Abstract
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.
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.
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.
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.
Introduction
In response to the epidemic of COVID-19,1 Australia implemented a travel ban from China on 1 February 2020, adding Iran and then South Korea and Italy to the ban on February 29th, March 5th and 10th, respectively. In addition, Australians evacuated from Wuhan and from the Diamond Princess Cruise ship were quarantined for 2 weeks in dedicated quarantine facilities. The ban on travel from China has been periodically reviewed, with lifting of restrictions announced on February 23rd for high-school students, who number less than 800. In contrast, over 120 000 university students are unable to enter Australia to commence or resume their studies, and a booming tourism industry has ceased.
Non-pharmaceutical interventions measures such as social distancing and quarantine are effective public health tools to control epidemic diseases,2 and Australia successfully delayed the introduction of the 1918 influenza A H1N1 pandemic by 1 year and reduced the total mortality compared with other countries.3 However, evidence on the effectiveness of travel bans in containing the global spread of emerging infectious diseases is still limited,4 and they are not sustainable indefinitely. Therefore, a careful risk analysis needs to be done comparing the health and economic consequences implementing travel bans for the control of COVID-19 for alternative scenarios of increasing and decreasing disease incidence. The epidemic in China peaked on February 5th and has declined since.5 The risk of importation of COVID-19 cases, firstly documented by the Bluedot group in Canada,6,7 through travel from an affected country is proportional to the volume of travel from that country and their prevalence of infection at that time point.
We aimed to estimate the impact of the implementation of the travel ban on China from 1 February 2020 on the epidemic trajectory in Australia, as well as the impact of lifting the ban completely or partially from the 8th of March, when disease incidence was waning in China.
Methods
Three scenarios were considered.
(i) No travel ban—the epidemic curve if the travel ban was never placed.
(ii) Complete travel ban from February 1st to March 8th, followed by complete lifting ban.
(iii) Complete travel ban from February 1st to March 8th, followed by partial lifting ban (allowing university students, but not tourists, to enter the country).
Estimation of infected cases coming into Australia from China
The evacuations from Wuhan and the Diamond Princess Cruise ship to Australia are not considered in this model, which only examines regular air travel between China and Australia. In order to estimate the effectiveness of the travel ban that has been implemented in Australia for travellers from China, we did not consider bans to other countries. We assumed that the chance of cases coming into Australia from China depends from the number of cases in China and the number of travellers to Australia. To estimate the number of people infected that are predicted to enter Australia every 2 weeks from 20th January to April, we utilized 2019 air travel passenger movements between China and Australia, derived from incoming passenger arrival cards, with data aggregated monthly and published by the Australia Bureau of Statistics (ABS).8 ABS incoming passenger card statistics are disaggregated based on permanent resident or citizen returning, permanent migration, short-term stay and long-term arrivals. Long-term arrivals are defined as >1 year. For the purpose of this analysis, air movements of passengers between China and Australia were derived from 2019 data. A baseline level of entries into Australia from China was calculated from the total number of entries over the April–June 2019 time period and was assumed to represent the baseline arrivals for the purpose of tourism and other business. The seasonal excess of travellers was then calculated by deducting this baseline from the January to March 2019 data. The seasonal excess arrivals were assumed to represent the arrival of international students starting the 2019 study year, which begins in February to March each year. Where travel bans were instituted in this analysis, or lifted, it was assumed that international students unable to enter Australia would return to Australia following the lifting of the ban, 60% in the rest of March and the remaining 40% over the month of April. However, tourists who are not able to travel during a travel ban were not assumed to enter Australia at a later date. Tourism activity was assumed to recover to baseline levels immediately after the lifting of a travel ban. We assumed that after lifting the ban, all the students that could not get in during ban will enter in addition to the new ones; however, tourists that did not enter Australia for the ban are not assumed to enter when the ban is lifted. Once the ban is lifted, partially or not, we consider the situation to be normal again, due to not having enough information to base different future assumptions on. It is possible that after lifting of the ban, travel will be reduced from past baseline numbers, which would reduce the risk of importations even more. The daily number of travellers from China to Australia in each month and for each scenario is showed in Table S2 of the Supplementary data.
