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Cattle Shipments and Disease Spread Modeling
Linköping University, Department of Physics, Chemistry and Biology, Ecological and Environmental Modeling. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4941-1313
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Spread of transboundary animal diseases can have large impact on animal welfare, public health and economy. The effects of this include economic losses in terms of lower milk production, lower weight gain and culling due to welfare concerns. Disease preparedness is therefore important to be prepared for a possible outbreak, and policies need to be in place in order to take appropriate actions in case of an outbreak. It is also important to be able to take preventive actions to lessen the risk and size of an outbreak. For this, mathematical models are useful to describe the effects of an outbreak and to facilitate informed policy decisions.

Mathematical models of spread of animal diseases, implicitly or explicitly, model the route of infection. One route of particular concern is the shipment of livestock animals since animal shipments have the possibility to move infected animals over long distances and introduce disease in previously unaffected areas. It is therefore important to have underlying data to use as input to models in order to consider possible future scenarios. Such data may however be sparse and not readily available. Based on observed (and sometimes incomplete) data, the underlying process that determines the probabilities of livestock shipments’ origins and destinations can be modeled. By using Bayesian statistics and Markov Chain Monte Carlo methods, it is possible to obtain distributions of the underlying parameters in the model, which in turn allow posterior predictive sets of shipments to be generated. These can further be used in a disease simulation to analyze the course of a potential outbreak. Given a large number of scenarios of interest and substantial stochastic effects, implementation of such models requires fast algorithms to facilitate execution of a sufficient number of replicated simulations, which may be infeasible under naive methods. The topics of this thesis are models of live cattle shipment, the problems of lack of shipment data and the computational challenges of modeling and simulating spread of infectious animal diseases.

In Paper I, the spatio-temporal variations in distance dependence of cattle shipments in Sweden were studied by using real shipment data, Bayesian statistics and Markov Chain Monte Carlo methods. The main results were that the spatial as well as the temporal aspect are important when modeling networks of cattle shipments in Sweden. The spatial variations distance dependence were analyzed at county, land (Norrland, Svealand and Götaland) and national level (i.e. no spatial variation). Similarly, the temporal aspect were investigated at three levels of granularity, using monthly-, quarterly- and annual variations (i.e no temporal variation). The level of granularity at which the spatio-temporal variations in distance dependence was captured better, in terms of Deviance Information Criterion, was identified at the county and quarter level. This results shows that such variations should be acknowledged when modeling networks of cattle shipments in Sweden.

Paper II considered cattle shipments in the U.S. It addressed the problem of intrastate shipments being absent in available data and included responses from a survey taken by experts to estimate the proportion of shipments moving intrastate. The results showed that data from experts had minor effects on the estimations of proportion of intrastate shipments, mainly because of disparate estimates provided by the experts. This paper also investigated three types of functional forms of the distance dependence, and it was shown that the type used in Paper I, was the least preferred of the three. The preferred functional form had a plateau-shape at short distances as well as a fat tail, describing high probability of long-distance shipments.

Paper III addressed the computational challenges of simulating spread of livestock diseases. In Paper III, infections were modeled to spread locally from farm to farm without modeling§ each pathway individually (this may include pathways such as airborne spread, wildlife etc.). To avoid evaluating infection probability of all pairs of infected and susceptible premises, spread of disease was simulated by partitioning the landscape into grids and thereby letting farms belong to a specific cell in this grid. An algorithm was introduced that make use of overestimations of the probability of infection to discard entire cells from further consideration as they are considered as uninfected in the current time frame. Despite introducing estimations of probabilities, the algorithm does not introduce estimations to the spread of disease, and does not compromise the integrity of the simulation. This algorithm was compared to the naive algorithm of evaluating the farms pairwise as well as to two other published algorithms developed for increased computational efficiency. It was shown that the algorithm presented in Paper III was as fast as or faster than other considered methods.

Paper IV expanded the methods of Paper II and used the methodology from Paper III to simulate spread of disease via cattle shipments and via local spread across the U.S. In Paper IV, additional data at state- and county level were included that aimed at capturing shipment patterns related to the infrastructure of the production system not captured by the distance dependence. The model also considered three types of premises: farm, feedlot and market. This approach allows for different parameters across premises types, acknowledging their different roles in the production system. The result showed that these types of data were important to include when modeling the system and increased model performance in terms of WAIC, suggesting that industry structure should be accounted for when modeling cattle shipments. The spread of disease simulation included control scenarios such as culling of specific premises and also included a SEIR-model to model the infection status of each premises, referred to as partial transition. The results showed that while the inclusion of partial transition slowed the outbreak, the spatial pattern of the outbreak did not change.

