Decision support for containing pandemic propagation

Hina Arora, Raghu Santanam, Ajay Vinze

Research output: Contribution to journalReview article

Abstract

This research addresses complexities inherent in dynamic decision making settings represented by global disasters such as influenza pandemics. By coupling a theoretically grounded Equation-Based Modeling (EBM) approach with more practically nuanced Agent-Based Modeling (ABM) approach we address the inherent heterogeneity of the "influenza pandemic" decision space more effectively. In addition to modeling contributions, results and findings of this study have three important policy implications for pandemic containment; first, an effective way of checking the progression of a pandemic is a multipronged approach that includes a combination of pharmaceutical and non-pharmaceutical interventions. Second, mutual aid is effective only when regions that have been affected by the pandemic are sufficiently isolated from other regions through non-pharmaceutical interventions. When regions are not sufficiently isolated, mutual aid can in fact be detrimental. Finally, intraregion non-pharmaceutical interventions such as school closures are more effective than interregion nonpharmaceutical interventions such as border closures.

Original languageEnglish (US)
Article number23
JournalACM Transactions on Management Information Systems
Volume2
Issue number4
DOIs
StatePublished - Dec 1 2011

Keywords

  • Dynamic decision making
  • Multiagent simulation
  • Pandemics
  • Public health
  • Resource allocation

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science(all)

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