Lessler, Justin, Edmunds, W. John , Halloran, M. E., Hollingsworth, T. Déirdre and Lloyd, Alun L. (2015) Seven challenges for model-driven data collection in experimental and observational studies. Epidemics, Volume 10 . pp. 78-82. 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:	Journal Article
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH):	Communicable diseases -- Prevention
Journal or Publication Title:	Epidemics
Publisher:	Elsevier BV
ISSN:	1755-4365
Official Date:	March 2015
Dates:	Date Event March 2015 Published 16 December 2014 Available 8 December 2014 Accepted 1 March 2014 Submitted
Date of first compliant deposit:	28 December 2015
Volume:	Volume 10
Page Range:	pp. 78-82
DOI:	10.1016/j.epidem.2014.12.002
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	National Institute of Allergy and Infectious Diseases (U.S.) (NIAID), National Institutes of Health (U.S.) (NIH), Wellcome Trust (London, England), United States. Department of Homeland Security. Science and Technology Directorate, Fogarty International Center, National Science Foundation (U.S.) (NSF)
Grant number:	K22-AI092150-01 (NIAID), U54-GM111274 (NIH), R37-AI032042 (NIH), 097830/Z/11/A-C (WT), P01-AI098670 (NIH), R01-AI091980 (NIH), RTG/DMS-1246991 (NSF)

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