Whole Disease Modeling
Whole Disease Modeling is a method that creates a comprehensive simulation of an entire disease and its treatment pathway. It helps decision-makers compare multiple health interventions in a consistent way, especially when looking at how resources should be best used across different stages of a disease.
At a glance
Use when
Evaluating multiple interventions across a disease continuum; informing system-wide resource allocation; avoiding siloed HTA assessments
Avoid when
Only a single intervention or narrow clinical question is of interest; data are highly uncertain across most of the pathway; limited modeling capacity or time
Inputs
Epidemiological data, treatment patterns, cost data, utility values, intervention effects, disease progression parameters
Outputs
Cost-effectiveness results across multiple interventions, resource use estimates, health outcomes (e.g., QALYs), system-level impact predictions
How it works
Developed at ScHARR, University of Sheffield, Whole Disease Modeling is a methodological framework that integrates multiple interventions and disease stages within a single coherent model structure. It enables consistent, system-level resource allocation decisions by simulating the full disease pathway, including prevention, diagnosis, treatment, and long-term outcomes, using decision-analytic modeling techniques.
- HTA domains
- Clinical Effectiveness, Costs & Economic Evaluation, Organisational aspects
- Categories
- Cost-effectiveness ModellingData Exploration & VisualizationModel ValidationPredictive Modelling
- Assumptions
- Disease pathways can be realistically structured and parameterized; data are available or can be synthesized across the pathway; interventions act within a connected system rather than in isolation
- Strengths
- Enables consistent comparison of diverse interventions; captures interactions and trade-offs across the care pathway; supports holistic decision-making; reduces risk of suboptimal decisions from fragmented analyses
- Limitations
- Requires extensive data and modeling expertise; can become complex and computationally intensive; transparency may be challenging due to model size; validation across all components is difficult
- Also known as
- Whole Disease Model, Tappenden's Whole Disease Modeling
Questions this answers
- › How do multiple interventions across a disease pathway compare in terms of cost and health outcomes?
- › What is the optimal allocation of resources across prevention, diagnosis, and treatment stages?
- › How do changes in one part of the disease pathway affect outcomes and costs elsewhere?
- › What are the long-term population-level impacts of introducing a new intervention?
- › How can modeling avoid fragmented evaluations of isolated interventions?
- › What are the cumulative benefits and costs of sequential interventions within a disease?
References & sources
Similar by meaning
Beta record. Based on the original catalogue summary; primary-source enrichment pending.