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


Lessler, J; Edmunds, WJ; Halloran, ME; Hollingsworth, TD; Lloyd, AL; (2014) Seven challenges for model-driven data collection in experimental and observational studies. Epidemics, 10. pp. 78-82. ISSN 1755-4365 DOI: 10.1016/j.epidem.2014.12.002

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Abstract

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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Research Centre: Centre for the Mathematical Modelling of Infectious Diseases
PubMed ID: 25843389
Web of Science ID: 352226900018
URI: http://researchonline.lshtm.ac.uk/id/eprint/2145753

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