Epidemic curve in Australia from cases imported from China
The cases of COVID-19 occurring over time in Australia due to imported cases from China were estimated for each scenario. We used an age-specific deterministic model, with eight mutually exclusive compartments: susceptible (S), latent traced (Et), latent untraced (Eu), infectious (I), isolated (Q), recovered (R) and dead (D). Each of those compartments is divided in 18 age stratified groups each of 5 years duration, ranging from 0 to 84 years old plus an additional age group of 85+ years. The entire Australian population was considered susceptible. The duration of each model run is 400 days. The initial infected cohort is assumed to be generated from cases arriving from China by air. After arrival of an infected case, it is assumed that, if and when they become symptomatic, they are isolated, and a designated portion of their contacts will also be quarantined. Cases transition between epidemiological compartments in accordance with transition rates determined by their duration of stay in each compartment. Model parameters are shown in Table 1. Further details of the model (diagram and differential equations) are described in the Supplementary data. Based on the growing evidence of viral loads in asymptomatic cases,12–16 we considered the latent period to be equally infectious as the symptomatic period; however, due to the different length of the two epidemiological states, our assumption results in 41.6% of the transmissions occurring in the pre-symptomatic state, which is also supported from a recent viral shedding study.17 The proportion of asymptomatic infections was assumed to be 34.6% based on testing of passengers aboard the Diamond Princess Cruise ship;18,19 however, we tested in sensitivity analyses the value of 17.9 and 41.6%, which are the range of values estimated for this parameter.20 The model uses an optimistic assumption that 80% of contacts are identified and quarantined, and 90% of symptomatic cases are isolated after 5 days.21 Studies show a long, mild prodrome of several days before people feel unwell enough to seek medical attention, which is also considered in the model.21 We conducted a sensitivity analyses on the proportion of asymptomatic people, R0 and the case detection rate (CDR) in China. The results are showed in the Supplementary data.
Parameters used in the model
| Parameter | Value | Source |
|---|---|---|
| Basic reproduction number | 2.2, sensitivity analyses as 1.8 and 3 | {Li, 2020 #5}1 |
| Infectious period | 12.2 days of which 5.2 asymptomatic and 7 symptomatic | 1 |
| Time to isolation once symptomatic | 5 days | 21 |
| Effectiveness of home quarantine | 50% reduction in the R0 | 29 |
| Duration of home quarantine | 14 days | Australian recommendation |
| Duration of hospital isolation | 20 days | |
| Proportion of asymptomatic or very mild infectious | 34.6% (17.9 and 41.6% used in sensitivity analyses) | 18–,20 |
| Proportion of contacts identified for home quarantine | 80% | 33 |
| Chinese detection rate | 10%, with sensitivity analyses as 30 and 50% | 10 |
| Proportion of symptomatic people that get isolated after 5 days | 90% | 33 |
| Age-specific case fatality rate (%) for the 18 age groups | 0, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 1.3, 1.3, 3.6, 3.6, 8, 8, 14.8, 14.8 | 34 |
| Parameter | Value | Source |
|---|---|---|
| Basic reproduction number | 2.2, sensitivity analyses as 1.8 and 3 | {Li, 2020 #5}1 |
| Infectious period | 12.2 days of which 5.2 asymptomatic and 7 symptomatic | 1 |
| Time to isolation once symptomatic | 5 days | 21 |
| Effectiveness of home quarantine | 50% reduction in the R0 | 29 |
| Duration of home quarantine | 14 days | Australian recommendation |
| Duration of hospital isolation | 20 days | |
