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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.
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Gilbertson K, Brommesson P, Minter A, Hallman C, Miller RS, Portacci K, Sellman S, Tildesley MJ, Webb CT, Lindström T, Beck-Johnson LM. The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics. Life (Basel) 2022; 12:1604. [PMID: 36295038 PMCID: PMC9605081 DOI: 10.3390/life12101604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/25/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023] Open
Abstract
Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.
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Affiliation(s)
- Kendra Gilbertson
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
| | - Peter Brommesson
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 581 83 Linköping, Sweden
| | - Amanda Minter
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Clayton Hallman
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO 80526, USA
| | - Ryan S. Miller
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO 80526, USA
| | - Katie Portacci
- USDA APHIS Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, CO 80526, USA
| | - Stefan Sellman
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 581 83 Linköping, Sweden
| | - Michael J. Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Colleen T. Webb
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 581 83 Linköping, Sweden
| | - Lindsay M. Beck-Johnson
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
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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.
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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
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Hidano A, Gates MC. Why sold, not culled? Analysing farm and animal characteristics associated with livestock selling practices. Prev Vet Med 2019; 166:65-77. [PMID: 30935507 DOI: 10.1016/j.prevetmed.2019.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 01/27/2019] [Accepted: 03/08/2019] [Indexed: 11/18/2022]
Abstract
Livestock disease simulation models that incorporate animal movements often assume (1) that farmers' livestock trading practices remain consistent over time in future, (2) that animals sold to other farms are chosen randomly from a herd, and (3) that the animals' fate on the destination farm is not influenced by their past production and movement histories. The objective of this study was to assess the extent to which these assumptions are violated in the real world using records from a national database in New Zealand that captures both milk production and movement data for individual dairy cattle. All individual animal milk test records from 2006 through 2010 were extracted from the database and processed to generate different animal and herd level variables including cow demographics, previous movement history, milk volume, and milk composition (somatic cell counts (SCC), protein percentage, and fat percentage). Various statistical models were used to explore factors associated with farms' selling practice and characteristics of animals being sold. The results showed farms' livestock selling practices were highly influenced by both external factors such as market milk price and internal factors such as previous year's cow mortality and how long farms had been in business. Higher milk price increased both the number of cows being sold and the number of farms selling cows. Compared with cows that remained in the herd at the end of lactation, cows sold to other farms had lower fat and protein percentages, but similar milk volumes and SCCs. Cows that had been sold more often in the past were more likely to be sold after controlling for the effects of age. Overall, these findings highlight the potential need for disease simulation models to account for dynamics in selling practices and animal characteristics when determining which animals will be sold to other herds.
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Affiliation(s)
- Arata Hidano
- EpiCentre, School of Veterinary Science, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand.
| | - M Carolyn Gates
- EpiCentre, School of Veterinary Science, Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand
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Kao SYZ, VanderWaal K, Enns EA, Craft ME, Alvarez J, Picasso C, Wells SJ. Modeling cost-effectiveness of risk-based bovine tuberculosis surveillance in Minnesota. Prev Vet Med 2018; 159:1-11. [DOI: 10.1016/j.prevetmed.2018.08.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/24/2018] [Accepted: 08/25/2018] [Indexed: 10/28/2022]
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Disease transmission in animal transfer networks. Prev Vet Med 2016; 137:36-42. [PMID: 28107879 DOI: 10.1016/j.prevetmed.2016.12.017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 12/26/2016] [Accepted: 12/28/2016] [Indexed: 11/23/2022]
Abstract
Infectious diseases transmission is strongly determined by who contacts whom. Bovine tuberculosis (bTB) caused by Mycobacterium bovis is a worldwide burden for animal populations. One of the major transmission mechanism between herd is the transfer of infectious animal. In East Africa, pastoralists may receive or bestow livestock to create and strengthen social relationships. Here, we used a network approach to examine the relative importance of such cattle transfer in the transmission of bTB. First, a total of 2550 cattle from 102 herds were tested using the comparative intradermal tuberculin test to assess the presence of bTB infected cattle in the herd. A herd was considered bTB positive if it had at least one tuberculin reactor animal. Next, we calculated the centrality of each herd in the cattle transfer network using four established measures of social network centralization: degree, betweenness, closeness and fragmentation. The relationships between the network centrality measures and bTB infection were examined using generalized linear mixed models (GLMM). We found that a herd's in-degree in the social network was positively correlated with the risk of being infected with bTB (b=4.2, 95%CI=2.1-5.7; p<0.001). A herd that was close to many others (i.e., had a higher closeness index) had a larger chance of acquiring bTB infection (b=2.1, 95%CI=1.4-2.8; p<0.001). Betweenness centrality was also positively associated with the presence of bTB infection. There was a negative relationship between the fragmentation index and bTB infection (b=-2.7, 95%CI=-4.9-1.3; p<0.001). The study clearly demonstrated that the extent to which a herd is connected within a network has significant implications for its probability of being infected. Further, the results are in accordance with our expectation that connectivity and the probability that a herd will transmit the disease to other herds in the network are related.
