The effectiveness of full and partial travel bans against COVID-19 spread in Australia for travellers from China during and after the epidemic peak in China

  1. Valentina Costantino
  2. David J Heslop
  3. C Raina MacIntyre

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Abstract

Background

Australia implemented a travel ban on China on 1 February 2020, while COVID-19 was largely localized to China. We modelled three scenarios to test the impact of travel bans on epidemic control. Scenario one was no ban; scenario two and three were the current ban followed by a full or partial lifting (allow over 100 000 university students to enter Australia, but not tourists) from the 8th of March 2020.

Methods

We used disease incidence data from China and air travel passenger movements between China and Australia during and after the epidemic peak in China, derived from incoming passenger arrival cards. We used the estimated incidence of disease in China, using data on expected proportion of under-ascertainment of cases and an age-specific deterministic model to model the epidemic in each scenario.

Results

The modelled epidemic with the full ban fitted the observed incidence of cases well, predicting 57 cases on March 6th in Australia, compared to 66 observed on this date; however, we did not account for imported cases from other countries. The modelled impact without a travel ban results in more than 2000 cases and about 400 deaths, if the epidemic remained localized to China and no importations from other countries occurred. The full travel ban reduced cases by about 86%, while the impact of a partial lifting of the ban is minimal and may be a policy option.

Conclusions

Travel restrictions were highly effective for containing the COVID-19 epidemic in Australia during the epidemic peak in China and averted a much larger epidemic at a time when COVID-19 was largely localized to China. This research demonstrates the effectiveness of travel bans applied to countries with high disease incidence. This research can inform decisions on placing or lifting travel bans as a control measure for the COVID-19 epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.09.20032045: (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

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A further limitation is the uncertainty of parameters used, particularly the proportion of asymptomatic cases. We have used a conservative estimate, but if the rate is higher than 40%, the outcomes would be worse. While it has been showed that distancing measures are highly effective (2, 22) a systematic review looking at the effectiveness of travel restrictions (23), shows that international travel restriction are effective in delaying the epidemic but may not contain it. We also assumed a very optimistic scenario of 80% of contacts being identified, which may not occur with high case numbers, if a high proportion of asymptomatic transmission is occurring, or if self-quarantine is ineffective. In this study we assumed voluntary home quarantine, which is showed to be about 50% effective in R0 reduction (16), however there could be an increased risk of intra-household transmission infected people to contacts (24), which is not considered in this model. We showed that the ban implemented for travellers from China, when the epidemic was almost at its peak, substantially delayed the spread into Australia. There is now evidence of community transmission in Australia, but the epidemic is still in the early stages, and this study provide evidence to support the new travel bans that have been implemented on Iran and South Korea, in order to delay the epidemic. The model predicted 57 cases by March 6th in Australia, which is slightly less than the notified number of 66, which suggests...

    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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  2. Version published to 10.1093/jtm/taaa081
  3. Version published to 10.1101/2020.03.09.20032045v1 on medRxiv