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Value Based Prioritization

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Academic article:


pip3 install vbp


Value Based Prioritization (vbp) uses value theory to quantitatively prioritize potential actions to accomplish a goal.

The module may be used from the command line to perform different VBP actions such as listing actions (list), counting actions (count), predicting values (predict), running Modeled VBP (modeled_value_based_prioritization), and more. For usage, run:

python3 -m

Any non-terminal output goes to the vbpoutput sub-folder.

Here is a simple example counting the number of groupings of underlying causes of death for the United States for the default data type:

python3 -m count UCODUnitedStates

Alternatively, if installed through pip, a vbp script wrapper may be used:

vbp count UCODUnitedStates

The academic article above includes footnotes with details on how to run vbp to produce the output of each step.

This package provides abstract classes and utility methods to run VBP, mostly focused on Modeled VBP which uses time series data to predict future values and prioritize actions based on the relative predicted values.

The vbp.DataSource class is the base abstract class for VBP.

The vbp.TimeSeriesDataSource abstract class inherits from vbp.DataSource and may be used for Modeled VBP. The vbp.ExampleDataSource class demonstrates a simple data source based on vbp.TimeSeriesDataSource.

Built-in Modeled VBPs include Underlying Cause of Death models for the United States (vbp.ucod.united_states.UCODUnitedStates) and the World ( These data sources both inherit from vbp.ucod.icd.ICDDataSource which inherits from vbp.TimeSeriesDataSource.


The model type is specified with --ets, --ols, and/or --prophet. These are not mutually exclusive; if combined during modeled_value_based_prioritization, an average is taken of the results. The default is --ets.

By default, action names are obfuscated to reduce bias during model building and testing. Specify --do-not-obfuscate to show actual names.

Some data sources have different data types (e.g. mutually exclusive groupings of data). Add the -a argument before the data source name to run for all data types. Add the --data-type X argument after the data source name to specify a specific data type.

In general, a list of actions may be specified to run for just that list; otherwise, without such a list, all actions are processed.


python3 -m modeled_value_based_prioritization UCODUnitedStates

python3 -m modeled_value_based_prioritization UCODUnitedStates --do-not-obfuscate "Heart disease" Cancer

Exponential Smoothing

Using exponential smoothing:

python3 -m modeled_value_based_prioritization ${DATA_SOURCE} --ets

Specify --ets-no-multiplicative-models to only use additive models.

Specify --ets-no-additive-models to only use multiplicative models.

Linear Regression

Using linear regression.

python3 -m modeled_value_based_prioritization ${DATA_SOURCE} --ols

Specify --ols-max-degrees X to model higher degrees.


Using Facebook Prophet.

python3 -m modeled_value_based_prioritization ${DATA_SOURCE} --prophet

United States

As of 2019-03-01, the unzipped U.S. mortality data consumes ~36GB of disk. It will be downloaded and unzipped automatically when a function is used that needs it.

Long-term, comparable, leading causes of death

Generate data/ucod/united_states/comparable_data_since_1959.xlsx for all long-term, comparable, leading causes of death in

python3 -m prepare_data UCODUnitedStates

Rows 1900:1957 and the sheet Comparability Ratios in data/ucod/united_states/comparable_ucod_estimates.xlsx were manually input from

Open comparable_data_since_1959.xlsx and copy rows 1959:Present.

Open comparable_ucod_estimates.xlsx and paste on top starting at 1959.

Process comparable_ucod_estimates.xlsx with its Comparability Ratios sheet to generate comparable_ucod_estimates_ratios_applied.xlsx:

python3 -m prepare_data UCODUnitedStates --comparable-ratios

Final output:

python3 -m modeled_value_based_prioritization UCODUnitedStates --data-type UCOD_LONGTERM_COMPARABLE_LEADING


As of 2019-03-01, the unzipped World mortality data consumes ~320MB of disk. It will be downloaded and unzipped automatically when a function is used that needs it.

When testing, writing data spreadsheets takes a lot of time and may be avoided with --do-not-write-spreadsheets.

Creating a new Data Source

Review vbp/ for a simple example. The basic process is:

  1. Create a sub-class of vbp.DataSource in
  2. Implement all @abc.abstractmethod methods and override any other superclass methods as needed.
  3. Import at the top of vbp/



pip3 install numpy pandas matplotlib statsmodels scipy fbprophet

Updating PyPI package:

# Edit version in and
python3 bdist bdist_wheel
python3 -m twine upload --skip-existing dist/*

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