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Estimate the effective reproduction number (Rt) over time by group

Source: R/analysis_functions.R
estimate_rt.Rd

A grouped wrapper around estimate_R (Cori et al. 2013). For each group, Rt is estimated in a sliding weekly window using a Bayesian framework with a Gamma-distributed serial interval. Results from all groups are combined into a single data frame.

Usage

estimate_rt(
  dat,
  grp_var,
  date_var,
  incidence_var,
  est_method = "parametric_si",
  si_mean = 6.48,
  si_std = 3.83
)

Arguments

dat

A data frame with at least a date column, an incidence column, and a grouping column. No NA values are permitted in incidence_var.

grp_var

Character string giving the name of the grouping column. Rt is estimated independently for each group.

date_var

Character string giving the name of the date column.

incidence_var

Character string giving the name of the daily incidence (case count) column.

est_method

Character string specifying the serial interval estimation method passed to estimate_R. One of "parametric_si" (default), "non_parametric_si", "uncertain_si", "si_from_data", or "si_from_sample".

si_mean

Mean of the serial interval distribution (days). Used when est_method = "parametric_si". Default is 6.48 (COVID-19; Nishiura et al. 2020).

si_std

Standard deviation of the serial interval distribution (days). Used when est_method = "parametric_si". Default is 3.83 (COVID-19; Nishiura et al. 2020).

Value

A data frame with one row per estimation window per group, containing:

date_start

Start date of the estimation window.

date_end

End date of the estimation window.

<grp_var>

Group identifier; column name matches grp_var.

r_mean

Posterior mean Rt.

r_median

Posterior median Rt.

r_q2.5

2.5th percentile of the posterior (lower 95% credible interval).

r_q97.5

97.5th percentile of the posterior (upper 95% credible interval).

Details

The default serial interval parameters (si_mean = 6.48, si_std = 3.83) are from Nishiura et al. (2020) for COVID-19 and should be updated for other pathogens.

References

Cori A, Ferguson NM, Fraser C, Cauchemez S (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9), 1505–1512. doi:10.1093/aje/kwt133

Nishiura H, Linton NM, Akhmetzhanov AR (2020). Serial interval of novel coronavirus (COVID-19) infections. International Journal of Infectious Diseases, 93, 284–286. doi:10.1016/j.ijid.2020.02.060

See also

estimate_R for full estimation control, including non-parametric serial intervals.

Examples

if (FALSE) { # \dontrun{
rt_estimates <- estimate_rt(
  dat           = china_case_data,
  grp_var       = "province",
  date_var      = "date",
  incidence_var = "cases"
)
} # }