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Brommesson P, Wennergren U, Lindström T. Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn't Fit All. PLoS One 2016; 11:e0164008. [PMID: 27760155 PMCID: PMC5070834 DOI: 10.1371/journal.pone.0164008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 09/19/2016] [Indexed: 11/18/2022] Open
Abstract
The structure of contacts that mediate transmission has a pronounced effect on the outbreak dynamics of infectious disease and simulation models are powerful tools to inform policy decisions. Most simulation models of livestock disease spread rely to some degree on predictions of animal movement between holdings. Typically, movements are more common between nearby farms than between those located far away from each other. Here, we assessed spatiotemporal variation in such distance dependence of animal movement contacts from an epidemiological perspective. We evaluated and compared nine statistical models, applied to Swedish movement data from 2008. The models differed in at what level (if at all), they accounted for regional and/or seasonal heterogeneities in the distance dependence of the contacts. Using a kernel approach to describe how probability of contacts between farms changes with distance, we developed a hierarchical Bayesian framework and estimated parameters by using Markov Chain Monte Carlo techniques. We evaluated models by three different approaches of model selection. First, we used Deviance Information Criterion to evaluate their performance relative to each other. Secondly, we estimated the log predictive posterior distribution, this was also used to evaluate their relative performance. Thirdly, we performed posterior predictive checks by simulating movements with each of the parameterized models and evaluated their ability to recapture relevant summary statistics. Independent of selection criteria, we found that accounting for regional heterogeneity improved model accuracy. We also found that accounting for seasonal heterogeneity was beneficial, in terms of model accuracy, according to two of three methods used for model selection. Our results have important implications for livestock disease spread models where movement is an important risk factor for between farm transmission. We argue that modelers should refrain from using methods to simulate animal movements that assume the same pattern across all regions and seasons without explicitly testing for spatiotemporal variation.
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Affiliation(s)
- Peter Brommesson
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Uno Wennergren
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- * E-mail:
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Controlling infectious disease through the targeted manipulation of contact network structure. Epidemics 2015; 12:11-9. [PMID: 26342238 PMCID: PMC4728197 DOI: 10.1016/j.epidem.2015.02.008] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Revised: 02/24/2015] [Accepted: 02/25/2015] [Indexed: 11/21/2022] Open
Abstract
Individuals in human and animal populations are linked through dynamic contact networks with characteristic structural features that drive the epidemiology of directly transmissible infectious diseases. Using animal movement data from the British cattle industry as an example, this analysis explores whether disease dynamics can be altered by placing targeted restrictions on contact formation to reconfigure network topology. This was accomplished using a simple network generation algorithm that combined configuration wiring with stochastic block modelling techniques to preserve the weighted in- and out-degree of individual nodes (farms) as well as key demographic characteristics of the individual network connections (movement date, livestock market, and animal production type). We then tested a control strategy based on introducing additional constraints into the network generation algorithm to prevent farms with a high in-degree from selling cattle to farms with a high out-degree as these particular network connections are predicted to have a disproportionately strong role in spreading disease. Results from simple dynamic disease simulation models predicted significantly lower endemic disease prevalences on the trade restricted networks compared to the baseline generated networks. As expected, the relative magnitude of the predicted changes in endemic prevalence was greater for diseases with short infectious periods and low transmission probabilities. Overall, our study findings demonstrate that there is significant potential for controlling multiple infectious diseases simultaneously by manipulating networks to have more epidemiologically favourable topological configurations. Further research is needed to determine whether the economic and social benefits of controlling disease can justify the costs of restricting contact formation.
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Lindström T, Grear DA, Buhnerkempe M, Webb CT, Miller RS, Portacci K, Wennergren U. A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network. PLoS One 2013; 8:e53432. [PMID: 23308223 PMCID: PMC3537632 DOI: 10.1371/journal.pone.0053432] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Accepted: 11/28/2012] [Indexed: 11/26/2022] Open
Abstract
Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.
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Affiliation(s)
- Tom Lindström
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- School of Biological Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Daniel A. Grear
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Michael Buhnerkempe
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Colleen T. Webb
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Ryan S. Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
| | - Katie Portacci
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America
| | - Uno Wennergren
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- * E-mail:
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Lindström T, Lewerin SS, Wennergren U. Influence on disease spread dynamics of herd characteristics in a structured livestock industry. J R Soc Interface 2011; 9:1287-94. [PMID: 22112656 PMCID: PMC3350725 DOI: 10.1098/rsif.2011.0625] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Studies of between-herd contacts may provide important insight to disease transmission dynamics. By comparing the result from models with different levels of detail in the description of animal movement, we studied how factors influence the final epidemic size as well as the dynamic behaviour of an outbreak. We investigated the effect of contact heterogeneity of pig herds in Sweden due to herd size, between-herd distance and production type. Our comparative study suggests that the production-type structure is the most influential factor. Hence, our results imply that production type is the most important factor to obtain valid data for and include when modelling and analysing this system. The study also revealed that all included factors reduce the final epidemic size and also have yet more diverse effects on initial rate of disease spread. This implies that a large set of factors ought to be included to assess relevant predictions when modelling disease spread between herds. Furthermore, our results show that a more detailed model changes predictions regarding the variability in the outbreak dynamics and conclude that this is an important factor to consider in risk assessment.
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Affiliation(s)
- Tom Lindström
- IFM Theory and Modelling, Linköping University, Linköping 581 83 Sweden
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