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The proposed multiplicative hazard regression model could be an interesting epidemiological tool to quantify the risk of communicable disease at hospital during community epidemics and the uncertainty inherent in such quantification.
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In rats, such quantification has been performed using clinical gamma cameras [11, 12].
In order to characterize the response itself, one could measure the avidity distribution of the T cell population for a given antigen, but such quantification in experimental assays remains not feasible.
Unfortunately, it has been difficult to accomplish such quantification in part because of methodologic limitations.
However, currently there is no free software for such quantification in the public domain.
Lewis' case for accepting commitment to possible worlds did not consist in just pointing at our quantifying on ways the world could be or have been, but also in providing independent motivations for taking such quantification at face value.
Such quantification is essential in order to determine the dynamics of filarial infection in vector populations and then to model its epidemiology.
Several uncertainty propagation methods are discussed in this context, and the practical challenges involved in such uncertainty quantification are outlined.
Thus, in such analyses, quantification of a small difference in signal intensity between PM and MM probes can be difficult due to the presence of cross-hybridization, and thus evaluation of specificity for single nucleotide mismatches is difficult at low target concentrations.
Second, the technique itself could be expanded to take into account data parameters and quantification; the formulæ described in Section 'Sample properties' gave a foretaste of such quantification and initial results indicate that quantification is a fertile ground for parallelism.
However, such quantification was not attempted here.
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