To examine outcomes from respiratory pathogens containment strategies focused on international travellers.
We developed a compartmental model generalisable to respiratory infectious diseases, in which international travellers interact with each other and airline/airport workers during transit. We used SARS-CoV-2 Omicron surge data (basic reproduction number (R0): 9.5) as a case example and performed sensitivity and scenario analyses, including varying the R0 for different respiratory pathogens.
A US high-volume airport.
Simulated international travellers and airline/airport workers.
Projection of new and imported SARS-CoV-2 infections without intervention (No Intervention); pre-travel screening for travellers who intend to travel (intended travellers) with PCR (Pre-travel PCR); or antigen testing (Pre-travel Ag); mask-wearing guidance for travellers and workers (Mask-wearing); and a Combined strategy (Pre-travel PCR & Mask-wearing).
The number of new and imported respiratory disease infections over the 90-day simulation period.
Over the 90-day simulation, the number of infected travellers entering the USA would be: 1 155 580 (27.2% of 4.2 million (M) intended travellers) with No Intervention; 709 560/4.2M (16.7%) with Pre-travel PCR; 862 330/4.2M (20.3%) with Pre-travel Ag; 1033 820/4.2M (24.4%) with Mask-wearing; and 650 480/4.2M (15.3%) with Combined. The number of new infections among airline/airport workers would be: 25 670 (73.3% of 35 000 workers) with No Intervention; 25 260 (72.2%) in Pre-travel PCR; 25 590 (73.1%) in Pre-travel Ag; 24 630 (70.4%) in Mask-wearing; and 18 770 (53.6%) in Combined. In scenario analyses, the most impactful parameters were R0 of the respiratory pathogen and population immunity level.
A Combined strategy of pre-travel PCR testing and mask-wearing would most effectively reduce respiratory infection among international travellers and airline/airport workers, but would still allow a substantial number of infections to enter the USA, especially when the pathogen is highly transmissible.