Uncovering COVID-19 Transmission Tree: Identifying Traced and Untraced Infections in an Infection Network

We present a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. This network, illustrating infector-infectee relationships, provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder the conventional analysis of such networks from epidemiological surveillance. To address this challenge, our research suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. Furthermore, the study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings of this study is the identification of notable transmission patterns in specific age groups, particularly in those aged 20–29, 40–69, and 0–9, based on the four type classifications. Moreover, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities.