Bovine Respiratory Disease: Epidemiological Drivers, Transmission Dynamics, and Economic Implications in Beef Production Systems

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is a very verbose paper (25 pages) that lacks novel insight. Additionally, there are many statements that lack appropriate citations. 

Author Response

Comments and Suggestions for Authors

This is a very verbose paper (25 pages) that lacks novel insight. Additionally, there are many statements that lack appropriate citations. 

 

Author Response to Reviewer:
We sincerely thank the reviewer for the thoughtful and constructive feedback. In response, we have implemented the following targeted improvements:

Manuscript Focus & Rigor

We have streamlined the manuscript to improve conciseness and reduce redundancy.

We have thoroughly vetted and standardized the paper for potential errors in grammar, tenses, and expressions.

We went ahead to strengthen citation support across key sections, particularly in epidemiology, diagnostics, vaccination, and prevention.

We also want to clarify that the paper is a foundational framework to support ongoing quantitative modeling and algorithm development for BRD mitigation in the US beef production system.

Scientific Novelty & Contribution

While this paper is structured as a comprehensive review, its central contribution is in condensing the epidemiology of BRD into a single article that is forward-looking.  

This paper articulated a translational pipeline linking epidemiological insights and molecular diagnostics strategies to AI-enabled surveillance and precision livestock technologies, with explicit identification of validation bottlenecks and real-world implementation constraints.

Through implementation of feedback from other reviewers, we have made substantive improvements to the paper to further elucidate molecular diagnostics, genetic selection strategies, quantitative diagnostic performance metrics, and prioritized translational research gaps.

We believe these revisions significantly enhance the manuscript’s clarity, scientific rigor, and contribution to the BRD literature, and we are grateful for the reviewer’s feedback, which directly informed these improvements.

Reviewer 2 Report

Comments and Suggestions for Authors

Refer to the attached

Comments for author File: Comments.pdf

Author Response

Introduction

Line 37: Please update the economic valuation to 2024-2025 USDA data

Author Response:

The economic valuation has now been updated using the most recent 2024–2025 USDA statistics, ensuring the manuscript reflects current industry conditions (see revised text, Lines 37–44).

“The beef industry is critical to the US economy, contributing approximately 22% of agricultural cash receipts in 2024 [1], with cattle and calf receipts reaching $112.1 billion [2] It anchors a system generating $9.5 trillion in economic output and over 1 million jobs [3], supplies about 20% of global beef, exporting $10.45 billion in 2024, nearly 14% of total production [4]. 2026 beef output is projected at 25.27 billion pounds(lb.) [1]. Since beef production is a significant contributor to the US economy, numerous challenges persist that continue to threaten the industry’s growth and sustainability, chief among which is bovine respiratory disease (BRD)”

 

Line 48: The sentence - BRD is known to be endemic in North America‖ could benefit from recent prevalence data.

Author Response:

We have strengthened this statement by incorporating four recent peer-reviewed references documenting contemporary BRD prevalence across U.S. production systems, providing robust epidemiological support while maintaining narrative flow in the introductory section.

 

Lines 66-70: Suggest adding numeric evidence of vaccination adoption rate and its effect size.

Author Response:

To enhance scientific grounding while preserving the introductory scope of the section, we have added multiple peer-reviewed references reporting vaccination adoption rates and quantified reductions in BRD morbidity and mortality.

Lines 94-101: The transition sentence could mention the knowledge gap the review aims to address.

Author Response:

A new transition paragraph now explicitly articulates the central knowledge gaps this review addresses, particularly the integration of molecular epidemiology, herd-level dynamics, and emerging digital technologies in BRD management (Lines 95–100).

 

2.1 Etiology and Risk Factors

Lines 104-110: Include emerging pathogens such as Influenza D virus from recent papers.

Author Response:

This valuable detail has been added to the review. Recent literature on Influenza D virus and its contribution to the BRD complex has now been incorporated to reflect emerging etiological evidence (Lines 110–111).

 

Lines 121-123: Rephrase for readability, perhaps use a conceptual ―host–pathogen–environment‖ diagram reference (Figure 1).

Author Response:

Thank you for this suggestion. The paragraph has been restructured for clarity, and a conceptual host–pathogen–environment schematic (Figure 1) has been added to visually anchor this framework within the epidemiological discussion (Line 125).

 

2.2 Pathogenesis

Lines 129-133: Are there data on cytokine expression or immune pathway activation (e.g., IL-8, TNF-α) that support the mechanistic claim?

