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.
Episodes
166 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...
In Theory, Real-Time Health Alerts Work. In Reality… They Don’t
Real-time health alerts are supposed to detect danger faster and trigger earlier intervention.They promise speed, precision, and smarter public health response.But what if the alert is fast and the system behind it is still slow?...
This Is Why Adjustment for Baseline Differences Doesn’t Work (And Nobody Talks About It)
Adjustment for baseline differences is one of the most common moves in health research and biostatistics. It is often treated as proof that two groups have been made more comparable and that bias has been reduced. But what if that adjustment is...
Everyone Uses Attack Rates… But They Fail When Exposure Isn’t Shared
Attack rates are one of the most common tools in outbreak epidemiology. They seem to offer a quick answer to a simple question: how many exposed people got sick? But what if the exposed group was never truly sharing the same exposure in the fir...
Everyone Uses Public Health Scorecards… But They Fail When the Incentive Is the Metric
Public health scorecards are supposed to improve accountability and make system performance easier to track.They promise clarity, targets, and faster decision-making.But what if the scorecard starts changing behavior in the wrong di...
Everyone Uses Sensitivity Analyses… But They Fail When the Assumption Space Is Too Small
Sensitivity analyses are often presented as proof that a result is robust and trustworthy. They are supposed to show that findings hold up even when assumptions are changed. But what if the analysis only tested a tiny corner of the uncertainty ...
You’ve Been Using Secondary Attack Rates Wrong — Here’s What Actually Happens
Secondary attack rates are often used to estimate how infection spreads among close contacts. They seem to provide a focused measure of transmission in households, schools, workplaces, and other settings. But what if the number is being shaped ...
Everyone Uses AI Triage Tools… But They Fail When the Health System Is the Real Problem
AI triage tools are designed to identify high-risk patients and communities faster.They promise smarter prioritization, earlier intervention, and more efficient care delivery.But what if the model is not the real bottleneck at all?<...
You’ve Been Using Statistical Power Wrong — Here’s What Actually Happens
Statistical power is one of the most familiar concepts in biostatistics and research design. It is supposed to help determine whether a study can detect a meaningful effect. But what if power is being used the wrong way after the study is alrea...
You’ve Been Using Prevalence Wrong — Here’s What Actually Happens
Prevalence is one of the most commonly used measures in epidemiology. It is often treated as a direct indicator of disease risk, spread, or public health urgency. But what if prevalence is telling a very different story than most people think?&...
This Is Why Health Equity Dashboards Don’t Work (And Nobody Talks About It)
Health equity dashboards are supposed to make disparities visible and drive better public health decisions.They promise transparency, accountability, and measurable progress.But what if the dashboard is making inequity easier to dis...
New Schedule Update: Introducing Triple-Drop Wednesdays
We’re making a small but important update to the podcast schedule.To focus on delivering higher-quality, more in-depth content, we’re moving to a new release format: Triple-Drop Wednesdays.Starting next week, all three weekly ep...
In Theory, Statistical Significance Works. In Reality… It Doesn’t
Statistical significance is one of the most familiar ideas in research. It is often treated as the dividing line between real evidence and random noise. But what if that binary framing is doing more harm than good?In this episode, we bre...
This Is Why Outbreak Curves Don’t Work (And Nobody Talks About It)
Outbreak curves are one of the most recognizable tools in epidemiology. They appear to show whether an epidemic is rising, peaking, or falling in real time. But what if the curve is reflecting reporting behavior as much as disease transmission?...
You’ve Been Using Predictive Models Wrong — Here’s What Actually Happens
Predictive models are widely used to identify high-risk patients and populations.They promise earlier intervention, better resource allocation, and improved outcomes.But what if prediction alone is not enough to actually change what...
Everyone Uses Subgroup Analysis… But It Fails When the Study Was Never Built for It
Subgroup analysis is one of the most persuasive tools in biostatistics and clinical research. It promises to show who benefits most, who responds differently, and where average effects break apart. But what if the study was never designed to an...
Everyone Uses Case Fatality Rates… But They Fail When Detection Is Unequal
Case fatality rate is one of the most commonly cited numbers during outbreaks and health emergencies. It seems to offer a direct answer to a simple question: how deadly is this disease? But what if the rate is being shaped less by biology and m...
Everyone Uses Health Risk Maps… But They Fail When the Data Is Delayed
Health risk maps are one of the most persuasive tools in public health.They make danger visible, focus attention, and seem to show exactly where action is needed most.But what if the map is already out of date by the time anyone use...
This Is Why Standard Errors Don’t Work (And Nobody Talks About It)
Standard errors are one of the most overlooked pieces of statistical output. They sit underneath confidence intervals, p-values, and claims about precision in almost every study. But what if those standard errors are wrong from the start? ...
Everyone Uses Incidence Rates… But They Fail When Time at Risk Is Wrong
Incidence rates are one of the most common measures in epidemiology. They are used to describe how quickly disease is appearing in a population and to compare risk across groups. But what if the rate looks correct while the underlying time at r...
In Theory, Benchmark Accuracy Works. In Reality… It Doesn’t
Benchmark accuracy is one of the most trusted signals in machine learning. It tells you which model performs best—and it often drives decisions about what gets deployed. But what if that number is giving you a false sense of confidence?
