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Reviewer 2: "Dynamics and Ecology of a Multi-stage Expansion of Oropouche Virus in Brazil"

Reviewers recommend a more rigorous methodological framework, including integrated phylogeographic analysis, improved spatial resolution, and explicit justification for key assumptions.

Published onJan 21, 2025
Reviewer 2: "Dynamics and Ecology of a Multi-stage Expansion of Oropouche Virus in Brazil"
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Dynamics and ecology of a multi-stage expansion of Oropouche virus in Brazil
Dynamics and ecology of a multi-stage expansion of Oropouche virus in Brazil
Description

Abstract In March 2024, the Pan American Health Organization (PAHO) issued an alert in response to a rapid increase in Oropouche fever cases across South America. Brazil has been particularly affected, reporting a novel reassortant lineage of the Oropouche virus (OROV) and expansion to previously non-endemic areas beyond the Amazon Basin. Utilising phylogeographic approaches, we reveal a multi-scale expansion process with both short and long-distance dispersal events, and diffusion velocities in line with human-mediated jumps. We identify forest cover, banana and cocoa cultivation, temperature, and human population density as key environmental factors associated with OROV range expansion. Using ecological niche modelling, we show that OROV circulated in areas of enhanced ecological suitability immediately preceding its explosive epidemic expansion in the Amazon. This likely resulted from the virus being introduced into simultaneously densely populated and environmentally favourable regions in the Amazon, such as Manaus, leading to an amplified epidemic and spread beyond the Amazon. Our study provides valuable insights into the dispersal and ecological dynamics of OROV, highlighting the role of human mobility in colonisation of new areas, and raising concern over high viral suitability along the Brazilian coast.

RR\ID Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.

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Review: While the study by Tegally et al. [10.1101/2024.10.29.24316328] addresses a relevant and timely topic, the current manuscript does not provide sufficient methodological detail or data robustness to justify its conclusions. With significant revisions, this study could provide valuable insights into the epidemiology of Oropouche virus (OROV).

Major comments: 

  • Lines 667–670: The procedure described in the Materials and Methods section demonstrates that the BEAST program was appropriately applied in this study to generate the results. However, a significant limitation of this type of analysis is that the outcomes are highly dependent on the dataset used (e.g., sampling imbalance). The dataset employed in this study does not represent a comprehensive sample of all OROV lineages circulating across their entire geographic range—particularly as the authors have not provided a complete list of the genomic data used in their analyses. Consequently, the results are influenced by biases inherent in the dataset, especially those arising from missing data. This limitation is particularly critical when reconstructing the dispersal history and dynamics of OROV lineages. I strongly recommend that the authors explicitly discuss the potential effects of incomplete OROV sampling on the reliability and interpretation of their findings. Additionally, the authors should include a complete list of the genomic data used, along with their accession numbers and associated metadata (e.g., how many samples come out of each site in Brazil), in the supporting information to enhance transparency and reproducibility. 

  • Line 421-423: The manuscript does not address the investigation of reassortment patterns within the dataset, which is a notable gap. Detecting segment reassortment events could provide valuable insights into the evolutionary processes shaping Oropouche virus (OROV) dynamics and may better inform the interpretation of phylogenetic and epidemiological findings. Reassortment is a critical mechanism in the evolution of segmented viruses, and its omission leaves an important aspect unexplored. Please clarify why reassortment events were not investigated in your dataset. If possible, consider incorporating analyses to detect reassortment events, as this could significantly enhance the study’s contribution to understanding OROV evolution. Additionally, I recommend including a paragraph in the Discussion section to detail this limitation and its potential implications for the study’s findings. This addition would provide a more balanced and transparent interpretation of the results.

  • Figure 1: The manuscript would be greatly enhanced by the inclusion of additional visualizations to complement the current analyses. Specifically, a detailed phylogenetic tree displaying the diversity of Oropouche virus (OROV) lineages, annotated with posterior probabilities, would provide critical insights into the confidence levels of the evolutionary relationships. In addition, integrating maps that illustrate the dispersal history and dynamics of OROV lineages for the three minigenomic segments within Brazil would offer a comprehensive view of both evolutionary and spatial patterns.

