Model-dependent denominators
Public-health rates are often treated as observed facts, but AI-enabled population estimation can turn the denominator itself into a model output. The indicator then inherits the model's assumptions and uncertainty.
Public Health Practice
How epidemiological and analytical evidence is applied in real public-health practice: surveillance, program evaluation, policy translation, and the operational realities of turning a finding into a decision.
In scope
- Translating research findings into public-health action
- Program and policy evaluation
- Surveillance and routine health-data systems
Out of scope
- Individual clinical diagnosis or treatment guidance
- Jurisdiction-specific regulatory or legal advice
Published articles
AI monoculture in public health
Shared foundation models can create hidden dependence across institutions that appear to be making independent judgments. Public-health resilience may require epistemic diversity, not only better average model accuracy.
ED data are a window, not a census
Emergency-department data can reveal fast changes in health-care use and syndromic activity, but the population reaching an ED is selected by access, severity, behaviour, coding and service organization.
Vaccination coverage and denominators
Vaccination coverage looks like a simple percentage, but its numerator and denominator depend on eligibility, population estimates, mobility, recording completeness and time.
When surveillance fails the decision
A surveillance system can be technically sound and still fail public health if its information arrives at the wrong time, for the wrong population, or in a form that cannot support the decision at hand.
Surveillance quality has no single optimum
Sensitivity, specificity, predictive value, timeliness, completeness, representativeness and acceptability are not independent virtues. Improving one can worsen another or increase cost.
From notification to action
A disease notification is only the first state in a longer information chain. Validation, classification, deduplication, enrichment, aggregation and interpretation determine whether a report becomes usable epidemiological information.
Vaccination trajectories
Two populations can have the same final vaccination coverage while experiencing very different periods of susceptibility. Timeliness and catch-up require longitudinal thinking.
Understanding cause-of-death data
Mortality statistics are built through certification, ICD coding and rules for selecting an underlying cause. The final tabulation is standardized, but it is not a direct transcription of biological reality.
Design the intervention first
Public-health programmes become difficult to evaluate when indicators are chosen before the causal and implementation logic of the intervention is made explicit.
Completion is not effectiveness
Activities and outputs can show that a programme operated. They cannot by themselves show that outcomes changed because of it.
Evaluating a surveillance system
A surveillance system is useful only when it supports its stated decisions with data that are timely, sufficiently complete, interpretable, and sustainable. Evaluation is a purpose-led assessment of trade-offs, not a single score.