Epidemiology
- 32 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
epidemiology is a Claude skill that performs epidemiological analysis, including compartmental disease modeling, outbreak investigation, and causal inference from health data.
About
This skill performs epidemiological analysis, including SIR/SEIR disease modeling, outbreak investigation, risk-factor identification, and incidence/prevalence estimation. A developer or researcher uses it when studying disease spread or population-level health patterns. It applies DAG-based confounder control and appropriate regression models with confidence intervals.
- Builds SIR/SEIR compartmental models and estimates R0
- Computes incidence, prevalence, attack rate, and case-fatality ratio
- Uses DAGs to identify confounders for causal inference
Epidemiology by the numbers
- 32 all-time installs (skills.sh)
- Ranked #1,095 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
epidemiology capabilities & compatibility
Free; uses open public-health datasets and R tooling.
- Capabilities
- exploratory data analysis · economics analysis
- Use cases
- data analysis · research
- Pricing
- Free
What epidemiology says it does
Build SIR/SEIR compartmental models. Estimate R0 from early epidemic growth rate or next-generation matrix.
For causal questions: draw a DAG to identify confounders and colliders.
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| Installs | 32 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Use it to run epidemiological analysis: disease modeling, outbreak investigation, and risk-factor identification from health data.
Who is it for?
Modeling disease spread and computing epidemiological measures from surveillance or study data.
Skip if: Clinical treatment decisions or non-population-level health tasks.
When should I use this skill?
The user discusses disease spread, public-health data, or population-level health patterns.
What you get
Produces epidemiological measures and disease-model estimates with confidence intervals and bias assessment.
- Epidemic curves
- Measures of association tables
- Compartmental model diagrams
By the numbers
- 7-step methodology
- 8-item quality checklist
- 6 databases/tools listed
Files
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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What disease models does it build?
SIR and SEIR compartmental models, estimating R0 from early growth rate or the next-generation matrix.
How does it handle confounding?
It draws a DAG to identify confounders and colliders, then adjusts with appropriate regression models.