How do I install the Epidemiology skill?

Run npx skillmds@latest add beita6969/epidemiology in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Epidemiology safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Epidemiology?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Epidemiology free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Epidemiology?

beita6969 (@beita6969) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Epidemiology by beita6969 — Agent Skill

Name: Epidemiology
Rating: 4.2 (5 reviews)
Author: beita6969

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.

beita6969 Updated Sep 9, 2026

File contents

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

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

---name: epidemiologydescription: 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 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

Frequently asked questions