Critical Review of Modelling Strategies against Pandemics

This project was runner-up for the "Technical Research" prize on our Pandemics (May 2024) course. The text below is an excerpt from the final project.

• Overview: A Theory of Change (ToC) is a roadmap that outlines how a specific intervention is expected to lead to desired outcomes, mapping out the intermediary causal relationships. This review critically examines the theories of change of various interventions that use modelling tools — mathematical or computational frameworks for understanding disease dynamics — to prevent, contain or mitigate catastrophic pandemic scenarios.
• Methodology: interventions and ToCs were collected and synthesised from various biosecurity sources, then assessed based on decision relevance to catastrophic pandemics (including robustness, relevance, pathogen-agnosticism and empirical evidence), tractability, neglectedness, and dual-use risks.
• Most Promising Modelling Interventions:
  • Assessing pathogen properties: Crucial for early pandemic response by rapidly estimating key properties like transmissibility and virulence.
  • Estimating the pandemic potential: Essential for determining a pathogen's potential to cause a pandemic and credibly raising the alarm when necessary.
• Promising Modelling Interventions:
  • Understanding factors of pathogen (re-)emergence: Provides insights for prevention but may have limited relevance for worst-case scenarios; some dual-use risks exist.
  • Simulating realistic preparedness scenarios: Useful for stress-testing response strategies to diverse pandemic scenarios, including catastrophic ones.
  • Detecting (stealth) outbreaks early: Important for triggering mitigation measures, especially in stealth pandemics, though likely too delayed for containment
  • Forecasting joint health and economic impacts of policies: Aids decision-making by balancing pandemic response strategies in real-time, less relevant in extreme scenarios.
• Less Promising Modelling Intervention:
  • Assessing countermeasure effectiveness in real-time: Useful for long-term adjustment of responses, less critical in the early stages of catastrophic pandemics.
• Key recommendations:
  • Prioritise preparedness and early response models
  • Focus on catastrophic scenarios: Ensure models are robust enough to handle scenarios like "wildfire" (very rapid disruption) or "stealth" (initially asymptomatic spread) pandemics.
  • Invest in model development between pandemics: Prepare analytical pipelines ready for immediate use in various scenarios.
  • Enhance modeller-policymaker collaboration: Establish dedicated teams to bridge the gap between modelling efforts and policy application.
• Key challenges, open questions and areas to investigate further are detailed in the conclusion.

Full project

You can view the full project here.