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on 21-Nov-2019 (Thu)

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The 2014–2015 outbreak of Ebola in West Africa serves to highlight both opportunities and challenges in modeling for public health. In the initial phase of this outbreak, real-time estimates of the reproduction number or simple exponential extrapolation (14) allowed short-term predictions of epidemic growth that were used, for example, to plan for necessary bed capacity. Quantitative phylogenetic tools applied to samples from initial victims provided important estimates of the origin of the outbreak (15). Early mechanistic models that explicitly took into account the roles played by different transmission routes or settings were informed by analysis of earlier outbreaks (16, 17).
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When the Distinct checkbox is marked, only unique values will be considered in the aggregation. This can be useful when, for example, we want to count the number of distinct customers, instead of their total number of appearances in the database.
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Bar charts versus line charts
While bar and line charts are considered interchangeable by many, there are actually specific use cases in which it is advisable to use one over the other. Bar charts are best used to compare different categories, for example, for comparing different Flight Types. Line charts are best used to detect trends in series that have an order, such as dates or steps within a process.
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Scatter Charts expressions
It is important to keep in mind that when dealing with scatter charts the expression that is defined first will be used for the X-axis, the second expression will be used for the Y-axis, and the third expression will always be used to set the bubble size
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We (Reich et al. 2019) present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.
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The FluSight challenge focuses on forecasts of the weighted percentage of doctor’s office visits where the patient showed symptoms of an ILI in a particular US Health and Human Services (HHS) region. Weighting is done by state population as the data are aggregated to the regional and the national level. This wILI metric is a standard measure of seasonal flu activity, for which public data are available for the United States back to the 1997/1998 influenza season (Fig. 1A).
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A majority of models consistently showed higher accuracy than historical baseline forecasts, both in regions with more predictable seasonal trends and in those with less consistent seasonal patterns (Figs. 3 B, D, and E); a majority of the presented models showed consistent improvement over the historical baseline for 1and 2-wk-ahead forecasts, although fewer models consistently outperformed the baseline model for 3- and 4-wk-ahead forecasts (Fig. 3E); at the presented spatial and temporal resolutions for influenza forecasts, we did not identify substantial or consistent differences between high-performing models that rely on an underlying mechanistic (i.e., compartmental) model of disease transmission and those that are more statistical in nature (Table 2); and forecast accuracy is significantly degraded in some regions due to initial partially reported real-time data (Fig. 6).
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Since ILI data represent an impure measure of actual influenza transmission, purely mechanistic models may be at a disadvantage in comparison with more structurally flexible statistical approaches when attempting to model and forecast ILI. To counteract this potential limitation of mechanistic models in modeling noisy surveillance data, many forecasting models that have a mechanistic core also use statistical approaches that explicitly or implicitly account for unexplained discrepancies from the underlying model (20, 26).
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relatively few additional data sources have been incorporated into these models
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no models are included that explicitly incorporate information about circulating strains
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no model explicitly includes spatial relation- ships between regions
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while seven seasons of forecasts from 22 models is the largest study we know of that compares models from multiple teams, this remains a less-than-ideal sample size to draw strong conclusions about model performance.
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Reich et al. (2019)'s results should not be used to extrapolate hypothetical accuracy in pandemic settings, as these models were optimized specifically to forecast seasonal influenza
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Gauges, don't use dimensions
If a dimension is present in the gauge chart, the gauge will show the value for the first sorted value in the dimension field. Always ensure, that no dimension is selected. This is especially important to keep in mind when using Fast Type Change on charts.
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Access hidden objects on a sheet
Remember that we can always access any object's properties via the Objects sheet of the Sheet Properties dialog (Ctrl + Alt + S).
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Flashcard 4598300019980

Question
Definition Betriebssysteme:
Answer
“The programs of a digital computing system which lay - together with the basic properties of the computing system - the foundation for the possible modes of operation and especially control and monitor the execution of programs.“

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Dimension Limits

In the Dimension Limits window, all dimensions available in the chart are listed to the left.

Simply highlight the desired dimension to which the Dimension Limits configuration should apply to and select any of the following settings to control the number of dimension values displayed:

• From the Show only option we can select First, Largest, or Smallest x values

• From the Show only values that are option we can select Greater than, Less than, Greater than or equal to, or Less than or equal to a certain value, which can be given as: ° A percentage relative to the total ° An exact amount

• From the Show only values that accumulate to option we can select a certain value, which can be given as: ° A percentage relative to the total ° An exact amount

The difference between the second and the third options is that the former evaluates the individual result corresponding to the dimension's value, while the latter evaluates the cumulative total of that value by either sweeping from largest to smallest or vice versa. This can be used, for instance, in a Pareto analysis in which we would present all carriers that make up the 80 percent of the flights, leaving all the rest out.

Dimension limits can only be set based on the first expression. In case the chart has more than one expression, the rest are not taken into account

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Using Variables in expressions

There are a few points about the steps we used that you will want to take note of:

• Enclosing the expression in parentheses: As we want to make sure that the expression always gets calculated in the right order, we enclose it in parentheses. Imagine, for example, we had an expression vExample containing 10 + 5 without parentheses. If we were to use that variable in an expression containing a fraction, for example, $(vExample) / 5, the wrong result would be returned (11 instead of 3).

• Not prefixing the variable expression with an equals sign: When the expression in a variable definition is prefixed with an equals sign (=), the variable gets calculated globally. In our example this would mean that the Load Factor % value is calculated once for the entire data model. When used in a chart, all dimensions would be ignored and the expression would just return the same global value for each dimension. As we obviously do not want this to happen, in this example we do not prefix our expression with an equals sign.

• Dollar Sign Expansion: Enclosing a variable (or an expression) between a dollar sign and parentheses (Dollar Sign Expansion), as we did on the chart's expressions, tells QlikView to interpret the contents, instead of just displaying the contents. For example, $(=1 + 1) will not return the static text 1 + 1, but will return 2. We will look at Dollar Sign Expansion in more detail in Chapter 10, Advanced Expressions. For now, it's sufficient to note that, when referencing variables, we should use the Dollar Sign Expansion syntax in order for them to be interprete

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