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’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.