| 1. |
- Brommesson, Peter, 1981-
(författare)
-
Cattle Shipments and Disease Spread Modeling
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
|
|
| 2. |
- Gilbertson, Kendra, et al.
(författare)
-
The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics
- 2022
-
Ingår i: Life. - : MDPI. - 2075-1729. ; 12:10
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 3. |
- Lindström, Tom, 1977-, et al.
(författare)
-
Bayesian analysis of animal movements related to factors at herdand between herd levels : Implications for disease spread modeling
- 2011
-
Ingår i: Preventive Veterinary Medicine. - : Elsevier. - 0167-5877 .- 1873-1716. ; 98:4, s. 230-242
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- A method to assess the influence of between herd distances, production types and herd sizes on patterns of between herd contacts is presented. It was applied on pig movement data from a central database of Swedish Board of Agriculture. To determine the influence of these factors on the contact between holdings we used a Bayesian model and Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters. The analysis showed that the contact pattern via animal movements is highly heterogeneous and influenced by all three factors, production type, herd size, and distance between farms. Most production types showed a positive relationship between maximum capacity and the probability of both incoming and outgoing movements. In agreement with previous studies, holdings also differed in both the number of contacts as well as with what holding types contact occurred with. Also, the scale and shape of distance dependence in contact probability was shown to differ depending on the production types of holdings. To demonstrate how the methodology may be used for risk assessment, disease transmissions via animal movements were simulated with the model used for analysis of contacts, and parameterized by the analyzed posterior distribution. A Generalized Linear Model showed that herds with production types Sow pool center, Multiplying herd and Nucleus herd have higher risk of generating a large number of new infections. Multiplying herds are also expected to generate many long distance transmissions, while transmissions generated by Sow pool centers are confined to more local areas. We argue that the methodology presented may be a useful tool for improvement of risk assessment based on data found in central databases.
|
|
| 4. |
- Lindström, Tom, 1977-
(författare)
-
Spatial Spread of Organisms : Modeling ecological and epidemiological processes
- 2010
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis focuses on the spread of organisms in both ecological and epidemiological contexts. In most of the studies presented, displacement is modeled with a spatial kernel function, which is characterized by scale and shape. These are measured by the net squared displacement (or kernel variance) and kurtosis, respectively. If organisms disperse by the assumptions of a random walk or correlated random walk, a Gaussian shaped kernel is expected. Empirical studies often report deviations from this, and commonly leptokurtic distributions are found, often as a result of heterogeneity in the dispersal process.In the studies presented in two of the included papers, the importance of the kernel shape is tested, by using a family of kernels where the shape and scale can be separated effectively. Both studies utilize spectral density approaches for modeling the spatial environment. It is concluded that the shape is not important when studying the population distribution in a habitat/matrix context. The shape is however important when looking at the invasion of organisms in a patchy environment, when the arrangement of patches deviates from randomly distributed. The introduced method for generating patch distribution is also compared to empirical distributions of patches (farms and old trees). Here it is concluded that the assumptions used for modeling of the spatial environment are consistent with the observed patterns. These assumptions include fractal properties such that the same aggregational patterns are found at different scales.In a series of papers, movements of animals are considered as vectors for between-herd disease spread. The studies are based on data found in databases held by the Swedish Board of Agricultural (SJV), consisting of reported movements, as well as farm location and characteristics. The first study focuses on the distance related probability of contacts between herds. In the following papers, the analysis is expanded to include production type and herd size. Movement data of pigs (and cattle in Paper I) are analyzed with Bayesian models, implemented with Markov Chain Monte Carlo (MCMC). This is a flexible approach that allows for parameter estimations of complex models, and at the same time includes parameter uncertainty.In Paper IV, the effects of the included factors are investigated. It is shown that all three factors (herd size, production type structure and distance related probability of contacts) are expected to influence disease spread dynamics, however the production type structure is found to be the most important factor. This emphasizes the value of keeping such information in central databases. The models presented can be used as support for risk analysis and disease tracing. However, data reliability is always a problem, and implementation may be improved with better quality data.The thesis also shows that utilizing spatial kernels for description of the spatial spread of organisms is an appropriate approach. However, these kernels must be flexible and flawed assumptions about the shape may lead to erroneous conclusions. Hence, the joint distribution of kernel shape and scale should be estimated. The flexibility of Bayesian analysis, implemented with MCMC techniques, is a good approach for this, and further allows for implementation of more complex models where other factors may be included.
|
|
| 5. |
- Lindström, Tom, 1977-, et al.
(författare)
-
Splitting the tail of the displacement kernel shows the unimportance of kurtosis
- 2008
-
Ingår i: Ecology. - : Wiley. - 0012-9658 .- 1939-9170. ; 89:7, s. 1784-1790
-
Tidskriftsartikel (refereegranskat)abstract
- Animals disperse in space through different movement behaviors, resulting in different displacement distances. This is often described with a displacement kernel where the long-distance dispersers are within the tail of the kernel. A displacement with a large proportion of long-distance dispersers may have impact on different aspects of spatial ecology such as invasion speed, population persistence, and distribution. It is, however, unclear whether the kurtosis of the kernel plays a major role since a fatter tail also influences the variance of the kernel. We modeled displacement in landscapes with different amounts and configurations of habitats and handled kurtosis and variance separately to study how these affected population distribution and transition time. We conclude that kurtosis is not important for any of these aspects of spatial ecology. The variance of the kernel, on the other hand, was of great importance to both population distribution and transition time. We argue that separating variance and kurtosis can cast new light on the way in which long-distance dispersers are important in ecological processes. Consequences for empirical studies are discussed.
|
|
| 6. |
- Tsao, K., et al.
(författare)
-
Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale
- 2020
-
Ingår i: Interface Focus. - : Royal Society Publishing. - 2042-8898 .- 2042-8901. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others. © 2019 The Author(s) Published by the Royal Society. All rights reserved.
|
|
| 7. |
- Westerberg, Lars, 1970-, et al.
(författare)
-
The effect on dispersal from complex correlations in small-scale movement
- 2008
-
Ingår i: Ecological Modelling. - : Elsevier BV. - 0304-3800 .- 1872-7026. ; 213:2, s. 263-272
-
Tidskriftsartikel (refereegranskat)abstract
- Calculations of large-scale displacement distances were made to evaluate the combined effect of small-scale movement pattern of a Collembola, Protaphorura armata. The effect of presence of food and conspecific density on turning angle, step length and activity/motility was investigated. Calculations of net square displacement were made both by assuming correlated random walk (CRW) and by resampling data to account for correlation structures in movement patterns that violate the assumptions of CRW. In presence of food, individuals spent less time moving (decreased activity), but when they moved they showed larger turning angles than individuals moving in areas without food. Increased conspecific density did not affect time spent moving by individuals, but when step length decreased and turning angle increased. P. armata showed negative density-dependent dispersal and exhibited area-restricted search as a response to both food and increased conspecific density. The CRW was relatively robust to some violations of its underlying assumptions. However, the expected displacement increased substantially, as much as 50%, when accounting for observed auto-correlation in step length and correlation between step length and turning angle. Hence, an explanation for increased displacement and dispersal of a species can also be the result of a more complex correlation of its behaviour rather than solely altering specific movement parameters, for example increasing step length or decreasing turning angle. The results emphasise the importance of careful analysis of small-scale movement before using them as predictors of population distribution and invasion speed in heterogeneous landscapes.
|
|
