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

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Lessler, J., Edmunds, W.J., Halloran, M.E., Hollingsworth, Deirdre and Lloyd, A.L. (2015) 'Seven challenges for model-driven data collection in experimental and observational studies'. Epidemics, Vol 10, pp. 78-82.

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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
Additional Information: This article belongs to a special issue: Challenges in Modelling Infectious Disease Dynamics
Subjects: W General Medicine. Health Professions > W 20.5 Biomedical research
WA Public Health > WA 105 Epidemiology
WA Public Health > Statistics. Surveys > WA 950 Theory or methods of medical statistics. Epidemiologic methods
Faculty: Department: Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI): https://doi.org/10.1016/j.epidem.2014.12.002
Depositing User: Lynn Roberts-Maloney
Date Deposited: 24 Apr 2015 09:12
Last Modified: 06 Feb 2018 13:09
URI: https://archive.lstmed.ac.uk/id/eprint/5108

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