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This spatial simulation approach allows us to randomly simulate outbreaks in space and time and eliminate bias associated with the selection of the timing and location of outbreaks.
The simulation approach allows all of the RAS's components such as equipment, biological processes (e.g., fish growth), and management practices to be evaluated jointly, so that an initial design can be fine-tuned to produce an optimized system and management regime suited to a specific fish farm within a reasonable time.
Further, the system's simulation approach allows ready quantification of uncertainty related to the routing of the county's timber.
The simulation approach allows us to have data with various levels of known prespecified "true" underlying type-specific infection burdens and genotyping test results based on the prespecified assay performance so the prevalence estimates calculated based on type-specific assay results can be compared to the pre-specified "true" underlying infection burden.
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For example, our simulation approach allowed us to obtain an accurate estimate of T1Galan Simul for the case of nine alleles in less than three minutes using the easy but slow programming language R, while the efficient implementation programmed by Galan et al. [ 13] in the much faster language C++ requires several hours to provide a similar value.
The single point formula, bipartite expression and simulation approach allowed extraction of accurate diffusion values from a previously reported approximated method.
We therefore used a simulation approach allowing us to monitor in silico the evolution of TBB under contrasting levels of assortative mating and environmental clines.
This simulation approach allowed for assessment of the effectiveness of the methodology (how often it successfully detects a hotspot), its timeliness (how long it takes after a hotspot is introduced to be detected) and its diagnostic capability (how accurately it describes the population affected by the hotspot).
Using a stochastic simulation modelling approach allows outbreaks with a variety of characteristics that are epidemiologically plausible to be generated.
The simulation-based approach allows one to express the functional dependence of erosion on the coating properties, and to quantitatively predict the erosion rate.
This simulation-based approach allows for automatic tuning of the MCA, by using a genetic algorithm that is proposed to analyse the results obtained from multiple simulations of the MCA with different parameters.
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