botech comparisons

Description

Generate tables of results, which are extracted from a data source.

• These can be different datatypes:
  • comparison: (the most common) two scenarios, compared by effects, costs and cost-effectiveness
  • record: the individual records, with effects and costs
• These can be presented by: region, income, appendix 3 status, author, intervention, or any combination of these
• These can be filtered by: region, income, appendix 3 status, author, intervention, scenario, or any combination of these
• These can be in various formats: csv, html, pandas dataframes, python lists.

Part of the Botech ecosystem, written by Forecast Health Australia

Context

This repository was created because we were generating a lot of health-economic model data, often from different sources, and we wanted to compare it in lots of ways.
I was frustrated with hard coding comparisons, and felt there was a better way to do it. There are lots of solutions to this type of thing, but it seemed marginally easier to write a new package that we could modify as our use cases changed.

How I use it

1. Have some general mechanism to convert modelling results to a csv of Records
2. Generate a configuration file
3. Invoke this package, either by installing it, or running the main script and passing your files as arguments.
4. Use the results however you'd like e.g.
   • Render the results as an html and host them on a website

Installation

Requirements

• Python (built with 3.10.12)
• country-metadata
• pandas

Setup

• git clone https://github.com/ForecastHealth/botech-comparisons.git
• pip install -r requirements.txt (use a virtual environment)

Usage

Data Types

There are two python dataclasses and one enum which are important to understand: The Record, the Comparison and the Filter. You can find their definitions in the datatypes module.

In particular:

• The Record is important, because we expect the underlying database to be a CSV file with the schema of a Record
• The Filter is important, because you can filter and group using these elements.

The configuration file

Write a config.json, which defines the following:

• data_type: the type of table you want to return (explained below)
  • blueprint (note - this is probably not useful unless you already know what it is)
  • filtered_records (individual records)
  • comparisons (comparisons of records - probably what you want)
• data_format: the format of the table you want to return csv, html, dataframe, or self.
  • dataframe is a pandas.DataFrame
  • self is a list of the data type
• scenarios is a list of exactly two elements, where each element corresponds to a scenario. These must be labelled in your dataset, e.g. baseline and scale-up
• groups is a list of lists, with each nested list being the ways you want to present the data. For instance, if you have list ["region", "income"], this means you want the data to be presented by region by income e.g. "North America x High Income", "Oceania x Low Income", etc.
• filters are dictionary of Filters where the value is a list of values that you want to include. e.g.
  • "income": ["HIGH INCOME"] will only include results from high income countries
  • "country": ["BRA", "MOZ"] will only include results from Brazil and Mozambique
  • etc

{
	"data_type": "comparisons",
	"data_format": "html",
	"scenarios": ["baseline", "scaleup"],
	"groups": [
		["region", "income"]
	],
	"filters": {
		"income": ["HIGH INCOME"],
		"intervention": [0]
	}
}

The API

Please refer to the init.py to read the high-level api create_tables(). The configuration can be created by parsing a JSON configuration using parse_configurations and the data will need to be provided by the user and parsed using something like pandas.read_csv().

Contributing

Feel free to fork, or submit a user issue. If you'd like to be added as a contributor, please message me, or email our website Forecast Health Australia

Tests

Tests are built with unittest and can be run locally using

python -m unittest discover tests

from the root directory.

Authors

Rory Watts, Forecast Health Australia

License

This repository is licensed under the Apache 2.0 License.

Contact Information

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