Review History


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Summary

  • The initial submission of this article was received on July 6th, 2017 and was peer-reviewed by 3 reviewers and the Academic Editor.
  • The Academic Editor made their initial decision on July 21st, 2017.
  • The first revision was submitted on August 8th, 2017 and was reviewed by 2 reviewers and the Academic Editor.
  • A further revision was submitted on September 1st, 2017 and was reviewed by the Academic Editor.
  • The article was Accepted by the Academic Editor on September 11th, 2017.

Version 0.3 (accepted)

· · Academic Editor

Accept

Dear authors,

The manuscript has high standards to be published in PeerJ in its current form. Therefore, my decision is TO ACCEPT.

Congratulations!


With respect and warm regards,
Dr Palazón-Bru (academic editor for PeerJ)

Version 0.2

· · Academic Editor

Major Revisions

Dear authors,

After reading the report of Reviewer 3, I think your manuscript still needs some major changes before publication. Therefore, my decision is TO REVISE.

Warm regards,
Dr Palazón-Bru (academic editor for PeerJ)

Reviewer 1 ·

Basic reporting

No comment

Experimental design

No comment

Validity of the findings

No comment

Comments for the author

All my previous concerns/comments have been satisfactorily answered. I have no further comments.

Reviewer 3 ·

Basic reporting

The authors made modifications which improved my understanding of the nature of the reported results. Some of my concerns remain. My main concern pertained to the clarity of the stated objectives (see below for details). To this end, I understand that no modification was made by the authors. Consequently, my troubles relating the reported results to the objectives remain. Of relatively lesser importance is the text’s lack of direction. A lot of information is given and it would help a lot if the authors explained, before the Results section, how they chose which information to report and the purpose of reporting this information. I also had a bit of difficulties finding the pieces of information which I thought most relevant to the paper.

I leave it to the editor to decide if the latter issues are important enough to be tackled.

Objective
To be more precise, I struggled understanding the last part of this sentence: “The objective of this study is to conduct a systematic review of multi-scale HIV immunoepidemiological models to infer the synergistic dynamics of HIV prognoses at the individual level and the transmission dynamics at the population level.” Is this what the authors meant: “The objective of this study is to conduct a systematic review of multi-scale HIV immunoepidemiological models to better understand how the evolution of the virus within the hosts could impact the transmission of the disease between individuals, and vice-versa.” ?

Please clarify in the text.

Results
Most of the Results section focuses on technical aspects of the modeling approach. What is the general purpose of presenting this information? Please provide a rationale in the text. In my mind and if I understand the objectives, the authors wished to inform the synergy between within- and between-host dynamics. If this is the case, then model inferences can inform this synergy (Table 1), not the modeling approach. The modeling approach, on the other hand, informs the reader of the validity of model inferences (see suggestion below for details). Although it is quite possible that this was the authors’ intent in presenting the modeling approach, how the results are presented does not help the reader make the appropriate assessment of inferences.

Accordingly, here are some modification suggestions:

- Most models have a great number of underlying assumptions (e.g. homogeneous populations, deterministic/stochastic nature of a relationship, etc.). Please mention why the specific model assumptions were chosen to be reported in the objectives. As an example, you could write: “Inferences from mathematical models are sensitive to model assumptions. To enable a better assessment of the validity of inferences, we will report the model assumptions that we believe had a larger impact on model predictions.”

- In the Results section, it would be advisable not to separate data-driven facts, model assumptions and model inferences, and make it clear which is which. As an example based on lines 275-279 (this is not meant to be accurate!): “There is evidence suggesting that deploying therapeutic interfering particles in even a small proportion of infected individuals reduces the prevalence of HIV to low levels. (ref=longitudinal study) This is thought to be due to TIPs’ ability to transmit between hosts and and target high-risk groups. (ref=source of this opinion) In one immunoepidemiological model, this was modelled by assuming that the susceptible population infected by TIP carriers become TIP carriers themselves, and those have one-half the transmission rate of a non-TIP carrier.(ref=model paper) This assumption was supported by clinical data. Results from analyzing this model suggest that TIP may reduce the challenges of ART therapy and vaccines, and can be complementary to both.(ref=model paper) This is due to [particular aspect of the model dynamics] and adds to the current knowledge ...”

- If I understood your objectives correctly, I suggest describing the most important information relative to your objectives in the text rather than in a table (e.g. Table 1). I would put less relevant technical aspects of the modeling approach in Appendix.

Experimental design

no comment

Validity of the findings

no comment

Version 0.1 (original submission)

· · Academic Editor

Major Revisions

Dear authors,

The three reviewers have indicated several concerns about your paper, which you should addressed before publication. Therefore, my decision is MAJOR REVISION.

With respect and warm regards,
Dr Palazón-Bru (academic editor for PeerJ)

Reviewer 1 ·

Basic reporting

Clearly written. Manuscript's structure is appropriate.

Experimental design

Goal and justification of this study, as a systematic review, are clearly stated. Methodology of systematic review is well presented.

Validity of the findings

Conclusions are well stated.

