Dear Dr Hill,

Thank you very much for submitting your manuscript "Modelling livestock infectious disease control policy under differing social perspectives on vaccination behaviour" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

In particular, I ask that you:

 - consider Reviewer #1's comment on the choice of kernel. At a minimum, the point identified by the reviewer should be acknowledged and discussed in the text.

 - consider improvements to the abstract and main text conclusions to address Reviewer #2's comment on implications and meaning that can be drawn from this work.

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Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

James M McCaw, PhD

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor-in-Chief

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 #1: The authors tried to develop a parsimonious modelling framework to combine decision dynamics with epidemiological dynamics. Classically these dynamics are often separately assessed and feed-back ignored. The authors base their efforts on foot-and-mouth outbreaks in two English counties specifying three types of farmers: precautionary, reactionary and non-vaccinators.

The conclusions are substantiated by the results and extensive sensitivity analysis of their model. This of course then relies on the suitability of the model to study the given dynamics.

Intentionally the model is kept fairly simple allowing for robust and tractable results. This illustrates under the model assumptions the potential disagreement between population level and individual level perspective on the most optimal strategy.

The optimal decision to vaccinate for reactionary vaccinators is solely based on the distance to infected farms and costs for vaccination and infection. This requires a full knowledge of the epidemiological state of other farms as well as knowledge on the transmission kernel. Both are often not available during (or directly after) an outbreak. It also does not allow for individual variation in risk attitude.

Another draw-back of this approach is that the model does not take into account the influence of peers or influential people on the decision to vaccinate. Non-vaccinators remain non-vaccinators in the model even when all their peers do vaccinate and might potentially pressure non-vaccinators to vaccinate. Also reactionary vaccinators might be persuaded to act differently with many non-vaccinators in their peer-network.

For an epizootic infections such as FMD in Europe, learning might not play a role, but the framework does not allow for changes in willingness to vaccinate after having experienced outbreaks. The authors acknowledge this limitation in the discussion.

This modelling framework is, altogether, an important addition in the toolbox of modelling for evaluation of intervention strategies.

Some minor remarks remain:

- Why choose for a parameterization of the kernel based on US outbreak? The transmission kernel represents both the infectivity of a particular strain as the contact structure within a country, which is likely to be very different in UK and US.

Reviewer #2: The abstract needs strengthening (beyond ‘deepening our understanding’) in terms of providing recommendations based on the authors findings:

By identifying instances of strong disagreement between the intervention stringency that is best from the perspective of a sole individual compared to the overall population, we can deepen our understanding of how stakeholders may react to veterinary health interventions.

• This is a timely paper, as noted. There has been a call by the infectious disease modelling community, in the human public health field, for greater incorporation of behavioural changes among infectious disease dynamic models.

• There is no mention of the role of incentives given the authors findings. Incentives are likely to be an important discussion point. This should be built into the paper

• From an economic point of view the significance of the differing perspectives (esp the population) in this context needs to be developed (e.g. In other areas of economics, such as health economics, perspective such as the NHS and Societal are relevant.)

The authors note: Population perspective The population perspective took the viewpoint of an entity who wanted to minimise costs for the overall population. Can this be expanded? For example what is the ‘entity’ and what are the relevant incentives for such an entity?

• Conclusions and recommendations need much more development based on the findings.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes: Egil Fischer

Reviewer #2: No

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References:

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Dear Dr Hill,

We are pleased to inform you that your manuscript 'Modelling livestock infectious disease control policy under differing social perspectives on vaccination behaviour' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

James M McCaw, PhD

Associate Editor

PLOS Computational Biology

Virginia Pitzer

Deputy Editor-in-Chief

PLOS Computational Biology

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