This podcast discusses the concepts of data science and public health, and then delves into their intersection, exploring the connection between the two fields in greater detail.
This Is Why Resource Allocation Models Don’t Work (And Nobody Talks About It)
Resource allocation models are supposed to help public health systems distribute scarce resources more intelligently. They promise better targeting, more efficient deployment, and stronger impact under constraint. But what if the model is optimizing inside a system whose deepest constraints should never have been treated as fixed? In this episode, we break down why resource allocation models often fail in practice, how optimization can normalize structural scarcity, and why better public he...
13 May 2026
Everyone Uses Censoring Assumptions… But They Fail When Leaving the Study Is Part of the Outcome
Censoring is one of the most common assumptions in epidemiology and survival analysis. It is often treated as a routine technical step for handling people who leave observation before the study ends. But what if leaving the study is not random noise—and is actually part of the outcome process itself? In this episode, we break down why censoring assumptions often fail, how loss to follow-up can distort longitudinal research, and why disappearing from the dataset is not the same thing as ...
13 May 2026
In Theory, Model Averaging Works. In Reality… It Doesn’t
Model averaging is often presented as a more careful and uncertainty-aware alternative to choosing one model specification. It is supposed to reduce overconfidence and make analysis more robust. But what if all the models being averaged share the same blind spots from the start? In this episode, we break down why model averaging often overpromises, how shared structural weaknesses survive the averaging process, and why uncertainty cannot be handled simply by blending similar models.&nbs...
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