| Proportion of asymptomatic or very mild infectious | 34.6% (17.9 and 41.6% used in sensitivity analyses) | 18–,20 |
| Proportion of contacts identified for home quarantine | 80% | 33 |
| Chinese detection rate | 10%, with sensitivity analyses as 30 and 50% | 10 |
| Proportion of symptomatic people that get isolated after 5 days | 90% | 33 |
| Age-specific case fatality rate (%) for the 18 age groups | 0, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 1.3, 1.3, 3.6, 3.6, 8, 8, 14.8, 14.8 | 34 |
Parameters used in the model
| Parameter | Value | Source |
|---|---|---|
| Basic reproduction number | 2.2, sensitivity analyses as 1.8 and 3 | {Li, 2020 #5}1 |
| Infectious period | 12.2 days of which 5.2 asymptomatic and 7 symptomatic | 1 |
| Time to isolation once symptomatic | 5 days | 21 |
| Effectiveness of home quarantine | 50% reduction in the R0 | 29 |
| Duration of home quarantine | 14 days | Australian recommendation |
| Duration of hospital isolation | 20 days | |
| Proportion of asymptomatic or very mild infectious | 34.6% (17.9 and 41.6% used in sensitivity analyses) | 18–,20 |
| Proportion of contacts identified for home quarantine | 80% | 33 |
| Chinese detection rate | 10%, with sensitivity analyses as 30 and 50% | 10 |
| Proportion of symptomatic people that get isolated after 5 days | 90% | 33 |
| Age-specific case fatality rate (%) for the 18 age groups | 0, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 1.3, 1.3, 3.6, 3.6, 8, 8, 14.8, 14.8 | 34 |
| Parameter | Value | Source |
|---|---|---|
| Basic reproduction number | 2.2, sensitivity analyses as 1.8 and 3 | {Li, 2020 #5}1 |
| Infectious period | 12.2 days of which 5.2 asymptomatic and 7 symptomatic | 1 |
| Time to isolation once symptomatic | 5 days | 21 |
| Effectiveness of home quarantine | 50% reduction in the R0 | 29 |
| Duration of home quarantine | 14 days | Australian recommendation |
| Duration of hospital isolation | 20 days | |
| Proportion of asymptomatic or very mild infectious | 34.6% (17.9 and 41.6% used in sensitivity analyses) | 18–,20 |
| Proportion of contacts identified for home quarantine | 80% | 33 |
| Chinese detection rate | 10%, with sensitivity analyses as 30 and 50% | 10 |
| Proportion of symptomatic people that get isolated after 5 days | 90% | 33 |
| Age-specific case fatality rate (%) for the 18 age groups | 0, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 1.3, 1.3, 3.6, 3.6, 8, 8, 14.8, 14.8 | 34 |
Results
Figure 1 shows the notified and estimated epidemic in China from 31 December 2019 to 23 February 2020.
The estimated true epidemic curve (blue) compared to the reported epidemic curve in China (red)10
Figure 2 shows the modelled epidemic curve fitting the incidence data from 5th to 23rd of February and then forecasted until the 4th of April, which is the time we expect the incidence decreasing to almost zero should be the current trend in China continue.
Estimated incidence data in China (blue) and model fit to the data and forecasting future daily incidence (red)
We found that following the peak on February 5th and decline of the epidemic in China, the probability that an infected traveller arriving from China under the partial ban scenario (allowing university students only) is low. The complete removal of travel restrictions on 8th of March results in an estimated arrival of 5 cases in the first 2 weeks and 1 in the following 2 weeks. However, if we compare a 5-week ban scenario (scenario 2) with the scenario without a travel ban (scenario 1), we estimate that 32, 43 and 36 infected coming every 2 weeks from the 26th of January would have been averted. Due to a surge of students coming in the first 2 weeks following the lifting of the ban in the second scenario, an additional 2 more infected are estimated to enter from 8th to 21st of March (Table 2).