Author Response:

We have expanded this section to include specific molecular evidence, including IL-8, TNF-α, and innate immune pathway activation, with appropriate citations to strengthen the mechanistic explanation of BRD pathogenesis (Lines 131–139).

“The stressors amplify this effect by upregulating pro-inflammatory cytokines [29], particularly IL-8, TNF-α, IL-1β, and IL-6 within bronchoalveolar and peripheral blood cells [30]. Elevated IL-8 enhances neutrophil chemotaxis and mucus secretion [31], while TNF-α and IL-1β disrupt epithelial integrity, promoting bacterial adherence and colonization [32]. Virus-infected respiratory mucosal cells speed up bacterial adhesion. Bacterial agents involved in the BRD complex are normal commensal organisms normally found in the nasopharyngeal region. However, they produce virulence factors when they enter the lower respiratory tract, which promote further proliferation, tissue damage, and subsequent inflammatory responses [33].”

 

2.3 Diagnosis

Lines 162-170: Add diagnostic performance metrics (Se/Sp, PPV, NPV) for DART vs PCR to improve rigor.

Author Response:

Sensitivity, specificity, and predictive value estimates for DART scoring relative to PCR-based diagnostics have now been added to enhance quantitative rigor (Lines 188–197).

“While widely used for field diagnosis, its diagnostic performance remains modest: pooled estimates indicate a sensitivity of 62–67% and specificity of 63–70%, with a positive predictive value (PPV) and negative predictive value (NPV) of approximately 58% and 72%, respectively, when validated against postmortem lung lesions and antimicrobial treatment records [37,41,42]. Laboratory techniques, including microbial culture, serology, polymerase chain reaction (PCR), and gene sequencing, are also gaining wider usage in BRD diagnosis; however, an important consideration for their field applicability remains the turnaround time, i.e., the lag between sampling and reporting [40]. The popularity of quantitative PCR (qPCR) for etiological diagnosis is rising, with sensitivity and specificity values between 85–97% and 92–100%, respectively, depending on the pathogen and assay configuration [43-45]. “

 

Line 172: Please clarify the sample size and validation statistics for [21].

Author Response:

The manuscript now specifies that the referenced study evaluated 297 animals (149 BRD-positive, 148 controls), achieving diagnostic accuracy up to 85%, with robust metabolite discrimination including phenylalanine, lactate, β-hydroxybutyrate, tyrosine, citrate, and leucine (Lines 203–211).

One example is the work by [21], which utilized blood ¹H NMR metabolomics to identify BRD biomarkers in feedlot cattle and classify animals as infected or non-infected. Their study analyzed 297 animals (149 BRD-positive and 148 controls) and achieved diagnostic accuracies up to 85% when clinical signs were used as the reference standard, with consistent differentiation based on metabolites such as phenylalanine, lactate, β-hydroxybutyrate, tyrosine, citrate, and leucine. This multi-reference validation approach highlights the translational potential of metabolomic biomarkers for objective, field-deployable BRD diagnostics.

 

Line 199: Provide citation for lack of ―gold standard‖.

Author Response:

Two authoritative references have been added to support this statement (Line 233).

 

Line 202: The paragraph on AI/ML could be expanded with validation limitations, cross-validation metrics or overfitting concerns.

Author Response:

The discussion now explicitly addresses model validation challenges, including reliance on cross-validation metrics, risks of overfitting to heterogeneous datasets, and the need for robust external validation (Lines 238–244).

However, the translational reliability of such AI/ML-driven approaches remains constrained by limitations in model validation, including dependence on cross-validation metrics derived from heterogeneous or sparsely labeled datasets, and the persistent risk of overfitting to context-specific management or environmental conditions [64]. Addressing these challenges will require robust external validation across production systems and careful alignment between predictive performance and biological interpretability [65].

 

2.4 Herd Dynamics

Line 222: Can you discuss how ―super-spreading herds‖ are identified?

Author Response:

 We have added a concise explanation of superspreading mechanisms and their identification using network analytics, contact frequency, and pathogen load profiling (Lines 262–267).

Superspreading in cattle herds occurs when a few highly infectious animals, due to elevated pathogen shedding or frequent close contacts, transmit disease to many others [69]. These individuals often hold central roles in herd networks, amplifying within-herd transmission. Network and epidemiological models [70] identify them through contact frequency, clustering, and pathogen load analysis.

 

Line 260: Could authors specify data sources for PARAMETRA integration (country coverage, metadata fields, etc.)?

Author Response:

Additional details have been provided indicating that PARAMETRA integrates transmission parameters from over 50 datasets spanning 27 countries, with standardized metadata on host species, pathogen traits, and epidemiological context (Lines 306–309).

