1
|
Smith MR, Sanderson MW. Modeled impacts of rapid and accurate cattle tracing in a Foot-and-Mouth Disease outbreak in the US. Prev Vet Med 2023; 215:105911. [PMID: 37084632 DOI: 10.1016/j.prevetmed.2023.105911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/03/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023]
Abstract
The purpose of this study was to estimate the impacts of rapid and accurate tracing of cattle movements during a Foot-and-Mouth Disease (FMD) outbreak in the United States (US). To simulate introduction and spread of FMD we utilized InterSpread Plus, a spatially explicit disease transmission model, and a national livestock population file. The simulations began in one of four regions of the US via beef or dairy cattle as the index infected premises (IP). The first IP was detected 8, 14, or 21 days after introduction. The tracing levels were defined by the probability of a successful trace and the time to trace completion. We evaluated three tracing performance levels, a baseline that represents a mix of paper and electronic interstate shipment records, an estimated partial implementation of electronic identification (EID) tracing, and an estimated full implementation of EID tracing. To evaluate the potential to decrease the size of control areas and surveillance zones with full EID use, we compared the standard size for each to a reduced geographical area for each. The total number of IPs in an outbreak varied with the location of the index farms. Within index farm locations and across tracing performance levels, early detection (day 8) resulted in fewer IPs and a shorter duration of the outbreak. The impact of improving tracing was most evident within introduction region when detection was delayed (day 14 or 21). Full EID use decreased the 95th percentile but had a smaller impact on the median number of IPs. Improved tracing also decreased the number of farms impacted by control efforts in control areas (0-10 km) and surveillance zones (10-20 km) by decreasing outbreak size (total IPs). Decreasing the control area (0-7 km) and surveillance zone (7-14 km) sizes while using full EID tracing further decreased the number of farms under surveillance but increased the number of IPs slightly. Consistent with previous results, this supports the potential value of early detection and improved traceability to control FMD outbreaks. Further development of the EID system in the US is necessary to achieve the modeled results. Further research into the economic impacts of enhanced tracing and decreased zone sizes are needed to determine the full impact of these results.
Collapse
|
2
|
Modeling nation-wide U.S. swine movement networks at the resolution of the individual premises. Epidemics 2022; 41:100636. [PMID: 36274568 DOI: 10.1016/j.epidem.2022.100636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 09/14/2022] [Accepted: 09/20/2022] [Indexed: 12/29/2022] Open
Abstract
The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between-premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within- and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.
Collapse
|
3
|
Brommesson P, Sellman S, Beck-Johnson L, Hallman C, Murrieta D, Webb CT, Miller RS, Portacci K, Lindström T. Assessing intrastate shipments from interstate data and expert opinion. ROYAL SOCIETY OPEN SCIENCE 2021; 8:192042. [PMID: 33959304 PMCID: PMC8074939 DOI: 10.1098/rsos.192042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 02/10/2021] [Indexed: 06/12/2023]
Abstract
Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate.
