Climate Health Analytics Platform (CHAP) ============================================================== CHAP 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 CHAP. For more general information about the project, we refer to `the CHAP project wiki `_. The documentation here is divided into sections referring to different use-cases: ---- **Full installation and integration with DHIS2** For users who want to fully install CHAP locally or on a server, e.g. to integrate with DHIS2, we recommend :doc:`setting up CHAP with docker compose ` and :doc:`using the Prediction app `. ----- **Integrating external or custom models with CHAP** For users who want to develop custom forecasting models and run or benchmark these through CHAP, or to simply evaluate external models on small example datasets, we recommend :ref:`installing the chap-core Python package ` and folowing the guides on :ref:`integrating external models ` and :ref:`developing custom models `. ----- **Using CHAP as a library** For users who want to use CHAP as a library, we refer to the tutorials and API documentation (see the menu). ---- All pages ---------- The following is an overview of all pages in the documentation: .. toctree:: :glob: :maxdepth: 2 :caption: Installation and getting started installation docker-compose-doc .. toctree:: :glob: :maxdepth: 2 :caption: Using the Predictions App with CHAP (and integration with DHIS2) prediction-app/* tutorials/downloaded_json_data .. toctree:: :glob: :maxdepth: 2 :caption: Integrating external or custom models with CHAP external_models/* .. toctree:: :glob: :maxdepth: 2 :caption: Using CHAP as a library tutorials/wrapping_gluonts api_docs