Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This paper considers a multi-type branching process model for calculation of outbreak probabilities, used to investigate expected impacts of demographic background and changes from real-world data.

The study appears to be technically correct, however my main concern is a lack of sensitivity analysis to model assumptions. Not every study needs systematic sensitivity investigation, but in this case it is hard to know if the changes found in outbreak probabilities due to demography might be smaller than those due to other factors, meaning that demography is not the most important thing to focus on.

In particular, I think that the model presented is Markov, meaning that offspring distributions will be geometric. However, it should be possible to calculate outbreak probabilities for more general negative binomial offspring distributions as has been done for some time.

Therefore, I would suggest that the authors consider adding some sensitivity analysis to some aspect of model structure in a revised version, but have no other significant concerns about the work.

Reviewer #2: This manuscript reports a mathematical analysis of how demographic change and associated shifts in contact behaviour could influence outbreak probability following importation of a novel pathogen. A range of scenarios are used to examine how outbreak risks are affected by different assumptions about the relationship between age, susceptibility and infectiousness.

The manuscript addresses an important topic, the model design is quite elegant, and the manuscript is (for the most part) clearly written. However, I found that several aspects of the framing, assumptions, and presentation would benefit from clarification, and I make several suggestions below for consideration.

While the reported study does have a public health application as its motivation, I also wondered if it might be suited to a mathematically oriented journal, such as Bulletin of Mathematical Biology, Mathematical Biosciences, etc., or even Epidemics.

Major comments:

Line 73 and Line 226: South Korea and Nigeria are very different settings in many ways that will affect outbreak risk. Consider rephrasing to describe as a comparison between different demographic scenarios typified by the populations of South Korea and Nigeria, rather than a direct comparison between these two settings, acknowledging that there are cultural, economic and ecological differences that would also have a substantial impact on outbreak risk.

Line 200: I’m not sure that I follow the reasoning regarding the scaling of contact rates. If an 10-14 year old in 2020 expects to have (say) 10 daily contacts with other 10-14 year olds, then I don’t see why a 10-14 year old in 2050 shouldn’t continue to have 10 daily contacts with other 10-14 year olds, even if they represent a smaller fraction of the population (barring catastrophic population collapse). It feels plausible that our intrinsic preference may be for a certain fixed level of social contact, rather than contact with a certain fixed proportion of the population in a particular age group. I’m aware that you explored this alternative as a supplementary analysis; however, I wonder whether it would make sense to reverse the “default” and “supplementary” scenarios?

Line 226: As noted above, I feel this comparison would be more appropriately described as a being between different demographic scenarios typified by the demographics of South Korea and Nigeria, rather than a direct comparison between two very different settings.

Line 270: This feels important, that the observed change in outbreak probability appears to be largely driven by the assumption that people will change their contact preferences in line with age distribution. Age distributions have already shifted markedly in many developed countries over the last century – is there any evidence from these settings that supports this assumption? (There may be – I’m not a demographer – if so, it would be good to cite this.) One context where I can imagine this happening is within households, as the number of contacts among children in that setting shrinks with declining family sizes. However, school class sizes, and other social networks would seem to be more robust (at least among children).

Line 360: While this study focuses on the impact of workforce participation of an ageing population, another factor to consider at least discussing (not necessarily including in the model) is the impact of changing demographics on models of care for aging populations. For example, residential care facilities (or aged care homes) have been associated with increased contact and transmission risk. Declining family sizes (and fewer children to look after aging parents) could result in a greater proportion of the population living in communal settings with increased transmission risk.

Minor comments:

Abstract: While the demographic component of the study was clear, I found the references to behaviour too general in the abstract. I suggest describing this component in more complex terms. For example, contact behaviour, compliance with interventions, health-seeking behaviour? There are so many possibilities! Or is workforce participation the behaviour referred to? If so, I suggest making this more explicit, it only really becomes clear when expanded in the introduction (i.e., line 78).

Line 173: Consider using meaningful names for each of scenarios A-D (e.g., as used in lines 276-277)

Line 180 (and elsewhere): In general, methods would be more readable if they consistently referred to the real-world referent (mathematical notation), e.g., “for different age groups (j)”

Line 206: Increased workforce participation is certainly a possibility, although by no means a certainty with increasing automation, AI, etc.

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Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Figure resubmission:

Reproducibility:

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Dear Miss Evans,

We are pleased to inform you that your manuscript 'Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

James M McCaw, PhD

Academic Editor

PLOS Computational Biology

Denise Kühnert

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: Thank you for your consideration and changes to the manuscript. I have only a couple of minor (optional) comments:

(line numbers refer to track changes version)

Line 81: “going forwards” -> “in the future” (“going forwards” sounds like unfortunate corporate speak!)

Fig 3 and S2: a minor point, but the legends in these plots still have scenarios labelled A-D (alongside the descriptive name, which I think reads much more clearly in the text).

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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Reviewer #2: No