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Thanks for revising your manuscript and following all the reviewers’ comments. I am pleased to inform you that the final version is accepted and suitable for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Dezene Huber, a PeerJ Section Editor covering this Section #]
The methods and results of the study are clearly reported.
The experimental design is sound.
The findings are valid, although it is disappointing that the authors seem interested in limiting some aspects of the discussion.
It is unfortunate that the authors seemed more inclined to argue many suggestions for additions or language clarity. But I respect the authors' right to control the way the manuscript is presented, despite my disagreements. Overall it is an excellent contribution.
I'm happy that the reporting criteria have been met
as before - was well thought out
useful information and valid to the field of biosecurity
The author line number used in the response letter did not always match the changes indicated - but I was usually able to find the changes. Although some of the comments to address my queries did not seem to have been reflected in the manuscript text, even though i think they would have made a useful addition, it is clear the authors have a vision and an idea of the knowledge they want to impart so i am happy for this manuscript to be accepted and published as is.
Based on my review of the revised manuscript and the authors' detailed responses to the previous comments, I find that the authors have satisfactorily addressed all the major concerns raised in the initial review. The revisions have significantly improved the clarity, accuracy, and overall quality of the paper. The authors have provided clear explanations, corrected typographical and numerical errors, and enhanced the structural organization of the manuscript.
Are standard and sound.
he revisions have significantly improved the clarity, accuracy, and overall quality of the paper. The authors have provided clear explanations, corrected typographical and numerical errors, and enhanced the structural organization of the manuscript.
N/A
This manuscript is well-written and follows proper methodology and reliable results.
Kindly check the attached reviewers' comments. As minor revisions are required modify clearly in your article and resubmit your revised version.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
I think this manuscript had a very clear and achievable goal of comparing centrality metrics within a very difficult pathosystem, and it very much achieved this goal. I also thought it was very well put-together as a manuscript. I have very few comments.
Are there any limitations to the study based on the sentinel network itself? States or counties with low or no facilities to report disease? Are there any defficiencies in the network? In other words, I'm interested in the authors speculating on how data you haven't collected might have affected your study.
104-106 Is there no role for oospores in CDM epidemics?
113-115 was this fungicide reduction actually observed, or just theoretically possible? It seems as if growers rarely trust these forecasting models and spray regardless.
94-121 I think this paragraph should describe how the pathogen disperses, ie how much is due to natural spread vs anthropogenic movement of plants or propagules. This comes up later when you are creating your dynamic network
103-108 This phenomenon reminds me of the northerly movement of Puccinia propagules in the midwest.
168 I am interested in how the windspeed was measured. Is this an average value or a maximum? I ask because we are talking about large propagules, and logically it seems as if "gustiness" could be a very important. How about precipitation? Phytophthora ramorum can be dispersed extremely long distances via wind-blown rain.
247-263 This paragraph mixes some results into the methods. Starting with line 255 it is not clear if the processes being described apply to both the first and second approaches described at the start of the paragraph.
268-270 rephrase this sentence
314-316 could this choice affect the performance of the centrality measures? If so, please state this either in the methods or discussion.
328-330 Flesh this out a little more
332-334 This sentence seems out of place - put it in the results section
533 "Removal of these nodes can contain an epidemic (Ezeoke et al., 2018), as was observed in this study." This, and the following paragraph needs to be rephrased - this is speculation within the framework of network analysis, yet it could be interpreted as stating that we could stop the spread of CDM by eradicating disease in certain locations, which I don't believe is a realistic solution, or a result supported by this study. You need to make it abundantly clear that the outcome of this study would be greater disease surveillance, not a reduction in the amount of disease.
564 rephrase this sentence
Generally I think the language could use some tightening up in terms of grammatical correctness and ease of reading. I have been through the PDF and have made detailed comments throughout where I think specific things could be changed. Sometimes the way it is written, the meaning is ambiguous, so more precision for clarity is recommended.
