Back

Unseen but Present: Asymptomatic COVID-19 Cases and Air Travel to Hong Kong

Yu, W.; Liu, H.; Bazira, D.; Ratnarajah, D.; Mane, H.; Nguyen, T. T.; Alipio, C.; He, X.; Hutsul, Y.; Chen, J.; Nguyen, Q. C.

2025-03-27 infectious diseases
10.1101/2025.03.26.25324137 medRxiv
Show abstract

The global spread of infectious diseases was influenced by human movement dynamics, particularly for highly transmissible diseases like COVID-19. Asymptomatic COVID-19 cases lacked symptoms before diagnosis, posing a challenge for containment. Their contribution to air travel remains understudied. This retrospective cross-sectional study investigated the role of asymptomatic COVID-19 cases in air travel and their impact on the global spread of the virus. Through our analysis of 11,775 COVID-19 cases in Hong Kong (January 2020-April 2021), log-binomial regression models assessed the association between asymptomatic status and air travel behavior 14 days before diagnosis. The Wilcoxon rank-sum test compared median flight durations between asymptomatic and symptomatic cases. Results revealed two-thirds of cases with air travel history were asymptomatic, with asymptomatic airport or flight crew ten times more likely to travel than symptomatic counterparts (adjusted PRR=10, 95% CI: 4.00-25.00). For non-crew individuals, the adjusted PRR was 1.14 (95% CI: 1.12-1.16). Median flight duration for asymptomatic cases was 4.6 person-hours shorter than symptomatic ones (p<0.01). These findings highlight the significant contribution of asymptomatic cases to air travel and suggest under-detection during initial travel restrictions. Our study emphasizes proactive public health measures early in pandemics involving airborne infections, irrespective of symptom presentation.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.