  • The phylogeographic analysis presented in the manuscript, based on a Bayesian approach, is separately conducted for the three minigenomic segments (S, M, and L). While this approach provides valuable insights, analyzing the segments together for samples with complete genetic information from all three minigenomic sequences could yield more robust results. Such a combined analysis might also offer additional support to the findings derived from each single genomic segment. I suggest conducting a joint phylogeographic analysis that integrates data from the S, M, and L segments for samples where all three sequences are available. Thus, it will provide stronger statistical support by leveraging the combined genetic information, and Offer a comparative perspective to validate the findings from the segment-specific analyses.

  • Line 164-243: The difference in spatial resolution between the genomic data and the raster data used to analyze the environmental conditions associated with Oropouche virus (OROV) lineage dispersal over time may introduce additional limitations. Specifically, if the genomic data were not sampled at the exact geographic coordinates used for the environmental data, this discrepancy could add an extra layer of uncertainty to the analysis. The spatial mismatch between these datasets may affect the precision of the inferred relationships between environmental factors and OROV lineage dispersal. I recommend discussing the potential impact of this spatial resolution discrepancy on the results.

  • Line 633-645: The manuscript would benefit from additional details regarding how the occurrence records used in the ecological niche modeling (ENM) were collected. It is essential to specify the methodology employed to gather these records and how each occurrence was assigned to its corresponding geographic coordinates. This information will help clarify the accuracy and reliability of the data used in the analysis. Clearly describe how the occurrence records were collected and assigned to its coordinate, with providing additional details including sources (e.g., field surveys, public databases, literature, or previous studies) and the time frame of data collection. For example, the link provided on line 636 didn't provide except general information on OROV transmission and didn't show any geographic references for OROV cases.

  • To enhance the transparency and reproducibility of the study, I strongly recommend that the authors provide the complete datasets used in the analysis—specifically the genomic data, occurrence records, and any scripts used for data processing and analysis. Since the results and conclusions are heavily dependent on the quality and accuracy of these datasets, making them available for review would allow for a more thorough evaluation of the study's methodology and findings. Including these datasets and resources as supplementary materials would significantly strengthen the manuscript by enhancing its transparency and reproducibility, and providing reviewers with the necessary tools to thoroughly assess the study’s validity.

Minor comments:

  • Line 168-169: The decision to select Aedes aegypti as the sole vector species for this study may not be sufficiently justified, particularly considering the well-established roles of other vector species in the transmission of arboviruses. Aedes aegypti has been widely studied and modeled in numerous other studies, and I found at least a few of these that critically evaluate the models used in this study. Given this background, it remains unclear why the authors chose to use Aedes aegypti alone as a covariate in their analysis.

  • Line 177: The authors mention the use of "high resolution," but do not specify the spatial resolution of the covariates used in the analysis. It is essential to clearly define the spatial resolution of these covariates so that readers can assess the suitability of the data for the modeling approach. Similarly, the manuscript does not address the temporal resolution of the data or how it correlates with the time of Oropouche virus (OROV) sampling. This aspect is important for understanding how the temporal dynamics of OROV transmission are represented in the models.

  • Line 218: Remove "human". The word "human" is duplicated. 

  • Line 252: The occurrence data used in the analysis appears to be limited to Brazil; however, the map presented in the manuscript shows the entire South American region. This discrepancy is notable, especially considering that Oropouche virus (OROV) has been sampled from other countries in South America. The limitation of occurrence data to Brazil raises questions about the representativeness of the analysis, particularly in the context of broader regional patterns of OROV transmission and dispersal.

  • Line 258: Specify the diameter of the radius implemented here. 

  • Line 289: Why 0.4 is used as a cutoff for thresholding the model. Please justify. 

  • Figure S3 as presented, does not provide meaningful insight and could be improved. Instead of this figure, I recommend replacing it with a reference to a link that provides the layers in GeoTIFF format. This would allow for greater transparency and reproducibility of the analyses, enabling other researchers to access and use the data for further study. 

  • Supplementary Table S3: Give a reference to the same link above. 

Comments
1
Jeio Breaath:

The review raises valid concerns about data completeness, phylogenetic methods, and transparency, which are crucial for ensuring the reliability of the study’s conclusions. Addressing sampling biases, reassortment analysis, and dataset availability would greatly enhance the study’s credibility and impact on Instagram fonts generator.