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Discharge varied up to one order of magnitude across the stream and the model was successful in capturing this variability.
Therefore a dynamic model capturing this variability was used to estimate the two infectiousness parameters.
Capturing this variability in applicably useful ways for large study population studies is challenging and often a multi-faceted approach using self-administered questionnaires, walk-through surveys, instrument deployments, and sentinel monitoring.
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Properly enveloping the expected properties of interest requires both a substantial amount of data to statistically capture this variability and a representative distribution capable of accurately describing the range of values.
Expectedly, the generic landscape model could not capture this variability.
Patients with different clinical phenotypes have been diagnosed as having migraine, and even strict classification according to IHS criteria [15, 16] may not sufficiently capture this variability and may be a source of misclassification in all studies.
Simply creating longer synthetic times series, by undertaking further model runs, is unlikely to capture this variability as the distributions describing the various attributes of the TC climatology are still based on historical records that may under-represent the more extreme events.
For mathematical models to capture this variability, a stochastic description is required.
Therefore, we suggest that it would be of utmost importance to capture this variability in a prospective longitudinal study.
Another limitation is the possible difference in how units interpret 'review of bacteraemia rates' and 'root cause analysis' as the survey may not have captured this variability.
To capture this variability in property meaning, we paired each property with two concepts, resulting in two different concept-property items.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com