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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.

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Summary
This summary is machine-generated.

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.

Keywords:
Control rankingDecision making under riskStochastic outcomes

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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.