limcat

Limcat is a project using Locally-interacting Markov models for TB control in California.

This project was originally started as a model for South Africa. That repo can be found here: https://github.com/alexgoodell/limsa

There are a few core components:

A data model is defined by SQLAlchemy in the Flask app app.py
Data is stored in an SQLite database found in the database directory.
Data preparation and analysis is written as python file in limcat.py. ──── This will serve as the primary document for the project_
One of the core principles of this project is that of reproducibility. No changes to the database should be made directly. Instead, this data should be stored by modifying limcat.md. limcat.md destroys the database as its first command and rebuilds it from ground up.
The model itself is run in Go
There are many required packages for python and Go. Please see the dockerfile for the specific requirements.

Here are all the files and their purposes (as of Dec 29 2015).

├── Makefile  ────  Make is a program that simplifies a few processes
├── README.md ────  You are here
├── app.py ────  This has all of the database definitions
├── database ────  This folder holds the database file(s)
│   └── limcat-zero-index.sqlite ────  This is the core database file. It is zero-indexed
├── docker
│   └── Dockerfile ────  This is a dockerfile. Create a VM that can run the program
├── get_variables.py ────  The moves variables from main_inputs.xls to database
├── go 
│   ├── cli.go ────  Sets up command-line interface
│   ├── getters-and-setters.go ────  These get and set
│   ├── globals.go ────  All global variables
│   ├── index.go ────  This has most of the functionality
│   ├── io.go ────  This is input and output
│   ├── scenarios ────  This folder holds different scenarios used to simulate
│   │   ├── basecase-config.yaml ────  This is an example of the basecase simulation
│   │   └── qft-fb-med-risk-config.yaml ────  This is an example of an intervention
│   ├── tmp ────  This folder holds all of the outputs
│   │   ├── cycle
│   │   ├── cycle_state ────  The primary CSV outputs live here
│   │   ├── events
│   │   ├── eventsPSA
│   │   ├── master
│   │   ├── model_calib_results.json
│   │   ├── other
│   │   └── otherPSA
│   ├── types.go ────  This defines the different object types
│   ├── variable_calculations.go 
│   ├── variables.go
│   └── variables_template.txt
├── import_from_csv.py ────  This transfers data from the XLSs to the database
├── ipums.py ────  This populates the database with demographic data
├── latent_tb.py ────  This populates part of the database with prevalence estimates for LTBI
├── limcat.py ────  This builds the database, calls on ipums, import, stratify, etc 
├── make_zero_index_db.py ────  This converts the 1-indexed database that limcat.py creates to zero-indexed, which is what go needs
├── output_html ────  This folder holds all of the HTML output
├── outputs2.py ────  This is the primary visualizaition script
├── outputs_ttt.py ────  This script runs the TTT analysis
├── raw-data-files ────  This folder holds all the raw data sources
│   ├── active_CDPH.csv
│   ├── active_tb_prevalence.csv
│   ├── calib_cycles.yaml
│   ├── cdph_data.csv
│   ├── chis_raw.csv
│   ├── initialization_data.xlsx
│   ├── latent_tb_raw.csv
│   ├── main_inputs.xlsx
│   ├── raw_all_years_ipums_small.tar.gz
│   ├── risk_of_death.csv
│   ├── rvct.yaml
│   ├── rvct_calib_data.csv
│   ├── tb.csv
│   └── ~$main_inputs.xlsx
├── set_variables.py ────  This just populates the database with variables
├── stratify.py ────  This builds the stratification system where needed
├── tmp ────  Temp folder
└── ttt-results ────  This holds the ttt results

alexgoodell/california_tb

limcat