Our overall goal is to provide strong, data-driven predictions of U.S. livestock shipments and simulations of livestock disease and to characterize uncertainty in these predictions in order to support decision-making in both response and planning contexts.

Objectives

The U.S. Animal Movement Model (USAMM) and Disease Outbreak Simulation (USDOS) were developed through an international collaboration with funding from the United States Department of Agriculture and the Department of Homeland Security.

The United States does not track individual livestock movements, therefore there is a need to characterize livestock shipments at the national scale in the U.S. USAMM provides estimates of the number of cattle or swine shipments between any two counties in the contiguous United States and includes estimates of uncertainty in the predictions. USDOS is a national scale, premises-level simulation model that uses original algorithms to produce outputs very quickly even at large spatial scales. USDOS incorporates both local and long-range transmission together with multiple response options. USDOS has relatively few parameters so that sensitivity of model outputs to changes in parameters can be fully investigated. The model can run using demographic information from any country together with appropriate shipping data and/or local spread transmission. Inquiries about current work on this project may be directed to Lindsay Beck-Johnson at L.Beck-Johnson@colostate.edu

This web site is planned for use by both management and field personnel and disease modelers. This web site contains high-level descriptions of project deliverables, visualization tools, downloadable model predictions that can be ported into users own tools/systems, supporting publications with technical details, freely available downloadable code and user-guides.

Philosophy

Our overall modeling philosophy has three main components:

  1. Strongly data-based modeling: Increases the relevance of the models to applied questions
  2. Close working relationship with state and government agencies: Informs model structures and capabilities to handle realistic scenarios
  3. Parsimonious modeling choices whenever possible: Enhances validation and sensitivity analysis by using fewer, better-informed parameters, optimizes tradeoffs among applied needs, available data and modeling techniques.

People

group picture

Colleen Webb -
Title: Vice Provost for Graduate Affairs and Dean of the Graduate School; Professor
Affiliations: Colorado State University, Graduate School; Department of Biology
Role: Principal Investigator
Contact info: colleen.webb@colostate.edu
Uno Wennergren -
Title: Professor
Affiliations: Linköping University, Department of Physics, Chemistry, and Biology
Role: Principal Investigator
Contact info: uno.wennergren@liu.se
Michael J. Tildesley -
Title: Professor
Affiliations: University of Warwick
Role: Principal Investigator
Contact info: m.j.tildesley@warwick.ac.uk
Tom Lindström -
Title: Associate Professor
Affiliations: Linköping University, Department of Physics, Chemistry, and Biology
Role: Principal Investigator
Contact info: tom.lindstrom@liu.se
Lindsay Beck-Johnson -
Title: Research Scientist III
Affiliations: Colorado State University, Department of Biology
Role: Principal Investigator
Contact info: l.beck-johnson@colostate.edu
Stefan Sellman -
Title: Principal Research Engineer
Affiliations: Linköping University, Department of Physics, Chemistry, and Biology
Role: Principal Investigator
Contact info: stefan.sellman@liu.se
Peter Brommesson -
Title: PhD
Affiliations: Linköping University, Department of Physics, Chemistry, and Biology
Role: Senior Personnel
Contact info: peter.brommesson@liu.se
Jonathan Bertram -
Title: Research Associate
Affiliations: Colorado State University, Department of Biology
Role: Personnel
Contact info: jh.bertram@colostate.edu
Michael Buhnerkempe -
Title: Research Assistant Professor
Affiliations: Southern Illinois University School of Medicine
Role: Senior Personnel
Contact info: mbuhnerkempe66@siumed.edu
Dominika Dec Peevey -
Title:
Affiliations: Colorado State University, Department of Biology
Role: Personnel
Contact info: Dominika.Peevey@colostate.edu
Kendra Gilbertson -
Title: PhD Candidate
Affiliations: Colorado State University, Department of Biology
Role: Personnel
Contact info: Kendra.Gilbertson@colostate.edu
Erin Gorsich -
Title: Associate Professor
Affiliations: University of Warwick, School of Life Sciences
Role: Senior Personnel
Contact info: https://warwick.ac.uk/fac/sci/lifesci/people/egorsich/
Daniel Grear -
Title: Wildlife Disease Ecologist
Affiliations: USGS National Wildlife Health Center
Role: Senior Personnel
Contact info: https://www.usgs.gov/staff-profiles/daniel-a-grear
Clayton Hallman -
Title: Mathematical Statistician
Affiliations: USDA Animal and Plant Health Inspection Service
Role: Agency Technical Expert
Contact info: clayton.n.hallman@aphis.usda.gov
Catherine M. Herzog -
Title: Data Scientist
Affiliations: CDC Center for Forecasting and Outbreak Analytics, Colorado State University, Department of Biology
Role: Personnel
Contact info: catherine.herzog@colostate.edu
Ryan Miller -
Title: Senior Ecologist
Affiliations: USDA Animal and Plant Health Inspection Service
Role: Agency Lead
Contact info: ryan.s.miller@aphis.usda.gov
Amanda Minter -
Title: Infectious Disease Modeling Consultant
Affiliations: Equations of Disease C.I.C.
Role: Senior Personnel
Contact info: amanda@equationsofdisease.com www.equationsofdisease.com
Deedra Murrieta -
Title: Biological Science Information Specialist
Affiliations: USDA Animal and Plant Health Inspection Service
Role: Senior Personnel
Contact info: deedra.murrieta@gmail.com
Katie Owers Bonner -
Title: Public Health Emergency Preparedness Surveillance Program Manager
Affiliations: New Hampshire Department of Health and Human Services
Role: Senior Personnel
Contact info: owersk@gmail.com
Katie Portacci -
Title: Veterinary Epidemiologist
Affiliations: USDA Animal and Plant Health Inspection Service
Role: Agency Lead
Contact info: Katie.Portacci@aphis.usda.gov
Brandon Simony -
Title: PhD Candidate
Affiliations: Pennsylvania State University, Department of Biology
Role: Personnel
Contact info:
Lauren Smith -
Title:
Affiliations: USDA
Role: Personnel
Contact info: Lauren.E.Smith@colostate.edu
Sam Smith -
Title: Research Associate II
Affiliations: Colorado State University, Department of Biology
Role: Personnel
Contact info: sm.smith@colostate.edu
Kim Tsao -
Title: Lead Data Scientist for the Chief Data Officer
Affiliations: USDA Office of the Chief Information Officer
Role: Senior Personnel
Contact info: http://www.kimtsao.com
Marleen Werkman -
Title: Research Associate
Affiliations: Imperial College London
Role: Senior Personnel
Contact info: Personal Website

