Open AccessA global assessment of the impact of school closure in reducing COVID-19 spread
Author(s) -
Joseph T. Wu,
Shujiang Mei,
Sihui Luo,
Kathy Leung,
Di Liu,
Qiuying Lv,
Jian Liu,
Yuan Li,
Kiesha Prem,
Mark Jit,
Jianping Weng,
Tiejian Feng,
Xueying Zheng
Publication year - 2021
Publication title -
philosophical transactions - royal society. mathematical, physical and engineering sciences/philosophical transactions - royal society. mathematical, physical and engineering sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.074
H-Index - 169
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2021.0124
Subject(s) - covid-19 , closure (psychology) , premise , transmission (telecommunications) , intervention (counseling) , medicine , pandemic , demography , infectious disease (medical specialty) , disease , outbreak , political science , sociology , virology , nursing , computer science , telecommunications , linguistics , philosophy , pathology , law
Prolonged school closure has been adopted worldwide to control COVID-19. Indeed, UN Educational, Scientific and Cultural Organization figures show that two-thirds of an academic year was lost on average worldwide due to COVID-19 school closures. Such pre-emptive implementation was predicated on the premise that school children are a core group for COVID-19 transmission. Using surveillance data from the Chinese cities of Shenzhen and Anqing together, we inferred that compared with the elderly aged 60 and over, children aged 18 and under and adults aged 19–59 were 75% and 32% less susceptible to infection, respectively. Using transmission models parametrized with synthetic contact matrices for 177 jurisdictions around the world, we showed that the lower susceptibility of school children substantially limited the effectiveness of school closure in reducing COVID-19 transmissibility. Our results, together with recent findings that clinical severity of COVID-19 in children is lower, suggest that school closure may not be ideal as a sustained, primary intervention for controlling COVID-19. This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.