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Ranking disease control strategies with stochastic outcomes
L J Verteramo Chiu1, L W Tauer2, Y T Gröhn1
1Department of Population Medicine and Diagnostic Sciences, Cornell University College of Veterinary Medicine, Ithaca, NY, 14850, USA.
Preventive Veterinary Medicine
|February 5, 2020
Summary
This study introduces stochastic dominance and expected utility methods for epidemiologists to rank and select disease control strategies with uncertain outcomes. These techniques aid decision-making in public health and veterinary epidemiology.
Area of Science:
- Epidemiology
- Decision Analysis
- Risk Management
Background:
- Choosing effective disease control strategies involves managing stochastic outcomes.
- Evaluating strategies requires robust analytical frameworks to rank potential benefits.
- Epidemiological decision-making often necessitates incorporating risk preferences.
Purpose of the Study:
- To demonstrate the application of stochastic dominance and expected utility theory in epidemiological control strategy selection.
- To provide a practical guide for ranking control strategies based on their benefit distributions.
- To illustrate how decision-maker risk preferences influence strategy selection when outcomes are not clearly dominant.
Main Methods:
- Utilizing first and second-order stochastic dominance to rank control strategies.
- Applying expected utility theory with specific risk preferences for strategy selection.
- Developing a step-by-step guide for applying these methodologies.
- Illustrating techniques with a case study of Mycobacterium avium subs. paratuberculosis (MAP) and mastitis control in dairy herds.
Main Results:
- Stochastic dominance provides a method to rank control strategies based on their distribution of benefits.
- Expected utility analysis, incorporating risk preferences, allows for selection between non-dominating strategies.
- The ranking and selection of control strategies are demonstrably affected by the decision-maker's risk aversion.
- The case study highlights practical application in veterinary epidemiology for endemic diseases.
Conclusions:
- Stochastic dominance and expected utility are valuable tools for optimizing epidemiological control strategies.
- Incorporating risk preferences enhances the selection process for uncertain health outcomes.
- These methods offer a quantitative approach to evidence-based decision-making in disease management.
- The framework is applicable to various epidemiological scenarios requiring strategic choices under uncertainty.
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