Journal
EPIDEMICS
Volume 10, Issue -, Pages 78-82Publisher
ELSEVIER
DOI: 10.1016/j.epidem.2014.12.002
Keywords
Modeling; Data collection; Observational studies; Experimental studies
Categories
Funding
- Newton Institute
- National Institute of Allergy and Infectious Disease [K22-AI092150-01]
- National Institutes of Health [U54-GM111274, R37-AI032042, P01-AI098670, R01-AI091980]
- Wellcome Trust Institutional Strategic Support Award [097830/Z/11/A-C]
- Research and Policy for Infectious Disease Dynamics (RAPIDD) program of the Science and Technology Directory, Department of Homeland Security
- Fogarty International Center, National Institutes of Health
- National Science Foundation [RTG/DMS-1246991]
- Wellcome Trust [097830/Z/11/A] Funding Source: Wellcome Trust
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1246991] Funding Source: National Science Foundation
Ask authors/readers for more resources
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. (C) 2014 The Authors. Published by Elsevier B.V.
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