Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis

This study reports on the application of a Portfolio Decision Analysis (PDA) to support investment decisions non-profit funder vaccine technology platform development for rapid response emerging infections. A value framework was constructed via document reviews and stakeholder consultations. Probability Success (PoS) data obtained 16 projects through expert assessments portfolio preferences Discrete Choice Experiment (DCE). The structure uncertainties in project PoS suggested non-linear, stochastic maximization problem. simulation-optimization algorithm employed, identifying optimal portfolios under different budget constraints. Stochastic dominance optimization solution tested mean-variance mean-Gini statistics, its robustness rank probability analysis Monte Carlo simulation. Project estimates were low substantially overlapping. DCE identified decreasing rates return investing single types. Optimal solutions reflected this non-linearity along an efficiency frontier diverged from model simply ranking by PoS-to-Cost, despite significant revisions during review process relation conduct DCE. Large confidence intervals associated with uncertainty valuations. Mean-variance Mean-Gini tests higher expected values also accompanied risks not achieving those decision maker’s constraint. highest ranked simulation; though having only 54% being preferred second-ranked portfolio. illustrates how modelling can help health R&D makers identify face involving trade-offs. However, light such extreme uncertainty, further due diligence ongoing updating performance is needed highly risky as well makers’ risk attitude before PDA conclude about robust solutions.