Clustering and link prediction for mesoscopic COVID-19 transmission networks in Republic of Korea

Abstract

We analyze the dataset of confirmed cases of severe acute respiratory syndrome coronavirus 2 (COVID-19) in the Republic of Korea, which contains transmission information on who infected whom as well as temporal information regarding when the infection possibly occurred. We derive time series of mesoscopic transmission networks using the location and age of each individual in the dataset to see how the structure of these networks changes over time in terms of clustering and link prediction. We find that the networks are clustered to a large extent, while those without weak links could be seen as having a tree structure. It is also found that triad-based link predictability using the network structure could be improved when combined with additional information on mobility and age-stratified contact patterns. Abundant triangles in the networks can help us better understand mixing patterns of people with different locations and age groups, hence the spreading dynamics of infectious disease.