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Epidemiology of Cattle Diseases: Data Collection and Analytical Methods for...
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Epidemiology of Cattle Diseases: Data Collection and Analytical Methods for Disease Control and Sustainable Production

A special issue of Pathogens (ISSN 2076-0817).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 5692

Special Issue Editor

Prof. Dr. Michael W Sanderson

E-Mail Website
Guest Editor
Epidemiology and Beef Production Associate Director, Center for Outcomes Research and Epidemiology W.S and E.C Jones Professor of Epidemiology, Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS, USA
Interests: application of epidemiologic principles; analytical methods; risk assessment and infectious disease modeling to decrease disease risk and improve decision making in livestock production systems

Special Issue Information

Dear Colleagues,

Projections of rising global population, income, and increased meat and milk consumption highlight the importance of livestock production to meet the nutritional needs of the human population. Calls for intensification of livestock agriculture to meet the increased demand need to be undertaken with care toward managing disease incidence and the economic and environmental sustainability of livestock enterprises. Epidemiologists have long recognized that the occurrence and control of animal disease involves more than just the etiologic agents. The application of epidemiology is needed to understand the complex interplay of the agents, hosts, environments, and human interactions in management to produce or prevent disease.  The tools for applied and analytical epidemiology are necessary to address these complex and multifaceted aspects of disease causation and its impacts on production, the environment, and economic outcomes. Current technology is rapidly advancing our ability to collect data from environments, livestock systems, herds, and individual animals. It is critical that we assure collected data is valid, and that we use valid methods to analyze and interpret this data. Analytical methods are also advancing and we need to apply new methods to best utilize data.

This Special Issue aims to address the advances in valid data collection and the availability and advances in analytical tools to analyze and interpret cattle disease risk and impacts. We encourage the submission of data collection methods and the application of analytical methods, including novel analytical methods, to take best advantage of available data. Diverse impact measures including disease, production, economic, sustainability, and environmental are welcomed.

Original research, synthesis research, and targeted reviews are welcome.

Prof. Dr. Michael W Sanderson
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pathogens is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • epidemiology
  • cattle
  • infectious disease
  • analytical methods

Published Papers (3 papers)

