Policy influencer persona

People who shape which evidence and models are used and how they are interpreted, but who usually do not hold the final legal authority to set policy.

Who they are

International program officers/implementers at NGOs, multilaterals, and foundations who design and fund programs across countries.

Embedded Gates Foundation program teams (PST/RCO) who set investment strategy and framing for IDM work.

Strategic funders/investors who control large resource flows based on modeling and economic analysis.

Technical advisors (LMIC technical public health professionals, academic partners) when they translate model outputs into policy recommendations for governments.

While policy influencers don’t often directly use IDM models, they are a large portion of the overall global health community and their ability to translate modeling output to policy recommendations is essential to improving health outcomes.

What they do

Commission or select models, interpret outputs, compare options across countries or portfolios, and use evidence for funding decisions, guidelines, and advocacy with governments and boards.

Skills/tools

Strong analytical and interpretive capacity; comfortable with dashboards, comparative analyses, and high-level modeling summaries, often working under time and donor constraints.

Key needs

Trusted, standardized evidence products (dashboards, briefs), clear assumptions and limitations, rapid turnaround for “what-if” questions, and narratives that connect model results to programmatic and investment choices.

Decision-making context

Policy influencers sit between modelers and policy makers, translating technical evidence into actionable recommendations. Their decisions are primarily about which evidence to trust, how to frame it, and how to advocate for it effectively with governments and boards. They often compare model outputs across a portfolio of countries or programs simultaneously rather than interpreting a single result in isolation.

Timing is critical: funding cycles, board meetings, and planning processes create hard deadlines. A model result delivered after a funding decision is made has no influence regardless of quality. Trust in the modeling source matters as much as technical rigor — policy influencers rely on reputation and track record, and they need to be able to explain and defend the evidence they present to others without interrogating the underlying math.

For code: Design dashboards and evidence products around comparison and portfolio views, not just single-model outputs. Support rapid “what-if” scenario generation so influencers can respond to questions in real time during meetings. Make assumptions and limitations visible by default — not buried in documentation — since influencers need this to defend recommendations. Outputs should be exportable in formats usable in presentations and briefs without reformatting.

For docs: Focus on connecting model outputs to programmatic decisions rather than explaining model mechanics. Use plain language and frame uncertainty in terms of what it means for decisions, not in statistical terms. Where relevant, show how outputs compare across contexts (for example, same intervention in different country settings) to support portfolio-level thinking.

Targeted content

It’s important to address policy influencer needs primarily in dashboards, presentations to GF program teams, and on the idmod.org website. In the software documentation, provide at most one or two sentences on the home page or landing page for policy influencers to understand broadly how the models apply to policy questions — not a dedicated section, case study, or reframed landing page. Policy influencers are not the target audience for software documentation.