Early intervention is the key to success in COVID-19 control

New Zealand responded to the COVID-19 pandemic with a combination of border restrictions and an Alert Level (AL) system that included strict stay-at-home orders. These interventions were successful in containing an outbreak and ultimately eliminating community transmission of COVID-19 in June 2020. The timing of interventions is crucial to their success. Delaying interventions may reduce their effectiveness and mean that they need to be maintained for a longer period. We use a stochastic branching process model of COVID-19 transmission and control to simulate the epidemic trajectory in New Zealand's March–April 2020 outbreak and the effect of its interventions. We calculate key measures, including the number of reported cases and deaths, and the probability of elimination within a specified time frame. By comparing these measures under alternative timings of interventions, we show that changing the timing of AL4 (the strictest level of restrictions) has a far greater impact than the timing of border measures. Delaying AL4 restrictions results in considerably worse outcomes. Implementing border measures alone, without AL4 restrictions, is insufficient to control the outbreak. We conclude that the early introduction of stay-at-home orders was crucial in reducing the number of cases and deaths, enabling elimination.


1.
The introduction section is long. It can be made more concise by summarising the intervention timeline and milestones (described in paragraph 2, L75) in a table (i.e., Table 1) without the need of explaining them in details in the paragraph. That should be enough to provide context of the control efforts made the government of New Zealand in the beginning of the epidemic. The mid-to-end section of paragraph 4 of the introduction, starting from L134, described detailed simulation scenarios that were also explained partly in the method section. Can consider to remove that particular section so the introduction can be more concise and no redundancy of information.
2. Figure 1 shows comparison of modelled reported (clinical) cases and reported cases (total of both domestic and international as the barcharts are stacked). Since the model only simulated local transmission, is it correct to assume that the modelled reported cases represented the sum of reported (actual) international cases (or simulated for the delayed border closure scenario) and modelled clinical cases (which then also affected by reporting probability, p_det=0.75, and also accounting for delay from infection to onset and testing results)? If this is the case, please clarify further either in the figure caption or the results/methods section to avoid confusion.

3.
One of the metrics assessed, measured as the daily new reported cases, is labeled as the maximum contact tracing load. From my understanding, the contact tracing process tracks down people who have been exposed by the virus (https://www.health.govt.nz/our-work/diseasesand-conditions/covid-19-novel-coronavirus/covid-19-health-advice-public/contact-tracingcovid-19). Hence, the number of the new reported cases shouldn't be a representative of the maximum contact tracing load (as it should be multiple times of the reported cases depending on how many people were 'exposed' by them). Please consider changing the terminology used throughout the article or explain further on why this doesn't really mean the number of people need to be traced.

Review form: Reviewer 2
Is the manuscript scientifically sound in its present form? Yes

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

Recommendation?
Major revision is needed (please make suggestions in comments)

Comments to the Author(s)
The authors present a modelling analysis of the impact of border measures and domestic restrictions in New Zealand in early 2020. This provides some useful insights into how timing of introduction influences the success of such approaches in local elimination of COVID-19, and the relative effect of different measures.
Main comments: -The model uses previously published estimates of Reff as input, rather than fitting the model to the data, but would it would be useful to have some brief details about how these input values were calculated -are the assumptions about generation time etc. in the Reff estimation equivalent to the assumptions in model in this paper?
-I was surprised that cases plateaued almost as soon as AL4 was introduced, given incubation and reporting delays (Fig 1). Presumably this was because the impact of international measures was showing up by that point, rather than AL4 driving the dynamics? If so, it would be good to show the timing of border restrictions in the figure too.
-The model includes isolation of symptomatic cases, but were contacts traced during the pre-AL4 period too? Or was contact tracing only implemented during AL4 (the text suggests something along these lines: L86 -"...alongside systems for widespread testing, contact tracing and case isolation")?
Other comments: -L32: 'Taiwan's early border closure, travel restrictions and 14-day quarantine for those entering the country have meant that, to date, Taiwan has avoided a mass lockdown' -this may be worth rewording given restrictions introduced in Taiwan in May 2021 (depending on how lockdown is defined).
-L589: 'Delaying border closure by 5 days could have led to a slightly larger outbreak, but not as large as if AL4 had been delayed by 5 days' -could you be more specific here?
Decision letter (RSOS-210488.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.

