The Chap modelling platform

The Chap modelling platform is a platform for forecasting and for assessing forecasts of climate-sensitive health outcomes. In the early phase, the focus is on vector-borne diseases like malaria and dengue.

The platform can perform data parsing, data integration, forecasting based on any of multiple supported models, automatic brokering of compatible models for a given prediction context and robust forecast assessment and method comparison.

This documentation contains technical information about installing and using the modelling platform. For more general information about the Chap project,

we refer to the CHAP project wiki.

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All pages

The following is an overview of all pages in the documentation:

Installation and getting started

• Installation and Getting Started
  • Option 1: Setting up CHAP Prediction App in DHIS2
  • Option 2: Setting up CHAP REST-API
  • Option 3: Setting up CHAP Core CLI tool
• Releases and Future Plans
  • Next Milestone
  • Previous Milestones

Using the Predictions App with the Chap modelling platform and DHIS2

• How to use Prediction App
  • 1 Selecting prediction data
  • 2 Status for predictions and prediction import
  • 3 Import prediction page
  • 4 Visualize

Using as a CLI Tool

• Running on JSON Data Downloaded from the Prediction App
  • Convert the JSON Data into a CHAP-DataSet
  • Evaluate Models on the Dataset
  • Predict Using the Best Model
• Use JSON-file from Prediction App with CHAP Core CLI
  • Convert the JSON data into a CHAP-DataSet
  • Evaluate models on the dataset
  • Predict using the best model

Using Chap modelling platform as a Python library

• Python API documentation
  • Data Fetching

Integrating external or custom models with the Chap modelling platform

• Making existing models compatible with CHAP
• Developing your own custom model with CHAP
  • Alternative examples in R
• External model specification
  • Defining an environment for the model
• Running models through CHAP
  • Running an external model on the command line
  • Running an external model in Python
• Integrating External Models with DHIS2 through CHAP
• Wrapping GluonTS models
• Overview of supported models
  • Epidemiar
  • EWARS
  • ARIMA
• Data requirements in CHAP
• Running models with examples
  • For Windows users

Contributor guide

• Contributor getting started
  • Getting started working with the chap-core codebase
• Important note for Windows contributors
  • First time WSL setup
  • Running commands through WSL
• Code overview
  • The chap command line interface
  • The REST API
  • External models
• Testing while developing
  • The quick tests
  • The comprehensive tests
• Writing and building documentation
  • How to edit the documentation
  • How to build the documentation locally