COVID-19 transmission dynamics: age-specific patterns and super spreaders in South Korea

The global spread of COVID-19 has underscored the vital need for precise tracing of individuals involved in transmitting the virus through contact tracing. It is crucial to identify both those who spread the virus (infectors) and those who contract it (infectees) in order to gain a detailed understanding of the transmission dynamics.

We investigate age-specific infection patterns and their impacts by analyzing a unique dataset of the COVID-19 infection network in South Korea. In particular, we derive degree-based distributions from infection tree networks reconstructed through contact tracing of secondary infections, thereby identifying instances of Super Spreading Events (SSEs). Furthermore, we thoroughly examined age-specific infection networks using various metrics such as edge counting, entropy, density, and centrality.

Our findings reveal systematic variations in age-specific COVID-19 patterns in South Korea. In particular, individuals aged 50 to 64 exhibit the highest centrality and number of connections within the COVID-19 infection network, underscoring their pivotal role in transmission dynamics. Moreover, the 20-49 age group consistently exhibited the lowest proportion of asymptomatic cases, in contrast to the higher rates observed among the 0-19 and 65≤ age groups. These results suggest that age groups differ not only in their structural positions within the transmission network but also in their clinical presentation, both of which are crucial for understanding the spread of infection.

Analyzing age-specific infection networks is vital for clarifying transmission patterns and susceptibility across different age groups. Our study provides valuable evidence for identifying vulnerable populations and key cohorts that drive dissemination within the infection network. These insights can directly inform policy formulation, particularly in prioritizing vaccination strategies and tailoring age-targeted prevention measures. More broadly, this network-based approach offers a framework for designing effective control strategies against future emerging infectious diseases.