| name | epidemiology |
| description | Performs epidemiological analyses including disease modeling (SIR/SEIR), outbreak investigation, risk factor identification, incidence/prevalence estimation, and causal inference from observational data; trigger when users discuss disease spread, public health data, or population-level health patterns. |
When to Trigger
Activate this skill when the user mentions:
- SIR, SEIR, compartmental models, R0, reproduction number
- Outbreak investigation, contact tracing, epidemic curves
- Incidence, prevalence, mortality rates, case-fatality ratio
- Risk factors, odds ratio, relative risk, hazard ratio
- Cohort studies, case-control studies, cross-sectional surveys
- DAGs (directed acyclic graphs), causal inference, confounding
- Vaccine efficacy, herd immunity, attack rate
Step-by-Step Methodology
- Define the epidemiological question - Specify the disease/condition, population, time period, and geographic scope. Determine if descriptive, analytic, or modeling approach is needed.
- Data characterization - Identify data source (surveillance, registry, survey). Assess case definitions (confirmed, probable, suspected). Check completeness and reporting biases.
- Descriptive epidemiology - Characterize by person (age, sex, demographics), place (geographic distribution, mapping), and time (epidemic curves, secular trends, seasonality).
- Measure calculation - Compute incidence rate (person-time denominator), prevalence (point or period), attack rate, case-fatality ratio. Report with 95% confidence intervals.
- Analytic methods - For causal questions: draw a DAG to identify confounders and colliders. Use appropriate regression (logistic for OR, Poisson/negative binomial for rates, Cox for time-to-event). Apply propensity score methods if needed.
- Disease modeling - Build SIR/SEIR compartmental models. Estimate R0 from early epidemic growth rate or next-generation matrix. Conduct sensitivity analysis on key parameters (transmission rate, recovery rate, latent period).
- Interpretation and communication - Translate findings into public health actions. Present results with absolute and relative measures. Discuss Hills criteria for causation assessment.
Key Databases and Tools
- WHO Global Health Observatory - International health statistics
- CDC WONDER / MMWR - US disease surveillance data
- Our World in Data - Pandemic and health metrics
- GBD (Global Burden of Disease) - Comprehensive disease burden estimates
- EpiEstim / R0 package - R0 estimation tools
- DAGitty - DAG drawing and analysis
Output Format
- Epidemic curves with proper time axis (onset date, not report date when possible).
- Measures of association as tables: measure, point estimate, 95% CI, p-value.
- Compartmental model diagrams with parameter definitions and values.
- Geographic maps with rates (not raw counts) and appropriate denominators.
Quality Checklist