 

2.5 Prevention and Management

Lines 297-304: Add mortality or morbidity reduction percentages achieved through preconditioning.

Author Response:

Quantitative reductions in BRD morbidity and mortality attributable to preconditioning have now been included (Lines 350–354).

“Preconditioning is typically performed at least three weeks prior to shipping and significantly enhances calf health and performance. [79] found that preconditioned calves required 40–55% fewer BRD treatments and achieved higher sale prices, while [80] observed improved feeding behavior and resilience post-commingling.  Additionally, [77] reported a 50-60% reduction in morbidity and 30–35% lower mortality among preconditioned calves compared with non-preconditioned groups.”

 

Lines 305-313: Clarify antibiotic classes used in metaphylaxis and potential withdrawal implications.

Author Response:

The manuscript now explicitly identifies the major antimicrobial classes used in metaphylaxis and discusses withdrawal considerations relevant to residue management and regulatory compliance (Lines 322–328).

“The antimicrobial classes commonly applied include macrolides (tulathromycin, tilmicosin), fluoroquinolones (enrofloxacin, danofloxacin), and phenicols (florfenicol), chosen for their extended tissue persistence and efficacy against Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni [83,84]. Non-compliance with metaphylactic protocols, such as withdrawal times, or poor record-keeping, may lead to violative residues and antimicrobial resistance concerns.”

 

Lines 389–396: To enhance literature context, add one concise sentence (Section 3.1, during the discussion of epidemiological variation). This placement is ideal because that paragraph compares historical and contemporary morbidity data across studies, molecular epidemiology and herd-level modeling. Suggested ready to insert sentence:

―Recent regional syntheses (Pathogens, doi.org/10.3390/pathogens14060577) provide complementary evidence of similar epidemiological transitions, underscoring the cross-continental patterns of BRD prevalence and pathogen adaptation.‖

Author Response:

The suggested sentence has been incorporated to strengthen the cross-regional epidemiological context and reinforce global patterns of BRD adaptation (Lines 413–419).

“Seasonal and regional differences further shape BRD epidemiology. [86] reported culling and mortality risks reaching 100% and 31.9%, respectively, in a study encompassing over eight million animals, with higher disease risk between March and September. In contrast, [105] identified elevated BRD morbidity (12%) and mortality (5.6%) in the fall, correlating with seasonal antimicrobial usage patterns. Auction-derived cattle experience a particularly high risk. [26] recorded morbidity and mortality of 61.9% and 12.9%, respectively, reinforcing the impact of marketing, transport, and commingling stress.”

 

Recommendations

Lines 661-663: Indicate which translational gaps are priority (diagnostics, surveillance, policy). Lines 690-698: Clarify how multidisciplinary collaboration will be operationalized—through funded consortia or open databases.

Author Response:

We now clearly prioritize diagnostics, molecular surveillance, and data integration as key translational gaps (Lines 725–732).

“Greater translational research, linking laboratory findings with on-farm implementation, is therefore essential to bridge the divide between scientific discovery and industry adoption. Key translational priorities include the development of rapid, field-deployable diagnostic and molecular surveillance tools capable of integrating multi-omics [131] and precision livestock data to improve early BRD detection and response [37,131]. Additionally, harmonized data-sharing frameworks and supportive policy mechanisms are required to translate these technologies into real-world disease monitoring and management systems [132].”

The section on multidisciplinary collaboration now outlines practical implementation pathways, including funded consortia, multi-institutional initiatives, and open data platforms, supported by newly added project references (Lines 764–767).

“Ultimately, the next phase of BRD research demands multidisciplinary integration, bringing together veterinarians, molecular biologists, engineers, and data scientists under a systems-thinking framework. Emphasis should be placed on creating adaptable models that capture the epidemiological, immunological, and environmental complexity of BRD within intensive beef production. Funding mechanisms that support large-scale field trials such as the genome-to-phenome research and FarmGTEx project [136,137], computational tool development, and open-access data sharing [138] will be pivotal in accelerating progress.”

