outbreak-theme/.coderabbit.yaml at main · epiforecasts/outbreak-theme · GitHub

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# CodeRabbit configuration for HPAI modelling challenge
# Following Abbott et al. workflow for infectious disease modelling
# Workflow reference: https://github.com/seabbs/infectious-disease-modelling-with-multiple-datasources

language: en-GB

# Chat context for CodeRabbit to understand the project
chat:
  auto_reply: true

reviews:
  high_level_summary: true
  high_level_summary_placeholder: "@coderabbitai summary"
  poem: false
  review_status: true
  collapse_walkthrough: false
  path_filters: []
  abort_on_close: true
  auto_review:
    enabled: true
    drafts: false
  request_changes_workflow: true  # Request changes, then approve when resolved

  path_instructions:
    - path: "hpai-challenge/notes/step00_*.md"
      instructions: |
        This is Step 00 of the workflow: Data Source Characterisation.
        Review for: completeness of data source coverage, appropriate use of the
        six-category checklist (metadata, scope, resolution, quality, utility, practical),
        and identification of potential biases and limitations.

    - path: "hpai-challenge/notes/step01_*.md"
      instructions: |
        This is Step 01: Research Questions and Target Estimands.
        Review for: clearly specified estimands, alignment with available data,
        and identification of causal vs associational questions.

    - path: "hpai-challenge/notes/step02_*.md"
      instructions: |
        This is Step 02: Process DAG Development.
        Review for: biological plausibility of transmission mechanisms,
        appropriate state variables, parameter identifiability considerations,
        and clear distinction between latent states and observations.

    - path: "hpai-challenge/notes/step03_*.md"
      instructions: |
        This is Step 03: Data Source Selection.
        Review for: justified inclusion/exclusion decisions, appropriate handling
        of missing data, and alignment with process DAG requirements.

    - path: "hpai-challenge/notes/step04_*.md"
      instructions: |
        This is Step 04: Observation DAG Construction.
        Review for: complete mapping from latent states to observations,
        appropriate delay distributions, and detection process modelling.

    - path: "hpai-challenge/notes/step05_*.md"
      instructions: |
        This is Step 05: Refining the Model DAGs.
        Review for: parameter identifiability analysis, appropriate
        reparameterizations, and documented simplifying assumptions.

    - path: "hpai-challenge/notes/step06_*.md"
      instructions: |
        This is Step 06: Modularising DAGs.
        Review for: independent testable modules, clear interfaces,
        and appropriate development order.

    - path: "hpai-challenge/notes/step07_*.md"
      instructions: |
        This is Step 07: Inference and Computation Choices.
        Review for: appropriate likelihood formulation, justified choice
        of inference method, and computational feasibility.

    - path: "hpai-challenge/notes/step08_*.md"
      instructions: |
        This is Step 08: Model Implementation.
        Review for: correct implementation of DAG specifications,
        appropriate priors, and clean modular code.

    - path: "hpai-challenge/notes/step09_*.md"
      instructions: |
        This is Step 09: Model Specification and Validation.
        Review for: prior predictive checks, MCMC diagnostics,
        posterior predictive checks, and simulation-based calibration.

    - path: "hpai-challenge/notes/step10_*.md"
      instructions: |
        This is Step 10: Data Integration Choices.
        Review for: appropriate integration strategy (joint vs staged),
        module compatibility, and uncertainty propagation.

    - path: "hpai-challenge/**/*.jl"
      instructions: |
        Julia implementation of HPAI transmission model.
        Review for: type stability, efficient array operations,
        appropriate use of Turing.jl/other probabilistic programming.

knowledge_base:
  opt_out: false
  web_search:
    enabled: true
  learnings:
    scope: "local"

# Project context:
# This project follows the Abbott et al. "Workflow for Infectious Disease Modelling"
# from https://github.com/seabbs/infectious-disease-modelling-with-multiple-datasources
#
# The workflow has 11 steps (00-10):
# 00. Data Source Characterisation - structured checklist for epidemiological data
# 01. Research Questions and Target Estimands - define what we want to estimate
# 02. Process DAG Development - model the latent transmission process
# 03. Data Source Selection - choose which data to use
# 04. Observation DAG Construction - model how we observe the process
# 05. Refining the Model DAGs - iterate on identifiability and assumptions
# 06. Modularising DAGs - decompose into testable components
# 07. Inference and Computation Choices - select fitting methods
# 08. Model Implementation - write the code
# 09. Model Specification and Validation - prior/posterior checks, diagnostics
# 10. Data Integration Choices - combine modules and data sources