This thesis provides insights to what factors are important when predicting animal shipments networks for usage in spread of disease simulations and how these factors can be modeled. It also stresses the importance of efficient algorithms when using simulations and presents an algorithm suited for simulating spread of disease between farms where pathways of the pathogen are not modeled explicitly. How to accurately estimate the spread of disease via shipments and how to simulate a large number of outbreak scenarios within reasonable time are two major challenges a modeler faces when trying to predict the impact of a potential outbreak.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. , p. 2246
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2246
National Category
Clinical Science Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-188732DOI: 10.3384/9789179294212ISBN: 9789179294205 (print)ISBN: 9789179294212 (electronic)OAI: oai:DiVA.org:liu-188732DiVA, id: diva2:1698325
Public defence
2022-09-28, Planck, F Building, Campus Valla, Linköping, 14:00 (English)
Opponent
Supervisors
Available from: 2022-09-23 Created: 2022-09-23 Last updated: 2023-03-15Bibliographically approved
List of papers
1. Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesnt Fit All
Open this publication in new window or tab >>Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesnt Fit All
2016 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 11, no 10, p. e0164008-Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE, 2016
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-132667 (URN)10.1371/journal.pone.0164008 (DOI)000386204000024 ()27760155 (PubMedID)
Note

Funding Agencies|Foreign Animal Disease Modeling Program, Science and Technology Directorate, U.S. Department of Homeland Security [HSHQDC-13-C-B0028]; European research area: animal health and welfare (ANIHWA) [ANR-13-ANWA-0007-03]

Available from: 2016-11-21 Created: 2016-11-18 Last updated: 2022-09-23
2. Assessing intrastate shipments from interstate data and expert opinion
Open this publication in new window or tab >>Assessing intrastate shipments from interstate data and expert opinion
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2021 (English)In: Royal Society Open Science, E-ISSN 2054-5703, Vol. 8, no 3, article id 192042Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Royal Society of Open Science, 2021
Keywords
livestock; spread of disease; cattle shipment; movement network; expert data
National Category
Evolutionary Biology
Identifiers
urn:nbn:se:liu:diva-174971 (URN)10.1098/rsos.192042 (DOI)000626174200001 ()
Note

Funding Agencies|US Department of Homeland Security Science and Technology DirectorateUnited States Department of Homeland Security (DHS) [HSHQDC-13-C-B0028]

Available from: 2021-04-14 Created: 2021-04-14 Last updated: 2022-09-23Bibliographically approved
3. Need for speed: An optimized gridding approach for spatially explicit disease simulations
Open this publication in new window or tab >>Need for speed: An optimized gridding approach for spatially explicit disease simulations
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2018 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 14, no 4, article id e1006086Article in journal (Refereed) Published
Abstract [en]

Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE, 2018
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-148397 (URN)10.1371/journal.pcbi.1006086 (DOI)000432169600026 ()29624574 (PubMedID)
Note

Funding Agencies|Foreign Animal Disease Modeling Program, Science and Technology Directorate, U.S. Department of Homeland Security [HSHQDC-13-C-B0028]; European research area: animal health and welfare (ANIHWA) [ANR-13-ANWA-0007-03]

Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2022-09-23
4. The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics
Open this publication in new window or tab >>The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics
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2022 (English)In: Life, E-ISSN 2075-1729, Vol. 12, no 10, article id 1604Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
foot and mouth disease; livestock demography; model assumptions; cattle shipment networks; outbreak simulation
National Category
Evolutionary Biology
Identifiers
urn:nbn:se:liu:diva-189791 (URN)10.3390/life12101604 (DOI)000874363900001 ()36295038 (PubMedID)
Note

Funding Agencies|U.S. Department of Homeland Security Science and Technology Directorate [HSHQDC-13-B0028, D15PC00278]; USDA National Institute of Food and Agriculture [2022-67015-36923]

Available from: 2022-11-08 Created: 2022-11-08 Last updated: 2023-03-15Bibliographically approved

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