Modelling during an emergency: The 2009 H1N1 influenza pandemic

B. Y. Lee, L. A. Haidari, M. S. Lee

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations


During the 2009 H1N1 pandemic, decision-makers had access to mathematical and computational models that were not available in previous pandemics in 1918, 1957, and 1968. How did models contribute to policy and action during the 2009 H1N1 pandemic? Modelling encountered six primary challenges: (i) expectations of modelling were not clearly defined; (ii) appropriate real-time data were not readily available; (iii) modelling results were not generated, shared, or disseminated in time; (iv) decision-makers could not always decipher the structure and assumptions of the models; (v) modelling studies varied in intervention representations and reported results; and (vi) modelling studies did not always present the results or outcomes that are useful to decision-makers. However, there were also seven general successes: (i) modelling characterized the role of social distancing measures such as school closure; (ii) modelling helped to guide data collection; (iii) modelling helped to justify the value of the vaccination programme; (iv) modelling helped to prioritize target populations for vaccination; (v) modelling addressed the use of antiviral medications; (vi) modelling helped with health system preparedness planning; and (vii) modellers and decision-makers gained a better understanding of how to work with each other. In many ways, the 2009 pandemic served as practice and a learning opportunity for both modellers and decision-makers. Modellers can continue working with decision-makers and other stakeholders to help overcome these challenges, to be better prepared when the next emergency inevitably arrives.

Original languageEnglish (US)
Pages (from-to)1014-1022
Number of pages9
JournalClinical Microbiology and Infection
Issue number11
StatePublished - Nov 2013
Externally publishedYes


  • Emergency
  • Influenza
  • Modelling
  • Pandemic
  • Policy-making
  • Simulation

ASJC Scopus subject areas

  • Microbiology (medical)
  • Infectious Diseases


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