Data Science x Public Health
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.
Data Science x Public Health
Latest Episodes
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 t...
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 ...
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 mode...
In Theory, External Validation Works. In Reality… It Doesn’t
External validation is often presented as the gold standard for proving that a predictive model works beyond its original dataset. It is supposed to show that the model can generalize to the real world. But what if one external dataset is still...
This Is Why Competing Risks Don’t Work (And Nobody Talks About It)
Competing risks methods are often presented as a more realistic way to analyze time-to-event data in epidemiology and public health. They promise to handle situations where other events prevent the outcome of interest from ever occurring. But w...
