Modeling U.S. cattle movements until the cows come home: Who ships to whom and how many?

Sellman, Stefan; Beck‐Johnson, Lindsay M.; Hallman, Clayton; Miller, Ryan S.; Bonner, Katharine A. Owers; Portacci, Katie; Webb, Colleen T.; Lindström, Tom

doi:10.1016/j.compag.2022.107483

Cited by 4 publications

(3 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 82 ]; Sellman et al . [ 83 ]; Tsao et al . [ 84 ]) 2013 statistical statistical other SA: disease control, limited data, SA: surveillance yes cattle USA non-specific, foot and mouth disease, bovine tuberculosis geo-locations livestock movement static movement permits, census 33 (Sellman et al .…”

Section: Resultsmentioning

confidence: 99%

Simulating contact networks for livestock disease epidemiology: a systematic review

Leung

Rudge

Fournié

2023

J. R. Soc. Interface.

View full text Add to dashboard Cite

Contact structure among livestock populations influences the transmission of infectious agents among them. Models simulating realistic contact networks therefore have important applications for generating insights relevant to livestock diseases. This systematic review identifies and compares such models, their applications, data sources and how their validity was assessed. From 52 publications, 37 models were identified comprising seven model frameworks. These included mathematical models ( n = 8; including generalized random graphs, scale-free, Watts–Strogatz and spatial models), agent-based models ( n = 8), radiation models ( n = 1) (collectively, considered ‘mechanistic’), gravity models ( n = 4), exponential random graph models ( n = 9), other forms of statistical model ( n = 6) (statistical) and random forests ( n = 1) (machine learning). Overall, nearly half of the models were used as inputs for network-based epidemiological models. In all models, edges represented livestock movements, sometimes alongside other forms of contact. Statistical models were often applied to infer factors associated with network formation ( n = 12). Mechanistic models were commonly applied to assess the interaction between network structure and disease dissemination ( n = 6). Mechanistic, statistical and machine learning models were all applied to generate networks given limited data ( n = 13). There was considerable variation in the approaches used for model validation. Finally, we discuss the relative strengths and weaknesses of model frameworks in different use cases.

show abstract

Section: Resultsmentioning

confidence: 99%

Simulating contact networks for livestock disease epidemiology: a systematic review

Leung

Rudge

Fournié

2023

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…When firm-level output data are not available but the distribution of firm-level output is available, the distribution information can be used along with already available aggregate data [40]. We assume the distribution of packers' outputs follows the gamma distribution with y i ∼ Γ(α 1 , 1/α 2 ), following Schons, et al [51] and Sellman, et al [52]. Then, Equation (3) can be restated as:…”

Section: Combining Information Of Micro Data Distribution With Aggreg...mentioning

confidence: 99%

Estimating Market Power Exertion in the U.S. Beef Packing Industry: An Illustration of Data Aggregation Bias Using Simulated Data

Lee,

Chung

2024

Sustainability

View full text Add to dashboard Cite

This study investigates data aggregation bias in estimating market power in the U.S. beef packing industry using New Empirical Industrial Organization (NEIO) models and shows empirical procedures that can alleviate the bias. Unlike many earlier studies in estimating market power exertion, our study examines the data aggregation bias when market-level data are used in place of firm-level data and show how the bias could be reduced. We first derive data aggregation bias analytically, then empirically investigate the aggregation bias by estimating both firm and aggregate industry models. Because the firm-level data are not available, we use simulated data generated from the Monte Carlo simulation method. Hybrid models, combining limited firm-level data with aggregate data, are also estimated to illustrate how the aggregation bias could be reduced. Our results show that aggregate models with industry-level data tend to underestimate market power exertion in the U.S. beef packing industry, and the aggregation bias is statistically significant at the 1% level. Comparing results from hybrid models with firm-level estimates, we find that hybrid models reduce the bias but do not remove the aggregation bias significantly. The sensitivity analysis shows that market power estimate and aggregation bias are sensitive to functional forms.

show abstract

“…In the United Kingdom, for example, outdoor and indoor pig production systems established close cooperation links and shared innovation from the outset [ 56 ], and in recent years, it is common for outdoor-reared animals to be finished indoors (the finished pigs are often sourced from outdoor farms which have a comparative advantage in parts of the United Kingdom where grain production is less profitable) [ 57 ]. In the United States, intra- and inter-state cattle movements are associated with maximizing production in the beef and dairy sectors [ 58 ]. Some of this movement is related to climate- and drought-related drivers, moving cattle to available pasture and water.…”

Section: Political Economymentioning

confidence: 99%

Understanding the roles of economy and society in the relative risks of zoonosis emergence from livestock

Hinchliffe,

Blanchette,

Chan

et al. 2024

R. Soc. Open Sci.

View full text Add to dashboard Cite

The emergence of zoonotic infections that can develop into pathogens of pandemic potential is a major concern for public health. The risks of emergence and transmission relate to multiple factors that range from land use to human–non-human animal contacts. Livestock agriculture plays a potentially significant role in those risks, shaping landscapes and providing hosts that can act as the source or amplifiers of emergent pathogens. The relative risks will be contingent upon the nature of those systems, with comparisons often made between intensive, indoor, biosecure systems and more extensive, outdoor, insecure systems. Microbiological, ecological and veterinary sciences provide useful entry points in specifying and modelling some of the relative risks. Yet, they often do so with little regard for social science inputs and by making assumptions about social and economic conditions. In this article, we respond to recent analyses of relative risks by raising the importance of social and economic drivers of risk. We chart social science insights and research that materially alter the zoonotic risks associated with livestock production. Our purpose is to emphasize the requirement for full appreciation of the social, economic and political components of zoonotic and pandemic risk.

show abstract

Modeling U.S. cattle movements until the cows come home: Who ships to whom and how many?

Cited by 4 publications

References 61 publications

Simulating contact networks for livestock disease epidemiology: a systematic review

Simulating contact networks for livestock disease epidemiology: a systematic review

Estimating Market Power Exertion in the U.S. Beef Packing Industry: An Illustration of Data Aggregation Bias Using Simulated Data

Understanding the roles of economy and society in the relative risks of zoonosis emergence from livestock

Contact Info

Product

Resources

About