Vaccination and testing of the border workforce for COVID-19 and risk of community outbreaks: a modelling study

Throughout 2020 and the first part of 2021, Australia and New Zealand have followed a COVID-19 elimination strategy. Both countries require overseas arrivals to quarantine in government-managed facilities at the border. In both countries, community outbreaks of COVID-19 have been started via infection of a border worker. This workforce is rightly being prioritized for vaccination. However, although vaccines are highly effective in preventing disease, their effectiveness in preventing infection with and transmission of SARS-CoV-2 is less certain. There is a danger that vaccination could prevent symptoms of COVID-19 but not prevent transmission. Here, we use a stochastic model of SARS-CoV-2 transmission and testing to investigate the effect that vaccination of border workers has on the risk of an outbreak in an unvaccinated community. We simulate the model starting with a single infected border worker and measure the number of people who are infected before the first case is detected by testing. We show that if a vaccine reduces transmission by 50%, vaccination of border workers increases the risk of a major outbreak from around 7% per seed case to around 9% per seed case. The lower the vaccine effectiveness against transmission, the higher the risk. The increase in risk as a result of vaccination can be mitigated by increasing the frequency of routine testing for high-exposure vaccinated groups.


Comments to the Author(s)
This paper presents a scenario analysis on the risk of community outbreaks of COVID-19 associated with vaccinating border workers in a largely unvaccinated population. It asks an important question as to whether this approach actually reduces risk in populations with an elimination strategy given differing reproduction numbers and vaccine efficacy against transmission. Although this paper presents an interesting idea and model to answer an important question, it is my opinion that it overlooks some very important impacts of vaccination, which makes it difficult to interpret how this analysis relates to risk of community outbreaks. In particular, the effect of vaccination on susceptibility to infection is overlooked. I believe this paper has a lot of potential, but would be inappropriate to publish until it addresses some of my concerns (given below).

Main points:
The paper only makes mention of the effect of vaccines on transmission but does not note its effect on susceptibility to infection. This paper considers a model that is initialised by seeding a case in a border worker, but this is not put in context of a lower seeding rate due to vaccination.
On a related note, their third scenario simulates transmission in a context where some contacts of border workers are vaccinated. The effect of vaccine efficacy against infection is important here as it impacts both the seeding rate and the outbreak dynamics.
Assuming 100% vaccine efficacy against symptoms is noted as being conservative and it is partially justified based on estimates of >90% efficacy against symptomatic infection. I think this overlooks the fact that conditional upon being infected the probability of symptoms may still be rather high. For example, suppose that the vaccine was 90% effective against infections and 90% effective against symptomatic infections, the probability that an infected worker would be symptomatic is still 33%. In the absence of reasonable estimates of efficacy against symptomatic infection I think this warrants a sensitivity analysis.
Minor points: There's no reference to the dispersion parameter k=0.5, I've seen estimates as low as k=0.1. It would either be worth knowing where this parameter came from or to see a sensitivity analysis with respect to dispersion. Similar to above, I would guess that results are quite sensitive to the transmission rate in subclinical infections.
The output in terms of probability of detection at different generations as well as the different generation sizes seems like an unintuitive proxy for risk. I found it not entirely straight forward to weigh up detection probabilities with the outbreak sizes when comparing the different scenarios. Maybe a more straightforward measure of risk would be the epidemic size distribution at the time of detection, or even hitting probabilities of epidemic thresholds before detection.

Review form: Reviewer 2
Is the manuscript scientifically sound in its present form? No

Do you have any ethical concerns with this paper? Yes
Have you any concerns about statistical analyses in this paper? No

Recommendation? Reject
Comments to the Author(s) The paper dose not have any mathematical model Decision letter (RSOS-210686.R0) We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.

Dear Dr Plank
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Associate Editor: 2 Comments to the Author: This is an interesting article, which uses a stochastic branching model to estimate the development of outbreaks of covid-19 caused by border control staff becoming infected and taking the infection out into the wider community. It could be argued that the work has limited applicability outside of New Zealand and Australia as much of the rest of the world still has quite significant community transmission. Many countries also have a higher level of vaccination within the general population. If it is possible to make the work more generally applicable, that might increase its audience.
Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) This paper presents a scenario analysis on the risk of community outbreaks of COVID-19 associated with vaccinating border workers in a largely unvaccinated population. It asks an important question as to whether this approach actually reduces risk in populations with an elimination strategy given differing reproduction numbers and vaccine efficacy against transmission. Although this paper presents an interesting idea and model to answer an important question, it is my opinion that it overlooks some very important impacts of vaccination, which makes it difficult to interpret how this analysis relates to risk of community outbreaks. In particular, the effect of vaccination on susceptibility to infection is overlooked. I believe this paper has a lot of potential, but would be inappropriate to publish until it addresses some of my concerns (given below).

Main points:
The paper only makes mention of the effect of vaccines on transmission but does not note its effect on susceptibility to infection. This paper considers a model that is initialised by seeding a case in a border worker, but this is not put in context of a lower seeding rate due to vaccination.
On a related note, their third scenario simulates transmission in a context where some contacts of border workers are vaccinated. The effect of vaccine efficacy against infection is important here as it impacts both the seeding rate and the outbreak dynamics.
Assuming 100% vaccine efficacy against symptoms is noted as being conservative and it is partially justified based on estimates of >90% efficacy against symptomatic infection. I think this overlooks the fact that conditional upon being infected the probability of symptoms may still be rather high. For example, suppose that the vaccine was 90% effective against infections and 90% effective against symptomatic infections, the probability that an infected worker would be symptomatic is still 33%. In the absence of reasonable estimates of efficacy against symptomatic infection I think this warrants a sensitivity analysis.
Minor points: There's no reference to the dispersion parameter k=0.5, I've seen estimates as low as k=0.1. It would either be worth knowing where this parameter came from or to see a sensitivity analysis with respect to dispersion.
Similar to above, I would guess that results are quite sensitive to the transmission rate in subclinical infections.
The output in terms of probability of detection at different generations as well as the different generation sizes seems like an unintuitive proxy for risk. I found it not entirely straight forward to weigh up detection probabilities with the outbreak sizes when comparing the different scenarios. Maybe a more straightforward measure of risk would be the epidemic size distribution at the time of detection, or even hitting probabilities of epidemic thresholds before detection.

