Introduction to principles of spatiotemporal modelling and streamlined implementation of models through Chap

Developing your own custom model with CHAP

CHAP is designed to allow model developers to easily develop their own models outside CHAP and use CHAP to benchmark/evaluate their models, or to import and use utility functions from CHAP in their own models.

We here provide guides for implementing custom models in Python and R. The recommended flow is slightly different for the two different languages, but the general idea is the same.

We have provided several example code base templates that contain minimal code and instructions on how to get started.

If you already have experience with statistical/machine learning modelling, you can go directly to our page for more experienced modellers, which directly explains how to employ chap when already knowing how to develop models If you want to learn more about modelling as you go, please follow our material that employs Chap to more efficiently teach core concepts of modelling, focusing on climate-sensitive disease forecasting as case through a combined coverage of theoretical and practical aspects: runs through Chap