Collapse
Affiliation(s)
- Peter Brommesson
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 58183 Linköping, Sweden
| | - Stefan Sellman
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 58183 Linköping, Sweden
| | | | - Clayton Hallman
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Deedra Murrieta
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Colleen T. Webb
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Ryan S. Miller
- Center for Epidemiology and Animal Health, United States Department of Agriculture-Veterinary Services, Fort Collins, CO 80526, USA
| | - Katie Portacci
- Center for Epidemiology and Animal Health, United States Department of Agriculture-Veterinary Services, Fort Collins, CO 80526, USA
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 58183 Linköping, Sweden
| |
Collapse
|
4
|
Miller RS, Pepin KM. BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models. J Anim Sci 2019; 97:2291-2307. [PMID: 30976799 PMCID: PMC6541823 DOI: 10.1093/jas/skz125] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 04/10/2019] [Indexed: 12/27/2022] Open
Abstract
Management and policy decisions are continually made to mitigate disease introductions in animal populations despite often limited surveillance data or knowledge of disease transmission processes. Science-based management is broadly recognized as leading to more effective decisions yet application of models to actively guide disease surveillance and mitigate risks remains limited. Disease-dynamic models are an efficient method of providing information for management decisions because of their ability to integrate and evaluate multiple, complex processes simultaneously while accounting for uncertainty common in animal diseases. Here we review disease introduction pathways and transmission processes crucial for informing disease management and models at the interface of domestic animals and wildlife. We describe how disease transmission models can improve disease management and present a conceptual framework for integrating disease models into the decision process using adaptive management principles. We apply our framework to a case study of African swine fever virus in wild and domestic swine to demonstrate how disease-dynamic models can improve mitigation of introduction risk. We also identify opportunities to improve the application of disease models to support decision-making to manage disease at the interface of domestic and wild animals. First, scientists must focus on objective-driven models providing practical predictions that are useful to those managing disease. In order for practical model predictions to be incorporated into disease management a recognition that modeling is a means to improve management and outcomes is important. This will be most successful when done in a cross-disciplinary environment that includes scientists and decision-makers representing wildlife and domestic animal health. Lastly, including economic principles of value-of-information and cost-benefit analysis in disease-dynamic models can facilitate more efficient management decisions and improve communication of model forecasts. Integration of disease-dynamic models into management and decision-making processes is expected to improve surveillance systems, risk mitigations, outbreak preparedness, and outbreak response activities.
Collapse
Affiliation(s)
- Ryan S Miller
- Center for Epidemiology and Animal Health, United States Department of Agriculture-Veterinary Services, Fort Collins, CO
| | - Kim M Pepin
- National Wildlife Research Center, United States Department of Agriculture-Wildlife Services, Fort Collins, CO
| |
Collapse
|
5
|
Gorsich EE, Miller RS, Mask HM, Hallman C, Portacci K, Webb CT. Spatio-temporal patterns and characteristics of swine shipments in the U.S. based on Interstate Certificates of Veterinary Inspection. Sci Rep 2019; 9:3915. [PMID: 30850719 PMCID: PMC6408505 DOI: 10.1038/s41598-019-40556-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 01/24/2019] [Indexed: 11/10/2022] Open
Abstract
Domestic swine production in the United States is a critical economic and food security industry, yet there is currently no large-scale quantitative assessment of swine shipments available to support risk assessments. In this study, we provide a national-level characterization of the swine industry by quantifying the demographic (i.e. age, sex) patterns, spatio-temporal patterns, and the production diversity within swine shipments. We characterize annual networks of swine shipments using a 30% stratified sample of Interstate Certificates of Veterinary Inspection (ICVI), which are required for the interstate movement of agricultural animals. We used ICVIs in 2010 and 2011 from eight states that represent 36% of swine operations and 63% of the U.S. swine industry. Our analyses reflect an integrated and spatially structured industry with high levels of spatial heterogeneity. Most shipments carried young swine for feeding or breeding purposes and carried a median of 330 head (range: 1–6,500). Geographically, most shipments went to and were shipped from Iowa, Minnesota, and Nebraska. This work, therefore, suggests that although the swine industry is variable in terms of its size and type of swine, counties in states historically known for breeding and feeding operations are consistently more central to the shipment network.
Collapse
Affiliation(s)
- Erin E Gorsich
- Department of Biology, Colorado State University, Fort Collins, CO, USA. .,Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA. .,The Zeeman Institute: Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UK. .,School of Life Sciences, University of Warwick, Coventry, UK.
| | - Ryan S Miller
- Department of Biology, Colorado State University, Fort Collins, CO, USA.,USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Holly M Mask
- Department of Biology, Colorado State University, Fort Collins, CO, USA
| | - Clayton Hallman
- Department of Biology, Colorado State University, Fort Collins, CO, USA.,USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Katie Portacci
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO, USA
| | - Colleen T Webb
- Department of Biology, Colorado State University, Fort Collins, CO, USA.,Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
| |
Collapse
|
6
|
Estimating and exploring the proportions of inter- and intrastate cattle shipments in the United States. Prev Vet Med 2019; 162:56-66. [DOI: 10.1016/j.prevetmed.2018.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 11/02/2018] [Accepted: 11/04/2018] [Indexed: 11/17/2022]
|