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See Appendix A.
Decision letter (RSOS-210686.R1) We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.

Dear Dr Plank,
It is a pleasure to accept your manuscript entitled "Vaccination and testing of the border workforce for COVID-19 and risk of community outbreaks: a modelling study" in its current form for publication in Royal Society Open Science. The comments of the reviewer(s) who reviewed your manuscript are included at the foot of this letter.

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Thank you for your fine contribution. On behalf of the Editors of Royal Society Open Science, we look forward to your continued contributions to the Journal. The referee has provided some useful comments on your paper and some sensible suggestions for improving it. I agree with the referee that your paper tackles an interesting problem and consequently we are keen to see a revision of the work. Nonetheless, there are some important points that need clarifying, and some extra thought might be needed as to the assumptions made in the modelling.
-Thank you for these positive remarks. We have addressed the specific points of Associate Editor 2 and Reviewer 1 -see below.
Associate Editor: 2 Comments to the Author: This is an interesting article, which uses a stochastic branching model to estimate the development of outbreaks of covid-19 caused by border control staff becoming infected and taking the infection out into the wider community. It could be argued that the work has limited applicability outside of New Zealand and Australia as much of the rest of the world still has quite significant community transmission. Many countries also have a higher level of vaccination within the general population. If it is possible to make the work more generally applicable, that might increase its audience.
-We have added some commentary to the last paragraph of the Introduction to make clear the broader applicability of our approach and conclusions.

Reviewer comments to Author:
Reviewer: 1 Comments to the Author(s) This paper presents a scenario analysis on the risk of community outbreaks of COVID-19 associated with vaccinating border workers in a largely unvaccinated population. It asks an important question as to whether this approach actually reduces risk in populations with an elimination strategy given differing reproduction numbers and vaccine efficacy against transmission. Although this paper presents an interesting idea and model to answer an important question, it is my opinion that it overlooks some very important impacts of vaccination, which makes it difficult to interpret how this analysis relates to risk of community outbreaks. In particular, the effect of vaccination on susceptibility to infection is overlooked. I believe this paper has a lot of potential, but would be inappropriate to publish until it addresses some of my concerns (given below).
-Thank you for these encouraging remarks. These comments are addressed in detail below.
Main points:

Appendix A
The paper only makes mention of the effect of vaccines on transmission but does not note its effect on susceptibility to infection. This paper considers a model that is initialised by seeding a case in a border worker, but this is not put in context of a lower seeding rate due to vaccination. Methods section and Tables 1-3).

-This is a good point and we have now added and discussed new results for additional scenarios where the vaccine's effect on transmission comes partly from infection prevention and partly from reduction of transmission in breakthrough infections (see new text in
On a related note, their third scenario simulates transmission in a context where some contacts of border workers are vaccinated. The effect of vaccine efficacy against infection is important here as it impacts both the seeding rate and the outbreak dynamics. -We agree and this scenario now also includes results for cases where part of the vaccine's reduction in transmission comes from infection prevention (Table 3).
Assuming 100% vaccine efficacy against symptoms is noted as being conservative and it is partially justified based on estimates of >90% efficacy against symptomatic infection. I think this overlooks the fact that conditional upon being infected the probability of symptoms may still be rather high. For example, suppose that the vaccine was 90% effective against infections and 90% effective against symptomatic infections, the probability that an infected worker would be symptomatic is still 33%. In the absence of reasonable estimates of efficacy against symptomatic infection I think this warrants a sensitivity analysis.
-We have added a new sensitivity analysis showing the results for a scenario where the vaccine is only 80% effective in preventing symptomatic COVID-19 in breakthrough infections (Table S4).

Minor points:
There's no reference to the dispersion parameter k=0.5, I've seen estimates as low as k=0.1. It would either be worth knowing where this parameter came from or to see a sensitivity analysis with respect to dispersion.
-We have added supporting references for this parameter value.
Similar to above, I would guess that results are quite sensitive to the transmission rate in subclinical infections.
-Rather than run an additional sensitivity analysis, we have added better justification and references for the choice of this parameter value. Note that the Supplementary Material includes a sensitivity analysis to the proportion of infections that are subclinical.
The output in terms of probability of detection at different generations as well as the different generation sizes seems like an unintuitive proxy for risk. I found it not entirely straight forward to weigh up detection probabilities with the outbreak sizes when comparing the different scenarios.
Maybe a more straightforward measure of risk would be the epidemic size distribution at the time of detection, or even hitting probabilities of epidemic thresholds before detection.
-We agree the results provided did not enable straightforward interpretation. As suggested we have added hitting probabilities of 10 infections and 40 infections before detection to Tables 1-3 and referred to these in the Results section. The overall outbreak size at detection does not provide useful extra information because it tends to have the same median and IQR as the subset of outbreaks detected at generation 1 (penultimate column of Tables 1-3) because these account for the majority of simulations.