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Mainstreaming modeling and simulation to accelerate public health innovation
Paul P Maglio1, Martin-J Sepulveda, Patricia L Mabry
1Paul P. Maglio is with the School of Engineering, University of California, Merced, and IBM Research, Almaden, CA. Martin-J. Sepulveda is with Health Systems and Policy Research, IBM Research, Yorktown, NY. At the time of the study, Patricia L. Mabry was with the Office of Behavioral and Social Sciences Research, National Institutes of Health, Bethesda, MD and is now with the Office of Disease Prevention, National Institutes of Health, Rockville, MD. Patricia L. Mabry is also a guest editor for this theme issue.
Dynamic modeling and simulation are valuable systems science tools for population health, but face adoption barriers. Overcoming these requires better data integration, training, collaboration incentives, and increased funding for systems science in health.
Area of Science:
- Systems Science
- Population Health
- Computational Modeling
Background:
- Dynamic modeling and simulation are powerful systems science tools for analyzing complex interactions over time.
- Despite their potential, these tools are underutilized in population health planning and policymaking.
Purpose of the Study:
- To identify barriers hindering the mainstream adoption of dynamic modeling and simulation in population health.
- To propose solutions for integrating these systems science approaches into health policy.
Main Methods:
- Analysis of impediments to adopting dynamic modeling and simulation in population health.
- Identification of potential remedies and strategies for enhanced utilization.
Main Results:
- Key barriers include the prevalence of traditional statistical methods, challenges in multidisciplinary collaboration, and inadequate communication of the value of systems science tools.
- Low funding for population health systems science further restricts adoption.
Conclusions:
- Addressing these barriers requires aggregating diverse datasets, implementing systems science training for health professionals, fostering collaborative research incentives, and increasing financial support for population health systems science initiatives.
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