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Anticipating future learning affects current control decisions: A comparison between passive and active adaptive
Benjamin D Atkins1, Chris P Jewell2, Michael C Runge3
1Mathematics for Real-World Systems Centre for Doctoral Training, Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom.
Journal of Theoretical Biology
|July 23, 2020
Summary
Active adaptive management (AM) using real-time epidemic data improves policy decisions. This approach, unlike passive or non-adaptive methods, actively resolves uncertainty for better epidemic control outcomes.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health Policy
Background:
- Infectious disease epidemics pose challenges for policymakers due to time constraints and uncertainty.
- Mathematical models aid in predicting intervention outcomes but lack early-stage accuracy.
- Ad hoc incorporation of new data can lead to suboptimal epidemic management.
Purpose of the Study:
- To compare non-adaptive, passive adaptive management (AM), and active AM approaches for epidemic control.
- To demonstrate how formally incorporating uncertainty resolution impacts management outcomes.
- To highlight the benefits of active AM in providing dynamic, long-term projections for policymakers.
Main Methods:
- Comparison of three theoretical epidemic management strategies: non-adaptive, passive AM, and active AM.
- Active AM explicitly incorporates future uncertainty resolution via real-time data gathering.
- Evaluation of how different data incorporation methods influence policy decisions.
Main Results:
- Trial-and-error management without formal uncertainty consideration leads to suboptimal outcomes.
- Active AM provides a structured framework for objective specification, modeling, and iterative learning.
- The method of incorporating new data significantly impacts initial policy decisions.
Conclusions:
- Active adaptive management, which integrates real-time data and uncertainty resolution, yields superior epidemic control.
- Structured frameworks like active AM enable dynamic, long-term projections for effective policy.
- Early policy decisions are better informed by active AM, leading to more desirable epidemic outcomes.
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