Corrections suggested: (see also attached document with comments)
Introduction
Line 48-50 - This could be phrased better ; for example: Dispersal properties of the pathogen are fundamental to progression of disease in an epidemics The transmission of XXXX and the resultant epidemic spread.
Line 52 “Measures that involve containment” - Include might be a better word
Line 53-54 Does this mean control measures? Specify for clarity
Line 55 “ Such efforts” – does this mean control efforts – specify for clarity
Line 57 - I suggest you rephrase for better grammar and English purposes - something like; analyses can help INFORM the designing of control programmes OR they can help decision makers OR can be helpful in designing …
Line 67 “potential of encounter” - Unclear what is meant - potential of encounter with a pathogen? Exposure to a pathogen? Could even be "encounter with"
Line 69 “been used to describe the spread of epidemics” - An epidemic by definition is spread of a pathogen...so it may be preferable to say "epidemic spread of...."
Line 88 “connected nodes provide more effective surveillance and opportunities for more targeted control” – I think this “more” is unnecessary.
Line 90 - Important twice in a row - I would replace with "….measures are crucial for…" or "….measures are key to identify
Line 100 - Adequate fungicide resistance? Or just leave the word adequate out.
Line 112 – 119. It is unclear to me here whether the focus of this paragraph is reducing the cost of the control (fungicide application) or the reducing the cost of the surveillance programme - both are relevant but for the point of the paragraph please clarify or restructure.
Methods
Line 138 and then line 162 vs line 164 –Epidemic vs cases? Were these actually epidemic records or just records of occurrence? An epidemic is different from a case. Please clarify
Line 139 and 151 – Voluntary or passive reporting. What was the mechanism by which these voluntary reports were collected - for example was there a passive surveillance system that was set up specifically to gather incidental reports nationwide, or did the researchers simply approach companies and ask them to report cases to them?
Line 164 and 166 – counties vs disease reports. Please clarify - how can a number of counties influence the number of disease reports?
Line 166 active surveillance sites to number of counties. Similarly to previous comment - it is unclear what one is looking for here. Please clarify.
Line 190 – should this be ‘source’ nodes?
Line 205/206 – were vs was/ parameter vs parameters. Please fix grammar
Line 208/ 210 – grammar – ‘that” study. Which study? The last study referred to was the present study - but I presume the Ojiambo study is what is meant here - so please clarify and state clearly in the sentence.
Line 210 – corresponding epidemic years - what exactly is meant by "corresponding epidemic years"? Please clarify.
Line 211 – 213 Threshhold probability vs prob of transmission. These sentences would benefit from rewriting for clarity, The sentences could be broken up and an explanation in words as well as an equation. From reading above - it seems that Y = probability of transmission from node i to node j - which means that the equation is saying that certain criteria need to be fulfilled to create a link - which are, distance and that the probably of transmission is greater than the threshold probability. It would be useful to explain how the threshold probability is calculated, as this seems key since it is referred to a lot later on.
Line 217 – Fig S4 needs better more detailed titles and captions and please provide an explanation of giant component
Line 240 – Disease outbreak - is this term being used as synonymous with epidemic? I would choose one and stick to it. Otherwise be precise where each of these terms is being used.
Line 242 – Node observation - does this mean whether a certain node was involved in the disease spread? Clarify this language as observed and involved is not quite the same thing.
Line 243/244 – grammar -leave out the “and”
Line 256/257/258 – recurrently between years? Please clarify. Make a list out of weighting approach criteria which will make it easy for the reader
Line 261 – Figure S5 - labelling on S5 figures need to be much clearer - remember all figures should stand alone and one should not need to refer back to other information (the figure refers back to a table at one point). The use of the word "It" in these figures is unclear and the red and pink colours are not easily discernible by the eye.