FAQ

Do I need to reference USAMM/USDOS? How do I do so?

Yes! USAMM and USDOS code were created by our group and should be referenced appropriately whether you use them as is or as base code for your own modifications. Suggested reference formats are:

To cite this page: Webb, C.T., Wennergren, U., Lindström, T., Tildesley, M.J., Dewey, T., Beck-Johnson, L.M., Sellman, S., and Leach, C. 2024. U.S. Animal Movement Model and Disease Outbreak Simulation (On-line). Accessed (date) at https://webblabb.github.io/usammusdos

To cite USDOS: Gilbertson, K., Brommesson, P., Minter, A., Hallman, C., Miller, R.S., Portacci, K., Sellman, S., Tildesley, M.J., Webb, C.T., Lindström, T., Beck-Johnson, L.M. 2022. The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics. Life 12 (10)

To cite USAMM-Swine: Sellman, S., Beck-Johnson, L.M., Hallman, C., Miller, R.S., Owers Bonner, K.A., Portacci, P., Webb, C.T., Lindström, T. 2022. Modeling Nation-Wide U.S. Swine Movement Networks at the Resolution of the Individual Premises. Epidemics 41 (December)

To cite USAMM-Cattle: Sellman, S., Beck-Johnson, L.M., Hallman, C., Miller, R.S., Owers Bonner, K.A., Portacci, P., Webb, C.T., Lindström, T. 2022. Modeling U.S. Cattle Movements until the Cows Come Home: Who Ships to Whom and How Many? Computers and Electronics in Agriculture 203 (December)

What is the license information for USAMM/USDOS?

License information for USAMM is available here .

License information for USDOS is available here .

Should I use shipment data or USAMM predictions?

We recommend using USAMM predictions. The underlying shipment data are a sample that needs to be scaled-up and do not capture within state movements. USAMM predictions solve both problems for you. The underlying shipment data are also not freely available (see FAQ on requesting underlying data).

How do I request underlying data?

Academic partners do not have the authority to share underlying data due to confidentiality agreements. Please contact the appropriate state or government agency with which the data originated. The best way to determine the appropriate agency is in the acknowledgments section of our papers and to contact agency co-authors on the paper describing the data set of interest.

What data do you use for U.S. beef/dairy/swine premises, feedlots, and markets?

Beef, dairy, and swine premises and feedlots locations and sizes are predictions of the Farm Location and Animal Population Simulator based on National Agricultural Statistics Survey data (Burdett et al. 2015). Market locations and sizes are based on aggregated market lists and were validated by us as described in (Smith et al. Submitted).

How do you maintain data confidentiality?

Academic partners have signed confidentiality agreements in place with the agency with whom the data originate and do not share the data. All published information is aggregated so that personally identifiable information cannot be determined. All presentations, publications, etc. are pre-reviewed before submission and approved by the agency with whom the data originate.

How do I run the models?

USAMM is not available for download, but network realizations can be accessed from the USAMM page on this website. USDOS is available as C++ code and can be accessed from the USDOS page on this website. Preprocessing and postprocessing pipeline code for running many USDOS realizations in batch mode is also available as R code.

What are the run times for USAMM/USDOS?

The parameter fitting process of USAMM is computationally intensive and requires up to weeks of run time on modern 32-core preocessors. However, network realizations from the fitted USAMM model can be generated within seconds on a laptop. The run times for USDOS vary depending on the size of the outbreak, but most simulations at the national scale finish in seconds even on a laptop. Longer USDOS simulations are on the order of minutes at the national scale.

What hardware do I need to run USDOS?

USDOS runs on a laptop for single or a few realizations or on a high-performance computing system for many realizations.

Why do I not want to run USAMM myself?

There are multiple reasons. For one, the underlying shipment data that we used to estimate USAMM parameters are confidential to protect personally identifiable information and are not freely available. Aside from this, additional shipment data, apart from what the model has already been fitted to, are not currently available, and without additional data, the estimated parameters will not change. Hence, there really is no reason to re-estimate USAMM parameters. Lastly, the parameter fitting process of USAMM is very computationally intensive and requires access to a modern high-performance computer cluster to give meaningful results.

How do I report bugs in the code?

Bugs may be reported to Lindsay Beck-Johnson at L.Beck-Johnson@colostate.edu. We will maintain a list of known bugs. We have tested the code extensively, but bugs can still appear. However, bugs that are discovered after the end of funding for the project are unlikely to be addressed.