EI
ReadyEpidemic Intervention, Identifiability and Uncertainty Study
A completed synthetic-population mathematics study of SEIR dynamics, parameter identifiability, intervention timing, bootstrap uncertainty and global sensitivity.
- Python
- NumPy
- SciPy
- Pandas
- Matplotlib
- Jupyter
Software compatibility
Python 3.12 and 3.14The retained release uses Python 3.14.6. The Docker workflow verifies Python 3.12 compatibility with the same experiment structure and tests.
- Project ID
- GP-MA-0X9O2PO
- Domain
- Mathematics
- Complexity
- Advanced
- Software
- Python 3.12 and 3.14
- Catalogued
- 21 Aug 2026
- Licence
- Single buyer
- Repository
- Private handover
- Buyer record
- Never public
Included in the project
- Complete Python source code
- SEIRV equations with cumulative incidence and conservation checks
- Synthetic negative-binomial reported-incidence generator
- Bounded parameter fitting and calibration-window analysis
- Beta profile, local correlation and 80-replicate bootstrap
- Four intervention scenarios and nine start-day experiments
- 320-row Latin-hypercube uncertainty and PRCC study
- Fifteen reproducible figures and complete machine-readable results
- 93-page project report in PDF and editable Word formats
- 12-page setup and usage guide in PDF and editable Word formats
- 45 annotated references including current 2026 literature and guidance
- 14 automated tests with above 98 percent branch-aware coverage