 

Reviewer 3 Report

Comments and Suggestions for Authors

• The review article titled "Bovine Respiratory Disease: Epidemiological Drivers, Transmission Dynamics, and Economic Implications in Beef Production Systems," offers a panoramic view of the current state of Bovine Respiratory Disease (BRD) management, economics, and emerging technologies in the U.S. beef industry.
• It critically sums up why current attempts have failed and strongly calls for the use of objective, data-driven technology to lessen the huge economic and social costs incurred due to the disease.
• The section on economic impact is crucial and well-supported, clearly justifying the need for continued research and intervention.
• The discussion on how molecular evolution and strain variation influence vaccine efficacy and diagnosis could be expanded further.
• The review does not deeply explore the practical application of genetic selection programs for BRD resistance. Discussion on breeding strategies based on genetic markers could offer preventable solutions.
• The strong emphasis on precision livestock technologies (PLT) and AI-enabled surveillance is timely, positioning these tools as the future for objective, early detection and management. While praising AI/PLT, the review is somewhat brief on the current state of molecular diagnostics (e.g., next-generation sequencing, or qPCR) which are necessary to ground the AI data. These tools are the foundation for rapid, objective identification of co-infecting agents, and their limitations in a field setting represent a crucial challenge.

Author Response

Comments and Suggestions for Authors

• The review article titled "Bovine Respiratory Disease: Epidemiological Drivers, Transmission Dynamics, and Economic Implications in Beef Production Systems" offers a panoramic view of the current state of Bovine Respiratory Disease (BRD) management, economics, and emerging technologies in the U.S. beef industry.
• It critically sums up why current attempts have failed and strongly calls for the use of objective, data-driven technology to lessen the huge economic and social costs incurred due to the disease.
• The section on economic impact is crucial and well-supported, clearly justifying the need for continued research and intervention.
• The discussion on how molecular evolution and strain variation influence vaccine efficacy and diagnosis could be expanded further.
• The review does not deeply explore the practical application of genetic selection programs for BRD resistance. Discussion on breeding strategies based on genetic markers could offer preventable solutions.

• The strong emphasis on precision livestock technologies (PLT)and AI-enabled surveillance is timely, positioning these tools as the future for objective, early detection and management. While praising AI/PLT, the review is somewhat brief on the current state of molecular diagnostics (e.g., next-generation sequencing, or qPCR), which are necessary to ground the AI data. These tools are the foundation for rapid, objective identification of co-infecting agents, and their limitations in a field setting represent a crucial challenge.

 

 

 

Response to Reviewer Comments on Manuscript

Author Response:

We thank the reviewer for their constructive comments and suggestions for our research paper. They are all excellent points, and we have made improvements on the manuscript as follows:

1. Expanding discussion on how pathogen molecular evolution and strain diversity influence vaccine performance and diagnostic sensitivity (See lines 114 - 153)

“Moreover, recent genomic and molecular epidemiology studies demonstrate that rapid viral and bacterial evolution, along with antigenic drift, significantly influence the epidemiological behavior and immune escape of key BRD pathogens [35]. In particular, strain-level variation in viral agents such as BVDV and BRSV has been shown to alter neutralizing epitopes, leading to fluctuating vaccine efficacy and inconsistent field protection [36]. Likewise, genomic diversification within M. haemolytica contributes to variability in virulence factors and impacts diagnostic sensitivity across production systems [37]. This ongoing molecular evolution underscores the necessity of continuous genomic surveillance and periodic vaccine reformulation to sustain diagnostic reliability and long-term immunoprophylactic efficacy against BRD in U.S. beef production systems.”

1. Adding a concise but substantive subsection on the practical application of genetic selection programs and marker-assisted breeding for BRD resistance (See lines 419 - 428)

“Importantly, long-term BRD prevention must also integrate genetic selection programs aimed at enhancing innate immune response, stress adaptability, and disease resistance [92]. Breeding programs based on genetic markers enable producers to reduce herd-level risk of BRD [93,94]. Studies [95,96] have shown that genetic variability in immune function and respiratory resilience traits, such as pulmonary macrophage activity, acute phase protein expression, and behavioral stress response, can be harnessed through genomic selection to reduce BRD incidence in feedlot cattle [95]. Genetic selection, therefore, serves as a strategic extension of herd management, complementing vaccination and nutritional interventions within a sustainable, multi-generational disease prevention framework.”

 

• Enhancing the molecular diagnostics discussion to clarify the foundational role of qPCR and next-generation sequencing in informing AI-driven surveillance while acknowledging their current field-level limitations. (See lines 725-733, 751 - 759)

“Greater translational research, linking laboratory findings (qPCR, multi-omics, and next-generation sequencing) with on-farm implementation strategies (PLT, etc.), is therefore essential to bridge the divide between scientific discovery and industry adoption. Key translational priorities include the development of rapid, field-deployable diagnostic and molecular surveillance tools capable of integrating multi-omics [131] and precision livestock data to improve early BRD detection and response [37,131]. Additionally, harmonized data-sharing frameworks and supportive policy mechanisms are required to translate these technologies into real-world disease monitoring and management systems [132].”

