Seven challenges for model-driven data collection in experimental and observational studies

J. Lessler, W. J. Edmunds, M. E. Halloran, T. D. Hollingsworth, A. L. Lloyd

Research output: Contribution to journalArticlepeer-review


Infectious disease models are both concise statements of hypotheses and powerful techniques for creating tools from hypotheses and theories. As such, they have tremendous potential for guiding data collection in experimental and observational studies, leading to more efficient testing of hypotheses and more robust study designs. In numerous instances, infectious disease models have played a key role in informing data collection, including the Garki project studying malaria, the response to the 2009 pandemic of H1N1 influenza in the United Kingdom and studies of T-cell immunodynamics in mammals. However, such synergies remain the exception rather than the rule; and a close marriage of dynamic modeling and empirical data collection is far from the norm in infectious disease research. Overcoming the challenges to using models to inform data collection has the potential to accelerate innovation and to improve practice in how we deal with infectious disease threats.

Original languageEnglish (US)
Pages (from-to)78-82
Number of pages5
StatePublished - Mar 2015


  • Data collection
  • Experimental studies
  • Modeling
  • Observational studies

ASJC Scopus subject areas

  • Epidemiology
  • Parasitology
  • Microbiology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases
  • Virology


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