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Kind regards, Royal Society Open Science Editorial Office Royal Society Open Science openscience@royalsociety.org Associate Editor Comments to Author: Two reviewers offer a number of comments that need to be addressed before this paper may be considered further. Please ensure you carefully respond to their comments, and provide a pointby-point response as well as a tracked-changes version of the revision -this will help in assessing your revision.
Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) The authors used stochastic simulations exploring the impact of alternative interventions timing scenarios during the early phase of COVID-19 epidemic in New Zealand in 2020 to determine the effectiveness of interventions used based on the timing of interventions. In general, the article is well-written and easy to understand. The simulations were done using some well-established methods with parameters reasonably chosen from other published studies. It is adequate to model the dynamics in the early phase of the epidemic in New Zealand and produce four metrics mentioned in the article that were used to measure the effectiveness of interventions in New Zealand. Modelling results seem to reflect the actual data well (I have an issue regarding the comparison of modelling results to the actual data in my comment). Some important points regarding the content and analysis in the article have been addressed by the other referees. Here, I will add some minor comments: 1. The introduction section is long. It can be made more concise by summarising the intervention timeline and milestones (described in paragraph 2, L75) in a table (i.e., Table 1) without the need of explaining them in details in the paragraph. That should be enough to provide context of the control efforts made the government of New Zealand in the beginning of the epidemic. The midto-end section of paragraph 4 of the introduction, starting from L134, described detailed simulation scenarios that were also explained partly in the method section. Can consider to remove that particular section so the introduction can be more concise and no redundancy of information.
2. Figure 1 shows comparison of modelled reported (clinical) cases and reported cases (total of both domestic and international as the barcharts are stacked). Since the model only simulated local transmission, is it correct to assume that the modelled reported cases represented the sum of reported (actual) international cases (or simulated for the delayed border closure scenario) and modelled clinical cases (which then also affected by reporting probability, p_det=0.75, and also accounting for delay from infection to onset and testing results)? If this is the case, please clarify further either in the figure caption or the results/methods section to avoid confusion.
3. One of the metrics assessed, measured as the daily new reported cases, is labeled as the maximum contact tracing load. From my understanding, the contact tracing process tracks down people who have been exposed by the virus (https://www.health.govt.nz/our-work/diseasesand-conditions/covid-19-novel-coronavirus/covid-19-health-advice-public/contact-tracingcovid-19). Hence, the number of the new reported cases shouldn't be a representative of the maximum contact tracing load (as it should be multiple times of the reported cases depending on how many people were 'exposed' by them). Please consider changing the terminology used throughout the article or explain further on why this doesn't really mean the number of people need to be traced.
Reviewer: 2 Comments to the Author(s) The authors present a modelling analysis of the impact of border measures and domestic restrictions in New Zealand in early 2020. This provides some useful insights into how timing of introduction influences the success of such approaches in local elimination of COVID-19, and the relative effect of different measures.
Main comments: -The model uses previously published estimates of Reff as input, rather than fitting the model to the data, but would it would be useful to have some brief details about how these input values were calculated -are the assumptions about generation time etc. in the Reff estimation equivalent to the assumptions in model in this paper?
-I was surprised that cases plateaued almost as soon as AL4 was introduced, given incubation and reporting delays (Fig 1). Presumably this was because the impact of international measures was showing up by that point, rather than AL4 driving the dynamics? If so, it would be good to show the timing of border restrictions in the figure too.
-The model includes isolation of symptomatic cases, but were contacts traced during the pre-AL4 period too? Or was contact tracing only implemented during AL4 (the text suggests something along these lines: L86 -"...alongside systems for widespread testing, contact tracing and case isolation")?
Other comments: -L32: 'Taiwan's early border closure, travel restrictions and 14-day quarantine for those entering the country have meant that, to date, Taiwan has avoided a mass lockdown' -this may be worth rewording given restrictions introduced in Taiwan in May 2021 (depending on how lockdown is defined).
-L589: 'Delaying border closure by 5 days could have led to a slightly larger outbreak, but not as large as if AL4 had been delayed by 5 days' -could you be more specific here?

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Author's Response to Decision Letter for (RSOS-210488.R0) See Appendix A.

Recommendation?
Accept as is

Comments to the Author(s)
The authors have satisfactorily addressed my comments.

Decision letter (RSOS-210488.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 Professor Binny, It is a pleasure to accept your manuscript entitled "Early intervention is the key to success in COVID-19 control" 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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