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replace integrate with connect, refer to a graph as a set of connected or related elements. homogenize first sentence of each pipeline. replace question at the end.
Copy file name to clipboardExpand all lines: learn/pipelines/index.qmd
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---
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## The pipeline approach
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## The Pipeline approach
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We can solve Outbreak Analytics *tasks*with sets of packages integrated in *pipelines*.
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We can solve Outbreak Analytics *tasks*connecting multiple packages in *pipelines*.
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## Outbreak analytics
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### Tasks
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We can view Outbreak analytics as a set of data analysis __Tasks__. In @fig-tasks we represent this in a directed graph, where each *node* is a Task and each *directed edge* represents the flow of input and output data. Tasks are connected similarly to the [tidyverse](https://r4ds.hadley.nz/whole-game.html) diagram for exploratory data analysis.
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We can view Outbreak analytics as a set of related data analysis __Tasks__. In @fig-tasks we represent this in a directed graph, where each *node* is a Task and each *directed edge* represents the flow of input and output data. Tasks are connected similarly to the [tidyverse](https://r4ds.hadley.nz/whole-game.html) diagram for exploratory data analysis.
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{#fig-tasks}
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We defined a __Pipeline__ as a set of connected Tasks required to obtain an informative outcome for decision-making purposes.
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For example, to quantify the time-varying reproduction number (@fig-pipe-01), we first*Read case data* to generate a linelist. Then, we *Describe case data*, using the linelist as inputs to generate delay distributions and epicurves. Finally, we use both outputs as inputs to *Quantify transmission* and generate an estimate of transmission. This output allows us to determine the intensity of interventions needed to achieve epidemic control [@cori2017key].
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For example, to quantify the time-varying reproduction number we can follow the *Transmissibility pipeline*(@fig-pipe-01). First, we *Read case data* to generate a linelist. Then, we *Describe case data*, using the linelist as inputs to generate delay distributions and epicurves. Finally, we use both outputs as inputs to *Quantify transmission* and generate an estimate of transmission. This output allows us to determine the intensity of interventions needed to achieve epidemic control [@cori2017key].
Similarly, in @fig-pipe-02, we simulate the final size of an epidemic. First, we *Read population data* to obtain its demographic distribution and social contact matrix. Next, we collect the estimate of transmission data output, ideally from the *Transmissibility pipeline*. Finally, we use these three data as inputs to *Simulate transmission scenarios* and determine the proportion of the population infected. This output allows us to assess the long-term impact of the outbreak and evaluate intervention choices [@cori2017key].
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Similarly, in @fig-pipe-02, to simulate the final size of an epidemic we can follow the *Scenarios pipeline*. First, we *Read population data* to obtain its demographic distribution and social contact matrix. Next, we collect the estimate of transmission data output, ideally from the *Transmissibility pipeline*. Finally, we use these three data as inputs to *Simulate transmission scenarios* and determine the proportion of the population infected. This output allows us to assess the long-term impact of the outbreak and evaluate intervention choices [@cori2017key].
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