Reviewer response for version 1
Summary :
Australia’s influenza season does not typically coincide with school holidays. 
However, in 2019, the influenza season occurred earlier than usual, allowing the opportunity to evaluate the impacts of school closure on influenza dynamics in Australia. 
In this article, the authors present state and territory-level influenza data by week, and perform segmented regression, comparing influenza incidence before and after the school holiday. 
They also count weekly increases and decreases before, during, and after the school holiday, and show age-based dynamics. 
This article asks an important question of public health relevance to non-pharmaceutical interventions in a setting where it has not been previously investigated in a concise manner. 
However, this article could be strengthened through additional framing, increased statistical rigor, and more nuanced interpretation.
Comments :
- The introduction leaves several unanswered questions that would help to more clearly identify the importance of the study. 
The relationship between school closure and influenza dynamics has been explored in a number of previous papers, with both epidemiological data and mechanistic models. 
Thus, it is unclear to me why is it important to consider this relationship in Australia, specifically. 
Do the authors have hypotheses regarding trends or mechanisms that might be different from other countries? 
Is it important for public health planning? 
This question seems especially salient to me since school closures and the influenza season in Australia do not typically coincide, so these epidemiological trends may not be typical for Australia.
- The article concludes that there is a significant reduction in influenza in most states in Australia based on the results of segmented regression comparing influenza incidence level and slope before and after the holiday period. 
I am not totally convinced by this conclusion, as Table 1 shows that the change in level is not significant for Victoria, Australian Capital Territory, and Southern Australia, and the change in slope is not significant for Queensland, Victoria, Western Australia, and Southern Australia. 
This is contrary to the section of the results that states that “the estimates for the initial effect and the subsequent slope show significant declines for all states except South Australia”. 
These results could be more carefully interpreted. 
This would give rise to an interesting question: what are the differences between the states/territories that result in different dynamics?
- I think this article could be strengthened with a more robust time series modeling approach, like ARIMA modeling. 
This would be particularly useful because the predicted vs. residual plot in Appendix 4 appears to indicate heteroskedasticity in the data. 
Including model terms in a time series model could handle this issue, as well as temporal autocorrelation.
- The results also provide the percent of weeks in the pre-vacation, during-vacation, and post-vacation period in which there is a weekly decrease in influenza incidence, indicating that there is a higher proportion of decreases in the during and post-vacation period. 
However, the pre-vacation period seems to include all of the early part of the season. 
If there were numerous decreases in this time period, then the onset of the influenza season would not have occurred. 
Thus, this finding may be a bit circular.
- It would be useful to compare this influenza season, which co-occurs with the holiday period, with another influenza season that does not co-occur with the holiday period, as a control. 
This could help to isolate the impact of school closure on influenza dynamics.
- The authors specify mid-May to early September as their study period. 
However, it’s unclear what total population and age-specific dynamics looked like post week 35. 
Past studies have shown that school holidays simply delay dynamics so that the epidemic wave recovers to pre-holiday rates after the holiday. 
I would be curious if this is the case here.
- In the model, a one-week lag was assumed. 
Is there evidence about care-seeking and influenza surveillance/reporting to support this? 
The discussion mentions a sensitivity analysis performed with no lag, but I wonder if the lag could be longer due to a combination of delay to seeking healthcare and delay in reporting.
- The fourth paragraph of the discussion comments on the possible causes of the association between school closure and influenza. 
This paragraph rules out several possible explanations but does not then provide a plausible explanation.
Minor comments:
- A y-axis label on Figure 1 and Figure 2 would be helpful. 
The Figure 1 caption says it is the influenza rate, so I assume it is influenza cases per week, but this could be clearer.
- Is the x-axis in Figure 2 the week number? 
It would be easier to interpret if it was put into the same units as Figure 1, with dates instead of week counts.
- Does Figure 2 include cases for all of the states and territories combined?
- In Table 1, it is unclear whether the changes described (in both slope and level) are comparing the during-vacation period or the post-vacation period to the pre-vacation period. 
The observed and predicted plots in Appendix 3 appear to show 3 different segments fit for each of these periods, but it is unclear what change is reported in Table 1.
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