What effect might border screening have on preventing importation of COVID-19 compared with other infections? A modelling study
Abstract
The effectiveness of screening travellers during times of international disease outbreak is contentious, especially as the reduction in the risk of disease importation can be very small. Border screening typically consists of travellers being thermally scanned for signs of fever and/or completing a survey declaring any possible symptoms prior to admission to their destination country; while more thorough testing typically exists, these would generally prove more disruptive to deploy. In this paper, we describe a simple Monte Carlo based model that incorporates the epidemiology of COVID-19 to investigate the potential benefit of requiring all travellers to undergo thorough screening upon arrival. This is a purely theoretical study to investigate whether a single test at point of entry might ever prove to be a way of significantly decreasing risk of importation. We therefore assume ideal conditions such as 100% compliance among travellers and the use of a “perfect” test. In addition to COVID-19, we also apply the presented model to simulated outbreaks of Influenza, SARS and Ebola for comparison. Our model only considers screening implemented at airports, being the predominant method of international travel. Primary results showed that in the best-case scenario, screening may expect to detect 8.8% of travellers infected with COVID-19, compared to 34.8.%, 9.7% and 3.0% for travellers infected with influenza, SARS and Ebola respectively. While results appear to indicate that screening is more effective at preventing disease ingress when the disease in question has a shorter average incubation period, our results indicate that screening alone does not represent a sufficient method to adequately protect a nation from the importation of COVID-19 cases.
Data availability
All results described in the work, in addition to technical descriptions of methods used, are made available in the supplementary material. The Python package used to implement these methods and obtain our results has been made accessible online[1].
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