Seven challenges for model-driven data collection in experimental and observational studies
J Lessler1, W J Edmunds2, M E Halloran3
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21224, USA.
Epidemics
|April 7, 2015
Summary
Infectious disease models can guide data collection for more efficient hypothesis testing and robust study designs. Integrating dynamic modeling with empirical data collection is crucial for advancing infectious disease research and preparedness.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health
Background:
- Infectious disease models serve as hypotheses and tools for understanding disease dynamics.
- Models have the potential to guide data collection in experimental and observational studies.
- Synergies between modeling and data collection are not yet the norm in infectious disease research.
Purpose of the Study:
- To highlight the potential of infectious disease models in guiding data collection.
- To emphasize the need for closer integration of dynamic modeling and empirical data.
- To underscore the benefits of overcoming challenges in model-informed data collection.
Main Methods:
- Review of existing literature and case studies (e.g., Garki project, H1N1 response, T-cell immunodynamics).
- Conceptual framework emphasizing the role of models in study design.
- Discussion of challenges and opportunities for integrating modeling with data collection.
Main Results:
- Infectious disease models can lead to more efficient hypothesis testing and robust study designs.
- Historical examples demonstrate the successful application of models in informing data collection.
- A significant gap exists between the potential of models and their current integration into research practices.
Conclusions:
- Integrating dynamic modeling with empirical data collection is essential for accelerating innovation in infectious disease research.
- Overcoming current challenges can significantly improve the response to infectious disease threats.
- Closer collaboration between modelers and empirical researchers is needed to realize the full potential of infectious disease modeling.
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