Predicting COVID-19 transmission in a student population in Seoul, South Korea, 2020-2021

As coronavirus disease 2019 (COVID-19) transmission depends on factors such as demography, comorbidity, and patterns of daily activity, a better understanding of the societal factors of the infection among students would be useful in planning prevention strategies. However, no studies to date have focused on societal factors associated with COVID-19 transmission among students.

This study aimed to characterize the factors of a student population associated with COVID-19 transmission in the metropolitan city of Seoul, South Korea.

We analyzed the epidemiological data for laboratory-confirmed (reverse transcription polymerase chain reaction) COVID-19 cases collected by the Korea Disease Control and Prevention Agency and Ministry of Education from January 2020 to October 2021. We calculated the global Moran's index, local Moran's index, and Getis-Ord's index. A spatial regression analysis was performed to identify sociodemographic predictors of COVID-19 at the district level.

The global spatial correlation estimated by Moran's index was 0.082 for the community population and 0.064 for the student population. The attack rate of adults aged 30- 59 years (P=0.049) was associated with an increased risk of COVID-19 attack rates in students, whereas the number of students per primary- (P=0.003) and middle- (P=0.030) school class was inversely associated with risk of COVID-19 attack among students.

We found that COVID-19 transmission was more attributable to the community-level burden in students than adults. We recommend that public health initiatives target initiatives that protect students from COVID-19 when the community carries a high burden of infection.