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.
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.