“Multi-modal sensor systems capable of monitoring parameters such as body temperature, rumination, movement, and respiratory rate, when combined with pathogen detection and biomarker analysis, could establish a new standard for real-time, predictive health surveillance in feedlot settings. Nonetheless, the economic feasibility of such technologies remains a major constraint, particularly for small and medium-scale operations. Future research must therefore address not only the technological refinement of PLTs but also cost reduction strategies, integration with molecular diagnostics techniques, industry-specific calibration, and demonstration of return on investment under commercial conditions.”

Reviewer 4 Report

Comments and Suggestions for Authors

Please see the attachment

Comments for author File: Comments.pdf

Author Response

This review titled “Bovine Respiratory Disease: Epidemiological Drivers, Transmission Dynamics, and Economic Implications in Beef Production Systems” (agriculture-4049205) presents the epidemiology, transmission routes, and prevention and treatment measures of Bovine Respiratory Disease (BRD) in beef production systems. It is a relatively comprehensive review with clear logic and proper writing. I have the following suggestions:

 

Author’s General Response:

We sincerely thank the reviewer for the careful technical evaluation of the manuscript’s presentation and formatting. All suggested editorial and stylistic improvements have now been implemented to enhance clarity, visual consistency, and compliance with journal guidelines.

 

Author’s response line by line

In the keywords section from Lines 30 to 32, some keywords such as “Bovine Respiratory Disease” are repetitive with the title. They can be deleted or replaced with other keywords to expand the search impact of this article.

Author Response:

Keywords have now been updated accordingly (Please see lines 30-32)
Keywords: Beef System, Feedlot Health, BRD Diagnosis, BRD Prevention, BRD Eco-nomics, Precision Livestock Technology, Antimicrobial Resistance, Herd Dynamics, Disease Surveillance

 

The colored image on the second page is recommended to be displayed in the form of a graphical abstract (GA).

Author Response:

The title GRAPHICAL ABSTRACT has now been added to the colored image. We have now converted the image into a graphical abstract per the reviewer's recommendation.

 

Lines 46–47, the capitalization of each letter in “Bovine Respiratory Disease (BRD)” is unnecessary. There are many similar issues in this article, which should be checked and corrected one by one.

Author Response:

We have now correctly standardized ‘Bovine Respiratory Disease (BRD)’ across the entire manuscript to ‘bovine respiratory disease’ except for the first usage where we have ‘Bovine respiratory disease’.

 

The table title on Line 182 should be placed above the table.

Author Response:

The table title has now been placed above the table as the reviewer suggested.

 

The figure on Lines 187–193 is in black and white. Since other figures are in color, it is suggested that this figure also be displayed in color to achieve better overall coordination.

Author Response:

All black and white figures in the paper have been replaced and updated to coloured display for overall coordination

 

Lines 398–404, the content of the last two columns in Table 2 can be considered to be merged into “Report Source” or “References.” If it is necessary to highlight the significance of the year here, the names of the authors can also be listed, as the literature itself includes the author and the year.

Author Response:

The last two columns on Table 2 have been merged into ‘References’ as suggested by the reviewer.

 

The use of people's names as the x-axis to display the results in Figure 4 on Lines 423–427 is inappropriate. Other forms of the x-axis should be considered, and it should be presented in color.

Author Response:

The x-axis on figure 4, now figure 5 have been replaced with the year of publication and author names removed as suggested.

 

The font size and type of the text in Figure 5 on Lines 536–539 are not coordinated with the previous ones and need to be consistent or as consistent as possible.

Author Response:

Font size and text type in Figure 5, now Figure 6 has been coordinated with the previous ones for consistency.

 

Line 781, The reference list needs to be modified and standardized according to the journal's requirements. For example, the journal names should be abbreviated, and the titles should generally have only the first letter capitalized, with the rest in lowercase. The accuracy and standardization of the titles are also important. For instance, it is questionable whether the appearance of “400” at the beginning of the title in reference 103 is correct and standardized.

Author Response:

We thank the reviewer for highlighting this anomaly. We have now correctly updated all references in line with the standard journal requirements.

 

I look forward to seeing the authors' improvements. Good luck!

Author Response:

We thank the reviewer for their time and efforts.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

All comments have been addressed

Reviewer 4 Report

Comments and Suggestions for Authors

The authors have made substantial improvements based on the previous comments. Only one item remains to be perfected: The journal names in the reference list should be abbreviated. For example, the journal name "Journal of dairy science" in reference 16 on Line 898 should be written as "J. Dairy Sci." These formatting changes can be completed during the proofreading stage. Congratulations!