Comments for the author

1. Assuming viral shedding as part of the basic viral dynamics model (line128, page 7; Table 2) is uncommon. A more representative example is found in Perelson 2002, Modelling viral and immune system dynamics, Nature Reviews.
2. Do not use "et al." when there are only two authors; e.g. "Martcheva and Li" instead of "Martcheva et al" (line 134, page 8)—also in lines 140,153, 181, 220, 222, 238.
3. Adding a formulation of model's summary for within host scale models for "HIV evolution" (lines144-149) and "HIV and therapeutic interfering particles" (lines 158-164) would improve the introduction of models (similar to Tables 2, 3, etc.).
4. Note that Saenz & Bonhoeffer (2013) also consider acute, latent and late stages of HIV infection, so this should be mentioned in the corresponding section (lines 177-185).
5. In section "HIV transmission rate as a function of viral load" (lines 212-233), mention the different types of functions that are used (e.g., linear, Hill's).
6. A better reference for the sentence "The viral load (and thus the transmission rate) is high during the acute and late stages of HIV infection while being low during the latent stage" (lines 214-216) is: Hollingsworth et al. 2008, HIV-1 transmission by stage of infection, Journal of infectious diseases.
7. A few typos in References list (e.g., "hiv" in line 376).
8. Models diagrams (Tables 2, 3, 4, 5, 6, 7) should include state variables when stating rates, otherwise they are inconsistent (e.g. "k" is not a rate in the same sense as "d").
9. The reproduction rate of T related to Tm is missing in the model diagram of Table 5.
10. Note that not all studies considered in the manuscript used the function r*V(t) as implied in Table 8.

Reviewer 2 ·

Basic reporting

'no comment'

Experimental design

'no comment'

Validity of the findings

'no comment'

Comments for the author

The manuscript is well organized. Thank authors for writing the manuscript carefully.

1. The manuscript is a review article. The review was done in a systematic way as described in lines 102 and in Figure 2.
2. The authors reviewed 9 articles selected from 89 found in the PubMed database. The inclusion and exclusion criteria were clearly defined in the METHODS section (line #98).
3. The authors summarized the results found in those selected papers and stated in the RESULTS section starting at line # 109. One of the key findings is the within host viral load that enhances between host infection. They also highlighted the impact of super infection, antiretroviral therapy, drug resistance, treatment at early or late stages on HIV infection and prevalence.
4. The authors though saw the increased complexity of the multi-scale modeling, but found significant public health impact of these models.
5. This review should be helpful for further studies to eliminate the long time persisted disease HIV-AIDS.

With regards to your concern “regarding the assessment of the experimental design or the validity of the findings were provided”- The authors of the manuscript did not conduct any experiments. So, there was no issue of validation of findings. However, they have followed a systematic review process that I mentioned in my opinion (1,2) above.

Based on my findings I have no objection to accept this manuscript for publication.

Reviewer 3 ·

Basic reporting

The authors are making a systematic review of the use of immunoepidemiological mathematical models of HIV. Overall, they found 9 articles where such models were used. Although the paper contains interesting pieces of information, the basic reporting would require modifications. A clear definition of the objectives is likely the most important issue, followed by a need for synthesis and better cohesion between the elements of this paper.
Without a clear understanding of the review’s objectives, it was difficult to assess both the relevance and adequacy of the results. I assumed that the authors meant to review how, in the mathematical models, the within-host evolution of HIV was considered to affect the transmission of the disease at the population level. To that regard, it was difficult to find this information in the paper. In terms of cohesion, working on the link between the pieces of information that are reported would help, as the sentences sometimes gave the impression to be “floating”, like bullet points. A clearer definition of the objectives would greatly help in that regard, as the information could be linked back to these objectives throughout the paper and hence give a better sense of direction to the reader. The authors could also consider discarding some of the information that is given, depending on these objectives.

In terms of the context, the authors should consider elaborating on the clinical relevance of immunoepidemiological models. Maybe a good way would be to describre models that consider, separately, the within-host or the between-host HIV dynamics, especially the questions they were able to answer but mostly those that cannot be answered because they are not considered altogether. Finding more precise and clinical research questions relative to what is written in lines 77-80 could help improve motivation. Also, is it the first time immunoepidemiological models are reviewed in the context of HIV?

As for the Results section, is it possible that some sentences from the Results were statement of facts or data-driven conclusions (e.g. lines 133-134, 159-160, 178-180, 187, 191-192), rather than model elements of the reviewed paper? If this is the case, these sentences would be a better fit in the Introduction. I would avoid dividing the results in very short sections and work on synthesizing the information. Some statements were ambiguous, e.g. sentences like “… can be modeled similar to co-infection (Metzger et al. 2011)… (line 161)” left me to wonder if the referenced authors are the ones stating that it could be modeled, or that they actually included this element in their model and, if so, if they were the only one considering TIPs. I believe important that the authors identify these sentences and make more precise statements. I suggest systematically reporting the frequency of papers having considered which specific model element and why they have done so. Maybe a table with one line per model and check marks for elements considered (columns) could help?

Table 1 contained the most interesting information. I suggest elaborating more on the important aspects of it. Consider discarding Figure 1, Tables 2 to 9, which did not bring much to the reading, unless they can be related to an objective. Table 9 could help with motivation (see above comment), but I would also add why it is important to assess these questions.

Experimental design

The general methodology seems adequate for a systematic review. However, I was wondering if the authors considered using MeSH terms, from PubMED; this is a great tool to find all terms related to concepts. Also, I suggest looking at other databases, as mathematical models can be underrepresented in PubMED.

Validity of the findings

I was unable to relate the discussion to the objectives, for the issues stated above.

Comments for the author

No further comment.

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