The North American Animal Disease Spread Model: A simulation model to assist decision making in evaluating animal disease incursions

Harvey, Neil; Reeves, Aaron; Schoenbaum, Mark A.; Zagmutt-Vergara, Francisco J.; Dubé, Caroline; Hill, Ashley E.; Corso, Barbara; McNab, W. Bruce; Cartwright, Claudia I.; Salman, Mo

doi:10.1016/j.prevetmed.2007.05.019

Cited by 140 publications

(158 citation statements)

References 28 publications

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“…The simulation was performed using the computer-based North American Animal Disease Spread Model (NAADSM) version 4.0.11, a temporal and spatial stochastic model based on the Monte Carlo method (Harvey et al 2007). The basic idea underlying the Monte Carlo method is the approximation of the expected value E(X) by the arithmetic mean of results of a large number of independent tests (simulations) all with the same distribution as X.…”

Section: Methodsmentioning

confidence: 99%

“…The software was developed by a team of experts of the Centre for Epidemiology and Animal Health at the US Department of Agriculture in Fort Collins, Colorado (Veterinary Services Science, Technology, and Analysis Services-Centre for Animal Health and Epidemiology-VS-Ceah STAS) (Harvey et al 2007). …”

Section: Methodsmentioning

confidence: 99%

“…Since the progression of an epidemic largely depends on environmental factors, the host and the pathogen, risk factors that are associated with this epidemiological triangle influence each epidemic and make them different (Harvey et al 2007;Harvey and Reeves 2010).…”

Section: Methodsmentioning

confidence: 99%

“…These contacts are related to transport of animals from one farm to another and indirect contacts through vehicles and people. The model also simulates the local spread of the disease (Harvey et al 2007;Stanojevic 2014).…”

Section: Methodsmentioning

confidence: 99%

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Simulation of a classical swine fever outbreak in rural areas of the Republic of Serbia

Valčić¹,

Stanojevic²,

Radojičić³

et al. 2015

Vet. Med. - Czech

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Several different strategies for control and eradication of classical swine fever (CSF) were compared using a Monte Carlo method-based simulation model. The control strategy analysed in this paper, in addition to other CSF control measures, includes application of biosecurity measures on pig farms and rural backyard holdings. Elements of the control strategy are based on applicable regulations and include the simulation of detection of the disease, setting up the protected and surveillance zones, standstill of pig movements and restricted movement of animals, vehicles, equipment, and people with strong control measures in protection and surveillance zones, euthanasia of susceptible pigs, protective vaccination of pigs, compensation etc. During the simulation, different output parameters were compared such as: duration of epidemic of a disease, number of affected holdings and animals, direct costs such as those for dead or culled animals, costs of surveillance, disposal of infectious materials, cleaning and disinfection. Depopulation of affected animals with early diagnostics and vaccination in the protection and surveillance zone proved to be the most effective measures to stop the spread and for the eradication of the disease. Moreover, during the simulation, systematic implementation of biosecurity measures in all pig production clusters was demonstrated to be an appropriate strategy for sustainable control of CSF and the establishment of a stable epidemiological situation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Simulation of a classical swine fever outbreak in rural areas of the Republic of Serbia

Valčić¹,

Stanojevic²,

Radojičić³

et al. 2015

Vet. Med. - Czech

View full text Add to dashboard Cite

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“…These types of systems can provide knowledge about the past, the present, and the future. Some interesting computer simulations include production systems (Huang et al 1983), weather forecasting (Johnson et al 1996), the big bang (Shavitt and Tankel 2004), flight (Jones et al 1999), health-care (Jun et al 10 1999;Brailsford and Hilton 2001), and disease spread (Harvey et al 2007). From this point forward, simulation will refer to computer simulation.…”

Section: Simulationmentioning

confidence: 99%

Integrated optimization and simulation models for the locomotive refueling system configuration problem

Verschelden¹,

Stamm²,

Easton³

2017

2017 Winter Simulation Conference (WSC)

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Locomotives in the U.S. use over 3 billion gallons of fuel each year and faster refueling can increase rail network capacity without the infrastructure cost associated with new terminals or tracks. This thesis introduces the locomotive refueling system configuration problem (LRSCP), which seeks to improve efficiency in refueling yards through new technologies or policies. This research also creates two new methods to solve LRSCP. Based on experimental results, solving either TRIP or SLIP and incorporating the optimal refueling policy improves railyard operations by 10 to 30%. This impact is statistically significant and increases the capacity of a railyard. Additionally, it impacts other important parameters such as time spent in the yard and the maximum queue for the railyard. Furthermore, there is a significant decrease in wasted time and an improvement to railyard efficiency.Implementing either method should increase a railyard's capacity and significantly increase revenue opportunities.iv

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On the distributed orchestration of stochastic discrete event simulations

Sui

Harvey

Pallickara

2013

Concurrency and Computation

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Discrete event simulations are a powerful technique for modeling stochastic systems with multiple components where interactions between these components are governed by the probability distribution functions associated with them. Complex discrete event simulations are often computationally intensive with long completion times. This paper describes our solution to the problem of orchestrating the execution of a stochastic, discrete event simulation where computational hot spots evolve spatially over time. Our performance benchmarks report on our ability to balance computational loads in these settings.The Unix version of NAADSM was written for a batch computing environment in which the modeler submits a job, and later receives completed outputs from the job, with no interaction in between. In contrast, Granules is a highly asynchronous framework in which computations may receive messages over their communication streams at any time. This mismatch was resolved by having the worker component, which is coded in Java, handle the asynchronous communications and act as a buffer for incoming messages. Communication between the worker component and the NAADSM executable is then accomplished synchronously, at specific points in NAADSM's run loop, via the stdin and stdout streams of the NAADSM processes. Consistent with our goal of loose coupling, this avoids the need to make deep changes in the NAADSM code (such as transforming it into a multithreaded program) just to support interaction with Granules.Partitioning the simulation by geography presented a challenge. Some interactions between farms do not have distance constraints associated with them, owing to the model's original scope of Figure 3. Interactions between the controller and the workers.

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The North American Animal Disease Spread Model: A simulation model to assist decision making in evaluating animal disease incursions

Cited by 140 publications

References 28 publications

Simulation of a classical swine fever outbreak in rural areas of the Republic of Serbia

Simulation of a classical swine fever outbreak in rural areas of the Republic of Serbia

Integrated optimization and simulation models for the locomotive refueling system configuration problem

On the distributed orchestration of stochastic discrete event simulations

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