Hosted by BJANALYTICS · 🇺🇸 US · EN-US · 166 episodes
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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.
BJANALYTICS hosts Data Science x Public Health, a education show with 166 episodes published.
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 model
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
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 wh
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 e
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 fr
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
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 wh
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
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 i
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 co
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
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 bottl
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
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
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
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
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 th
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
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 actual
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 t
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Data Science x Public Health is hosted by BJANALYTICS. The show is categorised under education (courses) and has published 166 episodes.
Data Science x Public Health has published 166 episodes.
Data Science x Public Health regularly covers education, courses, how to. It sits in the education category, with a courses focus.
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