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

• Main Authors: J. Lessler, W.J. Edmunds, M.E. Halloran, T.D. Hollingsworth, A.L. Lloyd
• Format: Article
• Language: English
• Published: Elsevier 2015-03-01
• Series: Epidemics
• Subjects: Modeling; Data collection; Observational studies; Experimental studies
• Online Access: http://www.sciencedirect.com/science/article/pii/S1755436514000711

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.