That’s really interesting, Mo. Appreciate you sharing! The Cholesky approach definitely makes sense conceptually.

From a practitioner perspective, the correlations tend to come from fairly intuitive system dynamics rather than anything formal. So in Northern Nigeria, when outreach improved, you would often see several things move together. Coverage would go up, dropout rates would fall, and supply chains would stabilize as demand became more predictable. The opposite would happen when systems were under strain. Staffing gaps, stockouts, and lower uptake would start reinforcing each other quite quickly.

The tricky part is that those shifts are often uneven and very context specific. Translating them into a stable covariance structure is not straightforward. But I agree there’s probably a useful bridge here between how these dynamics play out operationally and how they could be reflected in models.

That makes sense, and I think the tool does a great job of making those tradeoffs legible.

One thing I’ve found is that spending time in these settings can change how you think about some of the parameters, especially around counterfactuals and how multiple constraints interact in practice. Certain assumptions that look independent in a model often move together on the ground.

It would be interesting to see how that kind of correlated variation could be explored more systematically over time.

Wow Max, this is super impressive. I really appreciate how clearly you surfaced the assumptions and made the sensitivity analysis explorable. Making the counterfactuals explicit is enormously valuable.

Having worked in northern Nigeria, one thing that stood out to me is how dynamic those counterfactuals can be in practice. For example, in Sokoto and Zamfara, DHS coverage numbers capture the endpoint, but underneath that you have shifting factors like outreach consistency, staffing, supply reliability, and community trust. I have seen system performance change meaningfully over relatively short periods in ways that would materially affect those parameters.

It also made me think about places like Kenya and Mozambique that are highlighted as “best” countries in your table. Even within the same country, conditions can vary enormously across regions and over time depending on implementation strength and system capacity. Those differences do not always show up immediately in the underlying data, but they can have real implications for how stable those cost effectiveness estimates are.

Curious how you think about parameter stability over time in settings where the system itself is evolving. The model makes the tradeoffs legible, but the inputs themselves can be moving targets.

Really thoughtful contribution.