12. Uncertainty and Sensitivity Analysis

Overview

This module explores the role of uncertainty and sensitivity analysis in modelling and decision-making. Participants examine how uncertainty arises from assumptions, data limitations, and model structure, and how it can influence interpretation and policy recommendations. The module emphasises the importance of transparently communicating uncertainty to support informed and responsible decision-making.

TipLearning Objectives

By the end of this module, participants should be able to:

  • Explain what types of uncertainty exist in modelling

  • Understand how sensitivity analysis is used to assess the robustness of results

  • Demonstrate how uncertainty can be communicated effectively to non-technical audiences

NoteKey Concepts

  • Parameter uncertainty

  • Structural uncertainty

  • Data limitations and variability

  • Sensitivity analysis

  • Communicating confidence and uncertainty

Delivery Plan

This module consists of…

Topic Description Duration Format
Introduction to modelling

What is modelling?

Why modelling matters in malaria programmes

Examples of modelling informing policy decisions

 30 min Presentation
Different types of models

Overview of modelling approaches

Comparing model types and applications

Strengths and limitations of different approaches

 30 min Presentation
Interactive Exercise

Participants review real-world malaria programme questions

Small groups discuss whether modelling would be useful and why

 45–60 min Small group break out exercise
Group Discussion

What makes modelling useful—or not useful—for decision-making?

Challenges participants have encountered in using evidence

 30 min Group discussion (plenary)
Reflection and Wrap-Up

Key takeaways

Common misconceptions about modelling

Where to next

 15–30 min Presentation

Part 1: Introduction to Uncertainty (30 min)

  • Why uncertainty exists in all models

  • Different sources and types of uncertainty

Part 2: Sensitivity Analysis (45 min)

  • Understanding how assumptions affect outputs

  • Examples of sensitivity testing in malaria models

Part 3: Practical Exercise (45–60 min)

  • Participants review outputs under different assumptions

  • Explore how changing parameters influences conclusions

Part 4: Communicating Uncertainty (30 min)

  • Avoiding overconfidence in communication

  • Presenting uncertainty clearly and constructively

Part 5: Reflection and Wrap-Up (15 min)

  • Key lessons for interpreting and communicating uncertainty

Practical Exercise

Exercise Title: Exploring Uncertainty in Model Results

Instructions:
 Participants:

  1. Review model outputs under varying assumptions

  2. Identify which assumptions most influence outcomes

  3. Discuss implications for policy recommendations

  4. Develop key messages for communicating uncertainty

Groups compare findings and communication approaches.

Facilitator Guidance

  • Reinforce that uncertainty does not make modelling “wrong” or unusable

  • Encourage discussion on balancing confidence and caution

  • Use practical examples to demonstrate sensitivity analysis

  • Focus on implications for decision-making and communication

Materials and Resources

  • Example sensitivity analyses

  • Uncertainty communication guide

  • Scenario comparison worksheets

  • Facilitator presentation slides