What effect might border screening have on preventing importation of COVID-19 compared with other infections? A modelling study

  1. Declan Bays
  2. Emma Bennett
  3. Thomas Finnie

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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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  1. Version published to 10.1101/2020.07.10.20150664v2 on medRxiv
  2. Version published to 10.1017/s0950268821002387
  3. ScreenIT

    SciScore for 10.1101/2020.07.10.20150664: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A Python package implementing the above model (which has been used to calculate the presented values in the next section) has been produced by the author and made openly available online[9].
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  4. Version published to 10.1101/2020.07.10.20150664v1 on medRxiv