Reproduction number
Estimate how fast an epidemic is changing from the episode incidence curve — the instantaneous reproduction number (Rt) with the Cori renewal method and a log-linear epidemic-growth rate. These are retrospective research estimates with assumptions surfaced, never real-time surveillance or nowcasting.
Research question
Is transmission intensity rising or falling over this cohort's timeline, and how fast is the epidemic growing?
Who this is for
- Public-health research teams characterizing retrospective epidemic dynamics
- Hospital epidemiology researchers summarizing an outbreak's trajectory
- Analysts who need Rt reported with its serial-interval assumption and credible intervals
Data requirements
| Data | Required | Purpose |
|---|---|---|
| Infection episodes with index (or onset) dates | Yes | Daily incidence curve |
| Serial-interval estimate (mean, SD) | Yes | Renewal-equation infectiousness weights |
Workflow
Model dated episodes → Reproduction number
→ Set serial interval + window → Review Rt, CrIs, growth
→ AI interpretation
Step 1 — Build the incidence curve
Episodes are binned by index (or onset) date into a daily incidence curve across the observed period.
Step 2 — Estimate Rt and growth
The Reproduction number analysis computes the Cori et al. (2013) instantaneous Rt over sliding windows using a caller-supplied gamma serial interval, with gamma credible intervals, plus a log-linear epidemic-growth rate and doubling/halving time. The serial interval is cited in each run's provenance.
Step 3 — Read the assumptions
Rt is sensitive to the assumed serial interval and to small daily counts; caveats state this, and the result is framed as retrospective research — not real-time surveillance or operational decision support.
Expected outputs
- Epidemic curve and per-window Rt with 95% credible intervals
- Epidemic-growth rate with doubling/halving time
- Method citation and assumptions preserved in provenance