Line 306 – healthy nodes? Does this mean uninfected nodes? If yes use the same language throughout as you did previously - which was 'non-infected' for consistency
Line 314/315 – Sensitivity (predict infection) vs specificity (correctly predict non infection) - could add the terms sensitivity and specificity commonly used in epidemiologic studies
Line 331 – V explanation would help so that the reader doesn’t have to go back in the manuscript to remember what V is.
Results
Line 349 to 351 – relevance of increasing disease reports to increased number of infected counties - isn't this obvious and expected? this goes back to my comment earlier about perhaps explaining what is being looked for here?
Line 352 – disease was present? Ascertained by incidental reporting whether or not targeted surveillance was occuring? Please clarify.
Line 354 – epidemic extent?? Define
Line 369 – threshold value – define and explain how is it generated, and what it is indicative of – as mentioned previously.
Line 408 – Fig S7 - explanation of selection of years chosen to display? I didn’t see an explanation of why only 2008, 2011, 2014, 2016 were used? there is no explanation of this in the text or in the figure title or figure explanation
Discussion (almost reads like it was written by a different person – the style seems different)
Line 497 – scale free – Should this term be explained/ defined?
Line 524 – why is 2016 different? Any ideas why this might be? Perhaps add some thoughts.
Line 532 – indicate (should not be plural)
Line 534 – given this paper was about generating models based on data this may be better phrased as “as models suggested”, not observed.
Line 566-567 – this sentence could be rephrased with better grammar for ease of reading.
This is a very interesting and useful analysis. The methods are well thought out and the aims are clearly stated.
It would be helpful to explain why only subsets of the data are used or shown. Why are some data disregarded?
Showing all the variables built into the analysis in tabular form would be useful.
A clear definition of all the terms used (not just the centrality measures) would be helpful as epidemiological, ecological and modelling terms can be interchanged and for clarity for the reader being precise about which term is being used for which purpose in this manuscript would be helpful. I have noted several of these in my detailed line by line comments.
Line 590 – the fact that the results may not be generalisable is an important point and warrants some further explanation. Generalisation how far beyond the study, if at all? Why not? Why are there limitations? I commend the authors for assessing this and understanding the limitations of the results and I encourage them to expand the explanation of this further.
This is by definition in epidemiology part of the validity of the findings – where and how can these results be applied. I think an explanation of how these results can be used in changing control measures would be useful and thoughts on why, or why not something similar could be applied to other plant (or other) pathogens.
I think the findings need to be summed up better and linked back to the stated objectives at the start and examine how they have been fulfilled (or not).
The discussion hasn’t factored in the environmental factors such as wind direction and speed even though there are statements that spread is south to north. A northward expansion seems like a key point - yet there is no explanation of why this might be expected or needs to be taken into account.
This is an interesting manuscript and very useful in the field of biosecurity and epidemiology, but for better uptake of these results the application of thee results should be better spelled out and perhaps illustrated by a theoretical example when wrapping everything up at the end and tying it into a nice neat conclusion.
I had the opportunity to evaluate the manuscript titled "Understanding Pathogen Dispersal: A Network Analysis of Cucurbit Downy Mildew Spread in the Eastern United States."
find the paper to be well-written, addressing an important area of plant pathology that has been relatively underexplored. I commend the authors for their contribution to this field.
The paper investigates the dynamics of disease spread caused by cucurbit downy mildew (CDM), an invasive plant pathogen with significant implications for agricultural productivity and ecosystem health. The authors employ a network analysis approach to explore the transmission patterns of CDM across different regions and epidemic years, shedding light on the factors influencing disease dissemination. Through a combination of historical epidemic records, mathematical modelling, and network centrality metrics, the study aims to identify key nodes and pathways facilitating disease spread, ultimately contributing to the development of targeted surveillance and management strategies. In this review, I provide an assessment of the manuscript's strengths, weaknesses, and suggestions for improvement to enhance its scientific rigor and impact.