Imported cases in Australia from China under no, partial and full travel bans per each 2-week period considering disease in China only
| Time travelling | Infected entering Australia without ban (scenario 1) | Infected entering Australia with ban from 1st of February to 7th of March followed by full lifting of ban (scenario 2) | Infected entering Australia with following a ban from 1st of February to 7th of March followed by partial lifting of ban (scenario 3) |
|---|---|---|---|
| 26 January–8 February | 39 | 7 | 7 |
| 9–22 February | 43 | 0 | 0 |
| 23 February–7 March | 36 | 0 | 0 |
| 8–21 March | 3 | 5 | 0 |
| 22 March–4 April | 1 | 1 | 0 |
| Time travelling | Infected entering Australia without ban (scenario 1) | Infected entering Australia with ban from 1st of February to 7th of March followed by full lifting of ban (scenario 2) | Infected entering Australia with following a ban from 1st of February to 7th of March followed by partial lifting of ban (scenario 3) |
|---|---|---|---|
| 26 January–8 February | 39 | 7 | 7 |
| 9–22 February | 43 | 0 | 0 |
| 23 February–7 March | 36 | 0 | 0 |
| 8–21 March | 3 | 5 | 0 |
| 22 March–4 April | 1 | 1 | 0 |
Imported cases in Australia from China under no, partial and full travel bans per each 2-week period considering disease in China only
| Time travelling | Infected entering Australia without ban (scenario 1) | Infected entering Australia with ban from 1st of February to 7th of March followed by full lifting of ban (scenario 2) | Infected entering Australia with following a ban from 1st of February to 7th of March followed by partial lifting of ban (scenario 3) |
|---|---|---|---|
| 26 January–8 February | 39 | 7 | 7 |
| 9–22 February | 43 | 0 | 0 |
| 23 February–7 March | 36 | 0 | 0 |
| 8–21 March | 3 | 5 | 0 |
| 22 March–4 April | 1 | 1 | 0 |
| Time travelling | Infected entering Australia without ban (scenario 1) | Infected entering Australia with ban from 1st of February to 7th of March followed by full lifting of ban (scenario 2) | Infected entering Australia with following a ban from 1st of February to 7th of March followed by partial lifting of ban (scenario 3) |
|---|---|---|---|
| 26 January–8 February | 39 | 7 | 7 |
| 9–22 February | 43 | 0 | 0 |
| 23 February–7 March | 36 | 0 | 0 |
| 8–21 March | 3 | 5 | 0 |
| 22 March–4 April | 1 | 1 | 0 |
In Figure 3, we show the epidemic curve without and with the ban implemented for 5 weeks followed by a full lifting (scenarios 1 and 2) and we show a large impact on averting an epidemic in Australia. In both cases, the model reproduces the 15 notified imported cases reported in Australia between the 20th of January and 8th of February. The modelled epidemic in scenario 2, with the full ban, predicts 57 cases in Australia by the 6th of March. The notified cases by 6th of March were 66; however, we did not account for imported cases from other countries.
Daily incidence, cumulative number of cases and the cumulative number of deaths from the 20th of January onward for 400 days with and without a travel ban
In the epidemic curve for scenario 2, when travel resumes, there will be a small surge in cases followed by a decrease and the epidemic can be controlled, with a total of less than 300 cases and about 8 deaths. If the ban was never in place (scenario 1), the epidemic would continue for more than a year resulting in more than 2000 cases and about 400 deaths, in a scenario which considers only localized disease in China.
In each scenario tested, the implementation of the travel ban still reduces the total cases and deaths by about 85%; however, in the case of R0 being 3, the travel ban will only delay the epidemic curve pushing the peak about 50 days in time (Figure S2). The final number of cases and deaths is most sensitive to the R0 assumption, while it is similarly affected by changing the CDR in China or the proportion of asymptomatic people. Indeed increasing the proportion of asymptomatic people by 2.3 times (from 17.9 to 41.6%), the number of cases increase by 4 times (from a total of 124 to 491 cases with ban on or 975 to over 4000 in the scenario of no ban), while increasing the CDR about 66% (from 30 to 50%) results in 31% decrease in number of cases (from 106 to 76 and from 737 to 461 in the case of travel ban on and off, respectively), see Figures S3 and S4.
