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Model-Informed Risk Assessment and Decision Making for an Emerging Infectious Disease in the Asia-Pacific Region.

Robert Moss1, Roslyn I Hickson2, Jodie McVernon1,3

  • 1Centre for Epidemiology and Biostatistics, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia.

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Summary

Mathematical modeling shows that early detection of emerging infectious diseases (EID) in the Asia-Pacific is more effective than post-outbreak support. Proactive interventions significantly reduce the risk of large-scale outbreaks.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Emerging infectious disease (EID) threats require robust healthcare systems for early detection and containment.
  • The Asia-Pacific region faces elevated EID risks, necessitating international support for healthcare systems.
  • Limited data on populations, healthcare, and disease characteristics hinders effective risk assessment and support decisions in the region.

Purpose of the Study:

  • To develop a mathematical modeling framework to assess EID outbreak risks in the Asia-Pacific.
  • To integrate diverse data sources and explore uncertainties in risk assessment.
  • To evaluate intervention strategies for EID preparedness and response.

Main Methods:

  • Developed a mathematical modeling framework to integrate available data.
  • Systematically explored the impact of uncertainty on outbreak risk.
  • Simulated intervention scenarios, including a hypothetical Ebola Virus Disease importation.

Main Results:

  • Preemptive interventions for early case detection substantially reduce the probability of large outbreaks.
  • Early detection strategies are more effective than post-outbreak healthcare capacity building.
  • The framework quantifies the impact of resource constraints on healthcare system response capabilities.

Conclusions:

  • Mathematical models can assess healthcare system limitations in responding to EID outbreaks under uncertainty.
  • The framework aids in identifying resourcing priorities for healthcare system support.
  • Enables principled and quantifiable decision-making for EID preparedness in resource-limited settings.