Overview | Estimating R on sliding weekly windows, with a parametric serial interval | Estimating R with a non parametric serial interval distribution | Estimating R accounting for uncertainty on the serial interval distribution | Estimating R and the serial interval using data on pairs infector/infected | Changing the time windows for estimation | Accounting for missed generations of infections | Different ways of specifying the incidence | Specifying imported cases | EpiEstimApp

Input data | Step A: Serial Interval | Step B: Incidence | Estimate R~t~ | Step C: estimate_R() | Prior for R~t~ | Forecast future incidence | Step D: Projections package | Example A: Entire workflow | Example B: Estimating R~t~ using non-parametric distribution | Example C: Estimating R~t~ using infector-infected cases | Example D: Estimating R~t~ using "si_from_sample" | Example E: Estimating R~t~ using "uncertain_si" | Example F: Changing the prior for estimating R~t~ | Example G: Changing the time window to estimate R~t~ | References

SARS-CoV-2 variants

Background | Why do we need backimputation? | A simple toy scenario | Real world scenario: UK COVID deaths | No left-censoring: early R~t~ estimates remain similar | Left-censoring: back-imputation reduces bias. | Caveats | References

Estimate R~t~ from temporally aggregated incidence data | estimate_R() for aggregated data | Estimate R~t~ from weekly COVID-19 data | References

Summary table of other R packages and tools