epidemiology
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.
Best use case
epidemiology is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using epidemiology should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/epidemiology/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How epidemiology Compares
| Feature / Agent | epidemiology | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## 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
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