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Communicating uncertainty in epidemic models.

Ruth McCabe1, Mara D Kont2, Nora Schmit2

  • 1Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, UK; NIHR Health Protection Research Unit in Emerging and Zoonotic Diseases, The Ronald Ross Building, University of Liverpool, 8 West Derby Street, Liverpool L69 7BE, UK; MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, UK.

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Summary
This summary is machine-generated.

Communicating uncertainty in mathematical models of disease transmission is crucial for public health. This study reviews traditional and novel visualization methods for epidemic model uncertainty, proposing clearer presentation strategies.

Keywords:
COVID-19Communicating uncertaintyData visualisationDecision-makingTransmission modelling

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Mathematical models are vital tools for informing public health policy on disease transmission.
  • Effective communication of uncertainty in model outputs is often lacking, hindering decision-making.
  • The COVID-19 pandemic highlighted the need for improved uncertainty visualization in epidemic modeling.

Purpose of the Study:

  • To outline sources of uncertainty in epidemic models.
  • To review traditional and alternative methods for illustrating model uncertainty.
  • To propose improved, clear, and simple methods for visualizing uncertainty in disease transmission models.

Main Methods:

  • Literature review of uncertainty visualization techniques.
  • Analysis of presentation formats used during the COVID-19 pandemic.
  • Case study of recent modeling experience to inform new visualization approaches.

Main Results:

  • Identified various sources contributing to uncertainty in epidemic models.
  • Evaluated traditional uncertainty presentation methods and their limitations.
  • Documented diverse visualization strategies employed by modeling groups during COVID-19.
  • Developed a novel, simplified approach for presenting model uncertainty.

Conclusions:

  • Traditional methods for visualizing epidemic model uncertainty are often insufficient.
  • Alternative and novel visualization techniques can enhance clarity and understanding.
  • Improved communication of uncertainty is essential for effective public health decision-making informed by mathematical models.