediction Alliance

[Asia Economy Reporter Koo Chae-eun] KT announced on the 2nd that it has concluded the first phase of activities for the ‘Novel Coronavirus Infection (COVID-19) Spread Prediction Research Alliance’ and plans to continuously supplement and collaborate to utilize the research outcomes in the COVID-19 quarantine system.

The COVID-19 Spread Prediction Research Alliance was launched last March as a consultative body jointly established by KT, the Ministry of Science and ICT, and the Korea Data Agency to respond collectively to the rapid spread of COVID-19. Participants included ▲ Seoul National University ▲ Konkuk University ▲ Korea Advanced Institute of Science and Technology (KAIST) ▲ Korea Institute of Science and Technology (KIST) ▲ Korea Institute of Science and Technology Information (KISTI) ▲ Hanyang University-National Institute for Mathematical Sciences (NIMS).

KT provided its floating population and roaming statistical data and conducted research with participating institutions to predict the domestic inflow and regional spread of COVID-19 using technologies such as artificial intelligence (AI), big data, and mathematical modeling. The COVID-19 spread prediction research conducted in this alliance was mainly divided into two areas: ‘Prediction of the risk of infectious disease inflow from overseas’ and ‘Prediction of domestic spread areas.’

First, to predict the risk of domestic inflow when an infectious disease breaks out overseas, ▲ KT AI/BigData Service Division used machine reading technology to extract overseas infectious disease inflow predictions from global COVID-19 related news articles ▲ KISTI’s team led by Director Ahn In-sung presented ‘Prediction of confirmed COVID-19 cases inflowing from overseas’ ▲ Seoul National University’s Professor Hwang Eung-soo’s team presented ‘Estimation of unidentified overseas inflow patients in Seoul’ ▲ Hanyang University-NIMS’s Dr. Son Woo-sik introduced research results on ‘Prediction of foreign infected persons’ entry into Korea.’

Additionally, to predict domestic spread areas of infectious diseases, ▲ Konkuk University’s Professor Jung Eun-ok’s team shared ‘Analysis of changes in the reproduction number and floating population according to the government’s non-pharmaceutical intervention policies’ ▲ KAIST’s Professor Park Joo-yong’s team presented ‘COVID-19 transmission route network analysis’ ▲ KAIST’s Professor Hwang Ui-jong’s team shared ‘Floating population reduction analysis’ ▲ KAIST’s Professor Yoon Se-young’s team introduced ‘Infectious disease spread prediction and policy evaluation model’ ▲ KAIST’s Professor Lee Jae-gil’s team shared ‘Individual-level COVID-19 risk assessment model’ research results.

KT and the Ministry of Science and ICT plan to continuously supplement and advance the research outcomes to be utilized in the COVID-19 quarantine system, and based on the know-how gained from operating the first phase of the research alliance, they plan to launch the ‘COVID-19 Spread Prediction Research Alliance Phase 2’ by expanding research institutions and research scope. The research, conducted over four months, took place in the ‘Data Safe Zone’ operated by the Ministry of Science and ICT. The Data Safe Zone is a data utilization environment within the Korea Data Agency (K-DATA) that allows sensitive data from public institutions and private companies to be safely used for research and analysis under secure protection. This maximized synergy among participating institutions in the COVID-19 Spread Prediction Research Alliance and supported a smooth research environment.

Kim Chae-hee, Executive Director of KT AI/BigData Business Division, said, “We plan to consult with the government to utilize the research outcomes obtained through the first phase of the spread prediction research alliance to strengthen K-quarantine,” adding, “In addition, KT will continue to advance the Global Epidemic Prevention Platform (GEPP), which is being promoted for global expansion, contributing to minimizing global pandemics caused by infectious diseases.”

This content was produced with the assistance of AI translation services.