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Several Modeling and Nonmodeling Decision-Support Tools Are Available to Inform Infectious Disease Prevention and Control; Policymakers and Modelers Should Understand the Capabilities and Limitations of Each and Select the Best Approach(es) Based on the Circumstances and the Questions to Be Addressed

• Theory-based models work well when there is sufficient understanding and information on the disease dynamics and when the models are sufficiently validated in the context considered.
• Theory-based models are best suited to address questions about the magnitude of the threat and where interventions are possible, as well as to compare potential interventions; they are less useful than statistical models for forecasting disease incidence.
• Statistical models can make useful inferences and predictions without requiring a full understanding of all underlying causal mechanisms. But their use comes at a price: higher sensitivity to violations of the invariance assumption and difficulty using them to make decisions informed by causal relationships.
• Statistical models are best suited for mechanistic quantitative predictions concerning the phenomenon of interest (e.g., How fast will it spread? When will it peak?).
• Nonmodeling approaches are typically more valuable for real-time decisions. They can be informed by modeling, which is best undertaken before real-time decisions are needed. Examples include expert elicitation, policy analysis frameworks, preparedness exercises, and intra-action reports.
• A variety of techniques can assist in communication and planning for appropriate use of models and nonmodeling approaches. This starts with better mutual understanding between modelers and policymakers of both the tools available and the questions that policymakers face.