(Boosted) Bayesian Estimates of Effective Reproduction Number

estimate_rt() applies the approach of Cori et al (2013) to estimate a gamma-distributed reproduction number. It implements additional pre-processing to handle outliers and seasonality, and it smooths the data before computing Rt, rather than during the estimate. By default, it also corrects the portion of the smooth conditional on future data using rolling cross-validation and geometrically-weighted bootstraps based on the residuals for each future-conditional time point. Bootstrap weights are calculated using a geometric sequence with the first-order autoregressive coefficient as the discount factor.

estimate_rt(
  .data,
  .collection_date = "collection_date",
  .report_date = "report_date",
  serial_interval_mean = 6,
  serial_interval_sd = 4.17,
  start_date = "2020-03-12",
  trend = "30 days",
  period = "7 days",
  delay_period = "14 days",
  pct_reported = 0.9,
  cutoff = 0.05,
  plot_anomalies = FALSE,
  boost = TRUE
)

Arguments

.data

A data frame containing one incident observation per row

.collection_date

<tidy-select> A Date column to use as the collection date of the observed case

.report_date

<tidy-select> A Date column to use as the report date of the observed case

serial_interval_mean

The average number of days between infection of a primary case and a secondary case

serial_interval_sd

The standard deviation of the number of days between infection of a primary case and a secondary case

start_date

The start date of the epidemic; defaults to "2020-03-12", which is the beginning of the contiguous part of Shelby County's observed cases (at least one case observed per day since that date).

trend

The length of time to use in trend decomposition; can be a time-based definition (e.g. "1 month") or an integer number of days. If NULL or "auto", trend is set automatically using the tunable heuristics in the timetk package.

period

The length of time to use in seasonal decomposition; can be a time-based definition (e.g. "1 week") or an integer number of days. If NULL or "auto", period is set automatically using the tunable heuristics in the timetk package.

delay_period

The length of time to use in calculating reporting delay; can be a time-based definition (e.g. "2 weeks") or an integer number of days. If NULL, delay_period is set to "14 days".

pct_reported

The percent of total cases reported before considering a collection date to be fully observed. It is not recommended to set this to 1, as reporting delays typically contain very large outliers which will skew the results. The default is 0.9, which strikes a balance between sensitivity and robustness in Shelby County data.

cutoff

The cutoff value for anomaly detection; controls both the maximum percentage of data points that may be considered anomalies, as well as the critical value for the Generalized Extreme Studentized Deviate test used to detect the anomalies. Can be interpreted as the desired maximum probability that an individual data point is labeled an anomaly.

plot_anomalies

Should anomalies be plotted for visual inspection? If TRUE, the plot will be on the log-scale.

boost

Should the error in future-conditional estimates be estimated and accounted for?

Value

A tibble with columns .t, .pred (the median), .pred_lower(the lower bound of the 95% credible interval), .pred_upper(the upper bound of the 95% credible interval), .mean (the average), and .cv (the coefficient of variation)