Uncovering Covid-19 transmission tree

Identifying traced and untraced infections in an infection network

Bibliographic Data

ID22073252
AuthorsHyun-Woo Lee (0000-0002-1022-0264, Kyungpook National University), Hyunwoo Lee (0000-0002-1736-560X), Hayoung Choi (0000-0002-8482-4327, Kyungpook National University, corresponding author), Hyojung Lee (0000-0002-0471-6650, Kyungpook National University), Sunmi Lee (0000-0003-1126-0660, Kyungpook National University), Changhoon Kim (0000-0001-5971-863X, Pusan National University Hospital)
Year2024
Volume12
Pages1362823-1362823
Publication date2024-06-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1362823
PMID38887240
OpenAlexW4399297428
LanguageEN
Citations received1
References cited45

Introduction: This paper presents 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. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance. Methods: To address this challenge, this article 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. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. Results: 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 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. Furthermore, 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. Conclusion: This study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions

Biology · Data science · Disease · Pandemic · Pathology · Telecommunications · Complex Network Analysis Techniques · Computer Science · COVID-19 epidemiological studies · Medicine · Zoonotic diseases and public health · Epidemiology