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Interpretation of large-scale stochastic epidemic models

Explore stochastic epidemic models, their structures, uncertainty sources, ensemble outputs, and the impact of reporting choices. Gain insights into epidemic clustering, interpretation challenges, and effective visualizations for model outputs.

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Interpretation of large-scale stochastic epidemic models

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  1. Interpretation of large-scale stochasticepidemic models Iain Barrass Ian Hall and Steve Leach Health Protection Agency 14 September 2011

  2. Overview • Stochastic model structure • Source of uncertainty • Ensemble output • Epidemic clustering • Consequences of reporting choice • Interpretation and visualization

  3. Stochastic model structure Infection S I R Stochastic transition or event-driven simulation

  4. Spatial meta-population model Without interventions, R0~1.6

  5. Pneumonic plague: model Early symptomatic Susceptible Latent Removed Late symptomatic • Contact tracing • Post-exposure prophylaxis • Isolation • Generic antimicrobial treatment • Specific antimicrobial treatment

  6. Seeding: aerosol release • Variability in • release location (including height) • wind direction • infected individuals within patches

  7. Seeding: disease importation Decoupled global and UK models – global model acts as a seed for the UK model. Variability in importation profile and importee destination.

  8. Deaths from “large” release with intervention strategies Earlier commencement of prophylaxis reduces death count Clearly interpretable Pneumonic plague: results

  9. “Pandemic influenza” spatial spread • Initial seed of 10 cases in resident population of one patch

  10. Solution measures • Final attack size (whole population or typed)‏ • New cases over time • Individuals over time in a state • Duration of “high activity” • Peak of the attack • Consideration of morbidity and mortality (economic cost)‏

  11. Clustered epidemic curves 50% of epidemics fall within three clusters

  12. Model selection Model A Model B

  13. Single wave epidemic

  14. Visualization systems

  15. Summary • High complexity models (or large populations) lead to event-based simulation with large ensembles • Increasing model structure can increase observation variability • Consideration of seeding variability and parameter sensitivity complicates interpretation • Some measures are not very sensitive to model complexity • Choice of measure may influence model choice through desire for clarity of interpretation • Highly complex models benefit from specialised visualization approaches

  16. Acknowledgments • MRA team – in particular Joe Egan and Tim Cairnes • Funding: Department of Health (England), Home Office, EU FP7 project FLUMODCONT, EPSRC network CompuSteer

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