Introduction:
The introduction of the paper provides a comprehensive overview of the significance of pathogen dispersal in the context of disease epidemics, particularly focusing on the transmission of invasive plant pathogens and their impact on ecosystem services. The authors effectively highlight the importance of understanding the mechanics of invasions and the ecological consequences of disease spread for designing effective control measures.
However, I noticed limited discussion on practical implications and stakeholder engagement. While the introduction briefly mentions the practical implications of the study for disease surveillance and management, it could benefit from a more extensive discussion on stakeholder engagement and the potential impact on agricultural practices. Exploring how the findings could be translated into actionable strategies for growers or policymakers would enhance the relevance of the study.
Methodology:
The Methodology section presented in the manuscript appears to be comprehensive and well-structured. The authors have provided detailed explanations for each step of the analysis, which enhances the transparency and reproducibility of the study.
Justification of Approaches: The authors have effectively justified the approaches taken in the study. They have provided rationale for the selection of methods, such as the choice of centrality metrics, identification of important nodes, threshold values for network construction and accounted for error and sensitivity analysis. This strengthens the validity of the research findings.
Additional Considerations: While the methodology is generally well-explained, there are a few areas where further clarification or consideration may be beneficial.
Results:
The methodology described for determining the connectivity threshold (Ç) and generating static networks for the analysis of cucurbit downy mildew appears to be logical and well-justified. In addition, the results section provides a comprehensive overview of the variability observed in centrality measures across epidemic years and the selection of important nodes within the static networks.
The results also effectively describe temporal and spatial patterns observed in disease spread across different epidemic years. The analysis of probabilities of transmission between nodes and the northward expansion of the epidemic front provides valuable insights into the dynamics of disease spread over time and space. The interpretation of results is insightful, particularly regarding the increasing probability of infection over time and the northward expansion of the epidemic front. The discussion of nodes with relatively low infection probabilities in Illinois and Michigan by week 35 adds nuance to the understanding of disease spread dynamics within the eastern United States.
Discussion:
Overall, the discussion section effectively synthesizes the study's findings and provides valuable insights into the spread of CDM in the eastern United States. It presents a balanced analysis of the results while acknowledging limitations and suggesting directions for future research.
However, I think the manuscript could benefit from discussing shortly these two:
Discussion of Outliers: The presence of outliers in the distribution of centrality metrics, particularly the power-law distribution observed for betweenness centrality (BWC), warrants further discussion. It would be important to discuss the potential significance of these outliers and their potential impact on disease spread dynamics within the networks. For example, outliers with exceptionally high BWC values may represent nodes that play critical roles in facilitating disease transmission or acting as bridges between different parts of the network, or not? Understanding the influence of these outliers could provide insights into strategies for targeted disease surveillance and control.
Comparison with Static Network: The comparison between the dynamic network model and the static network representation of disease spread adds depth to the analysis. By highlighting differences between the two representations, the section emphasizes the dynamic nature of disease spread and the importance of considering temporal and spatial factors in modelling epidemics. For instance, while static networks capture connectivity patterns at a single point in time, dynamic models account for changes in network structure over time, allowing for more accurate predictions of disease spread trajectories. Discussing the strengths and limitations of each approach can provide a comprehensive understanding of disease dynamics and inform future modelling efforts.
Figures:
In Figure 3 caption (and line 426), the authors mention that "In all years, the initial source of disease outbreak was in Miami-Dade County (open square) in southern Florida". Would that suggest controlling the initial source would be crucial for disease control or there are other elements/justifications that authors haven't covered this in the discussion? Is it because the node in that area had a lowest weight? What might be the reason? I think even though a node may have a lower centrality value, its control could still disrupt disease transmission pathways or prevent secondary outbreaks in nearby regions. Therefore, while centrality measures provide valuable insights into network structure and potential disease spread pathways, they should be considered alongside other factors when devising control strategies.
Please see above.
Please see above.
Please see the pdf file attached for further comments.
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