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In contrast, the competing risk branching process could not describe the kinetics of CD8+ T cells.
The fitted competing risk branching process is shown in Figure 6.
As explained below these two conclusions are difficult to reach simultaneously under the competing risk branching process.
When fitting the competing risk branching process to our experimental data, we reached a compromise between the above two scenarios.
An application is presented where we study the proliferation of human CD8+ T lymphocytes using our model and a competing risk branching process.
On the other hand, the competing risk branching process could capture an increase of the population size by letting the latent r.v.s λ g,0 and λ g,1 be such that.
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These independent studies therefore corroborate the conclusion that we have reached regarding competing risk branching processes.
The limitations of competing risk branching processes will also appear in our analysis of experimental data on CD8 T cells.
However, unlike other studies, we used the outbreak final size distribution equations derived from branching process theory to calculate the risk for a large Ebola outbreak under the assumptions of immediate and delayed transmission control after an importation.
Finally, the mean disintegration time was estimated as 5.4 hours using the competing risk model, which is much shorter than the 45 hours that we obtained with the proposed branching process, and explains why the proportion of dead cells in each generation is still well represented by the competing risk process.
Branching process models occur in an amazing variety of applications.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com