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Research

11 pages, 313 KiB  
Article
Ecological and Anthropogenic Spatial Gradients Shape Patterns of Dispersal of Foot-and-Mouth Disease Virus in Uganda
by Anna Munsey, Frank Norbert Mwiine, Sylvester Ochwo, Lauro Velazquez-Salinas, Zaheer Ahmed, Luis L. Rodriguez, Elizabeth Rieder, Andres Perez and Kimberly VanderWaal
Pathogens 2022, 11(5), 524; https://0-doi-org.brum.beds.ac.uk/10.3390/pathogens11050524 - 29 Apr 2022
Cited by 1 | Viewed by 1733
Abstract
Using georeferenced phylogenetic trees, phylogeography allows researchers to elucidate interactions between environmental heterogeneities and patterns of infectious disease spread. Concordant with the increasing availability of pathogen genetic sequence data, there is a growing need for tools to test epidemiological hypotheses in this field. [...] Read more.
Using georeferenced phylogenetic trees, phylogeography allows researchers to elucidate interactions between environmental heterogeneities and patterns of infectious disease spread. Concordant with the increasing availability of pathogen genetic sequence data, there is a growing need for tools to test epidemiological hypotheses in this field. In this study, we apply tools traditionally used in ecology to elucidate the epidemiology of foot-and-mouth disease virus (FMDV) in Uganda. We analyze FMDV serotype O genetic sequences and their corresponding spatiotemporal metadata from a cross-sectional study of cattle. We apply step selection function (SSF) models, typically used to study wildlife habitat selection, to viral phylogenies to show that FMDV is more likely to be found in areas of low rainfall. Next, we use a novel approach, a resource gradient function (RGF) model, to elucidate characteristics of viral source and sink areas. An RGF model applied to our data reveals that areas of high cattle density and areas near livestock markets may serve as sources of FMDV dissemination in Uganda, and areas of low rainfall serve as viral sinks that experience frequent reintroductions. Our results may help to inform risk-based FMDV control strategies in Uganda. More broadly, these tools advance the phylogenetic toolkit, as they may help to uncover patterns of spread of other organisms for which genetic sequences and corresponding spatiotemporal metadata exist. Full article
(This article belongs to the Special Issue Epidemiology of Cattle Diseases: Data Collection and Analytical Methods for Disease Control and Sustainable Production)
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12 pages, 225 KiB  
Article
Predictive Models for Weekly Cattle Mortality after Arrival at a Feeding Location Using Records, Weather, and Transport Data at Time of Purchase
by Lauren Wisnieski, David E. Amrine and David G. Renter
Pathogens 2022, 11(4), 473; https://0-doi-org.brum.beds.ac.uk/10.3390/pathogens11040473 - 15 Apr 2022
Cited by 1 | Viewed by 1348
Abstract
Feedlot mortality negatively affects animal welfare and profitability. To the best of our knowledge, there are no publications on predictive models for weekly all-cause mortality in feedlot cattle. In this study, random forest models to predict weekly mortality for cattle purchase groups ( [...] Read more.
Feedlot mortality negatively affects animal welfare and profitability. To the best of our knowledge, there are no publications on predictive models for weekly all-cause mortality in feedlot cattle. In this study, random forest models to predict weekly mortality for cattle purchase groups (n = 14,217 purchase groups; 860,545 animals) from arrival at the feeding location (Day 1) to Day 42 and cumulative mortality from Day 43 until slaughter were built using records, weather, and transport data available at the time of purchase. Models were evaluated by calculating the root mean squared error (RMSE) and accuracy (as defined as the percent of purchase groups that had predictions within 0.25% and 0.50% of actual mortality). The models had high accuracy (>90%), but the RMSE estimates were high (range = 1.0% to 4.1%). The best predictors were maximum temperature and purchase weight, although this varied by week. The models performed well among purchase groups with low weekly mortality but performed poorly in high mortality purchase groups. Although high mortality purchase groups were not accurately predicted utilizing the models in this study, the models may potentially have utility as a screening tool for very low mortality purchase groups after arrival. Future studies should consider building iterative models that utilize the strongest predictors identified in this study. Full article
(This article belongs to the Special Issue Epidemiology of Cattle Diseases: Data Collection and Analytical Methods for Disease Control and Sustainable Production)
14 pages, 2626 KiB  
Article
Predicting Bovine Respiratory Disease Risk in Feedlot Cattle in the First 45 Days Post Arrival
by Hector A. Rojas, Brad J. White, David E. Amrine and Robert L. Larson
Pathogens 2022, 11(4), 442; https://0-doi-org.brum.beds.ac.uk/10.3390/pathogens11040442 - 06 Apr 2022
Cited by 6 | Viewed by 1789
Abstract
Bovine respiratory disease (BRD) is the leading cause of morbidity in feedlot cattle. The ability to accurately identify the expected BRD risk of cattle would allow managers to detect high-risk animals more frequently. Five classification models were built and evaluated towards predicting the [...] Read more.
Bovine respiratory disease (BRD) is the leading cause of morbidity in feedlot cattle. The ability to accurately identify the expected BRD risk of cattle would allow managers to detect high-risk animals more frequently. Five classification models were built and evaluated towards predicting the expected BRD risk (high/low) of feedlot cattle within the first 45 days on feed (DOF) and incorporate an economic analysis to determine the potential health cost advantage when using a predictive model compared with standard methods. Retrospective data from 10 U.S. feedlots containing 1733 cohorts representing 188,188 cattle with known health outcomes were classified into high- (≥15% BRD morbidity) or low- (<15%) BRD risk in the first 45 DOF. Area under the curve was calculated from the test dataset for each model and ranged from 0.682 to 0.789. The economic performance for each model was dependent on the true proportion of high-risk cohorts in the population. The decision tree model displayed a greater potential economic advantage compared with standard procedures when the proportion of high-risk cohorts was ≤45%. Results illustrate that predictive models may be useful at delineating cattle as high or low risk for disease and may provide economic value relative to standard methods. Full article
(This article belongs to the Special Issue Epidemiology of Cattle Diseases: Data Collection and Analytical Methods for Disease Control and Sustainable Production)
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