epidemiology

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

1. Define the epidemiological question - Specify the disease/condition, population, time period, and geographic scope. Determine if descriptive, analytic, or modeling approach is needed.
2. Data characterization - Identify data source (surveillance, registry, survey). Assess case definitions (confirmed, probable, suspected). Check completeness and reporting biases.
3. Descriptive epidemiology - Characterize by person (age, sex, demographics), place (geographic distribution, mapping), and time (epidemic curves, secular trends, seasonality).
4. Measure calculation - Compute incidence rate (person-time denominator), prevalence (point or period), attack rate, case-fatality ratio. Report with 95% confidence intervals.
5. 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.
6. 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).
7. 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

• Case definition explicitly stated
• Denominators appropriate (person-time for rates, population for prevalence)
• Confidence intervals provided for all estimates
• Confounders identified via DAG and adjusted for
• Selection bias and information bias discussed
• Model assumptions stated and sensitivity analysis performed
• Absolute and relative measures both reported
• Temporal relationship between exposure and outcome verified