Discussion
We estimated that the travel ban implemented by Australia on 1st of February, close to the peak of the epidemic in China, has been very effective, reducing the number of cases and deaths from COVID-19 by about 87%. Studies have been published on the effectiveness of domestic and international travel restrictions on COVID-19.22–24 However, this study is the first one to show the effectiveness of travel bans in Australia and can inform a phased approach of partial lifting of bans when cases in the source country decline over the course of the pandemic. This allows the monitoring of the ongoing situation in China, which may yet see a second wave of the epidemic. The risk of having a person travelling to Australia already infected from China depends from the volume of travel and the infection prevalence in China at that time as well as individual host factors. We used a deterministic compartmental model, so we could not consider singular host infectivity and susceptibility, but we acknowledge that these are additional important parameters which could influence the results and would require an agent-based modelling approach to be tested.
When calculating the number of students and tourists arriving from China, we have assumed that the seasonal excess must largely be attributed to the seasonal movement of international students who we estimated to represent the large part of the excess movements at this time, which was the most feasible way to account for the normal business and general tourist movements. However, the effect of lunar new year on non-student travel is a limitation on this approach.
Our estimate of the true epidemic curve is supported by other studies,10,11,25 and projected case numbers would change with any change in this estimate. Even if the true number of cases in China is 10 or 100 times that reported, only a fraction of the entire population of China has been infected, which leaves a possibility of a subsequent wave of the epidemic. Indeed the fraction of remining susceptibles is still too high to stop transmissions, for an R0 equal to 2.2, about 60% of the population is required to be immune to achieve herd immunity.26 If cases increase in China, the model can provide the estimates of risk based on daily new case numbers.
The pandemic has since surged globally, with new epicentres in Europe and the USA. We do not consider cases coming in from other countries—however, this study illustrates the principle of travel bans and public health impact on epidemic control using China as a case study during a period when the epidemic was largely localized to China. A further limitation is the uncertainty of parameters used, particularly the proportion of asymptomatic cases. While we did not use age-weighted assumptions of asymptomatic infection, younger people are more likely to be asymptomatic,20 which would have the effect of increasing undetected transmissions. 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,2,27 a systematic review looking at the effectiveness of travel restrictions28 shows that international travel restrictions are effective in delaying an 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;29 however, there could be an increased risk of intra-household transmission infected people to contacts,30 which is not considered in this model.
It has been showed that there could be different transmissions rates per different settings or events, such as super dispersion in a mass gathering.31 As the model used is a deterministic compartmental model, we could not include different networking or environments. A further limitation of this study is not taking into account diverse transmissions events such as super-spreading and lower transmissions. However, the age-specific transmission rates allowed us to take into consideration age, which includes heterogeneity in social mixing.
We showed that the ban implemented for travellers from China, when the epidemic was almost at its peak in China, substantially delayed the spread into Australia. There was subsequently evidence of community transmission in Australia and many more cases imported from other countries, but this study provides evidence to support the subsequent travel bans that have been implemented on Italy, Iran, South Korea and all other countries, 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 on that date, which suggests that the model assumptions were reasonable, given we did not account for cases coming in from other countries. Community transmission in Australia in early March was likely linked to imported cases from China, given the fairly long incubation period32 and less than three incubation periods since the first evacuation of Australians from Wuhan on February 3rd. Only symptomatic evacuees were tested, meaning undetected transmissions may have occurred from that event. The model fit to observe data was good, also suggesting that the epidemic was still possible to contain. Although Australia had a subsequent surge and peak in cases, mostly travel-related, in late March, strong border control resulted in excellent control by April.
This analysis is a first insight into the effectiveness of travel restrictions for COVID-19 outbreak, supports the effectiveness of the Australian response, informs gradual lifting of the bans or placing of new bans on other countries and could inform other countries in reducing the burden of importations and resulting domestic transmission of COVID-19.
Author contribution
Valentina Costantino: Methodology and modelling construction, parameterisation of model, writing and revision. David Heslop: Manipulation of travelling data, writing and revision. Raina MacIntyre: Conception of the study and scenarios, parametrisation of model, writing and revision. All: Designing and conceptualizing the model.
Funding
This work has not funding.
Conflict of interest
The authors declare no conflicts of interest.
