The importance of livestock demography and infrastructure in driving Foot and Mouth disease dynamics

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

Solving the Generalized Assignment Problem by column enumeration based on Lagrangian reduced costs

In this thesis a method for solving the Generalized Assignment Problem (GAP) is described. It is based on a reformulation of the original problem into a Set Partitioning Problem (SPP), in which the columns represent partial solutions to the original problem. For solving this problem, column generation, with systematic overgeneration of columns, is used. Conditions that guarantee that an optimal solution to a restricted SPP is optimal also in the original problem are given. In order to satisfy these conditions, not only columns with the most negative Lagrangian reduced costs need to be generated, but also others; this observation leads to the use of overgeneration of columns. The Generalized Assignment Problem has shown to be NP-hard and therefore efficient algorithms are needed, especially for large problems. The application of the proposed method decomposes GAP into several knapsack problems via Lagrangian relaxation, and enumerates solutions to each of these problems. The solutions obtained from the knapsack problems form a Set Partitioning Problem, which consists of combining one solution from each knapsack problem to obtain a solution to the original problem. The algorithm has been tested on problems with 10 agents and 60 jobs. This leads to 10 knapsack problems, each with 60 variables

Cattle Shipments and Disease Spread Modeling

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

Median shape and scale estimates for counties in Sweden in quarter Q1 to Q4, presented on the log scale.

<p>Median shape and scale estimates for counties in Sweden in quarter Q1 to Q4, presented on the log scale.</p

Table of DIC scores, lppd values and the proportion of 95<i>^th</i> percentile and 50<i>^th</i> percentile, encapsulated by the corresponding posterior predictive distributions 95% central credibility intervals.

<p>Table of DIC scores, lppd values and the proportion of 95<i>^th</i> percentile and 50<i>^th</i> percentile, encapsulated by the corresponding posterior predictive distributions 95% central credibility intervals.</p

Medians and corresponding 95% credibility intervals of the posterior predictive distribution of the 50th (dots) and the 95th (diamonds) percentiles of movement distances for selected months and counties, based on parameters from 2007.

<p>Dashed and dotted lines indicate the corresponding quantities in the observed data from 2008.</p

Kernel shape for three parameter sets, inset figure shows kernel values (on the log scale) for larger distances.

<p>Kernel shape for three parameter sets, inset figure shows kernel values (on the log scale) for larger distances.</p

Exploring the two-gene ratio in breast cancer – independent roles for HOXB13 and IL17BR in prediction of clinical outcome

Background: The two-gene expression ratio HOXB13:IL17BR has been proposed to predict the outcome of tamoxifen-treated breast cancer patients. We intended to examine whether this ratio can predict the benefit of 5 years vs. 2 years of tamoxifen treatment of postmenopausal patients. A further objective was to investigate any prognostic effects of the ratio in systemically untreated premenopausal patients. Based on the current knowledge of HOXB13 and IL17BR, we hypothesized that these genes may have individual prognostic or predictive power. Patients and methods: Expression of HOXB13 and IL17BR were quantified by real-time PCR in tumors from 264 randomized postmenopausal patients and 93 systemically untreated premenopausal patients. Results: A high HOXB13:IL17BR ratio was associated with aggressive tumor characteristics, as were low levels of IL17BR alone. The ratio and HOXB13 alone predicted recurrence-free survival after endocrine treatment, with a benefit of prolonged treatment in estrogen receptor-positive patients correlated to a low ratio (recurrence rate ratio: RR=0.39; p=0.030), or low expression of HOXB13 (RR=0.37; p=0.015). No difference in recurrence-free survival was seen for the high ratio or high HOXB13 subgroups. The predictive value of HOXB13 and HOXB13:IL17BR was significant in multivariate analysis. In the systemically untreated cohort, only IL17BR showed independent prognostic significance. Conclusion: We conclude that the ratio or HOXB13 alone can predict the benefit of endocrine therapy, with a high ratio or a high expression rendering patients less likely to respond. We have also shown that IL17BR might be an independent prognostic factor in breast cancer.The original publication is available at www.springerlink.com: Piiha-Lotta Jerevall, Sara Brommesson, Carina Strand, Sofia Gruvberger-Saal, Per Malmström, Bo Nordenskjöld, Sten Wingren, Peter Söderkvist, Mårten Fernö and Olle Stål, Exploring the two-gene ratio in breast cancer – independent roles for HOXB13 and IL17BR in prediction of clinical outcome, 2008, Breast Cancer Research and Treatment, (107), 2, 225-234. http://dx.doi.org/10.1007/s10549-007-9541-8. Copyright: Springer, www.springerlink.co

Assessing intrastate shipments from interstate data and expert opinion

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

Exploring the two-gene ratio in breast cancer – independent roles for HOXB13 and IL17BR in prediction of clinical outcome

Background: The two-gene expression ratio HOXB13:IL17BR has been proposed to predict the outcome of tamoxifen-treated breast cancer patients. We intended to examine whether this ratio can predict the benefit of 5 years vs. 2 years of tamoxifen treatment of postmenopausal patients. A further objective was to investigate any prognostic effects of the ratio in systemically untreated premenopausal patients. Based on the current knowledge of HOXB13 and IL17BR, we hypothesized that these genes may have individual prognostic or predictive power. Patients and methods: Expression of HOXB13 and IL17BR were quantified by real-time PCR in tumors from 264 randomized postmenopausal patients and 93 systemically untreated premenopausal patients. Results: A high HOXB13:IL17BR ratio was associated with aggressive tumor characteristics, as were low levels of IL17BR alone. The ratio and HOXB13 alone predicted recurrence-free survival after endocrine treatment, with a benefit of prolonged treatment in estrogen receptor-positive patients correlated to a low ratio (recurrence rate ratio: RR=0.39; p=0.030), or low expression of HOXB13 (RR=0.37; p=0.015). No difference in recurrence-free survival was seen for the high ratio or high HOXB13 subgroups. The predictive value of HOXB13 and HOXB13:IL17BR was significant in multivariate analysis. In the systemically untreated cohort, only IL17BR showed independent prognostic significance. Conclusion: We conclude that the ratio or HOXB13 alone can predict the benefit of endocrine therapy, with a high ratio or a high expression rendering patients less likely to respond. We have also shown that IL17BR might be an independent prognostic factor in breast cancer.The original publication is available at www.springerlink.com: Piiha-Lotta Jerevall, Sara Brommesson, Carina Strand, Sofia Gruvberger-Saal, Per Malmström, Bo Nordenskjöld, Sten Wingren, Peter Söderkvist, Mårten Fernö and Olle Stål, Exploring the two-gene ratio in breast cancer – independent roles for HOXB13 and IL17BR in prediction of clinical outcome, 2008, Breast Cancer Research and Treatment, (107), 2, 225-234. http://dx.doi.org/10.1007/s10549-007-9541-8. Copyright: Springer, www.springerlink.co