Skip to contents

tbl.now: Tidy Extension for Nowcasting

tbl.now-package.Rd

Provides a 'tbl_now' class for temporal data for performing nowcasts. The 'tbl_now' allows for easy manipulation of event dates, report dates, strata and covariates for implementing different nowcasting models while at the same time permitting ease of use with 'dplyr' functions.

Details

Surveillance data arrives late. A case that happened on Monday may not reach the system until Thursday, so the most recent counts always look lower than they will turn out to be. Nowcasting corrects that: it estimates how many cases have already happened but have not been reported yet.

tbl.now is the tidy scaffolding around that problem. You declare which columns hold the event date, the report date and anything else that matters once, and everything else – describing, diagnosing, plotting, fitting, scoring – follows from that declaration.

The workflow

  1. Declare. tbl_now() turns a data.frame into a tbl_now, or as_tbl_now() converts an object from another nowcasting package. The result is still a tibble, so dplyr keeps working.

  2. Describe. summary() says what is in the data; autoplot() draws it.

  3. Diagnose. diagnose() says what is wrong with it, and points at the statistical tests worth running – diagnose_drift() for delays that are getting longer, diagnose_batches() for backlog releases, diagnostic_plot() for the reporting process as a picture.

  4. Reshape. to_count(), complete_zeroes(), aggregate_time_units(), align_weeks() and censor_reports() put the data on the grid a model needs.

  5. Fit. run_nowcast() takes the data and an engine() – one interface over epinowcast, baselinenowcast, NobBS, EpiNow2, surveillance and diseasenowcasting. Write your own with nowcast_fit() and nowcast_tidy().

  6. Check. score_nowcast() and nowcast_backtest() say whether the nowcast was any good; nowcast_ensemble() combines several.

vignette("tbl.now") walks through this end to end. The package website carries longer articles on the modelling packages, batch reporting, ensembles and writing your own backend.

Datasets

Six surveillance datasets ship with the package for experimenting: denguedat, mpoxdat, flusight, covid_colombia, covid_us and hai_bucaramanga – the last deliberately messy, for the diagnostics.

See also

Useful links:

Author

Maintainer: Rodrigo Zepeda-Tello rzepeda17@gmail.com (ORCID)

Authors:

Other contributors:

  • Teresa Yamana (ORCID) [contributor]

  • Jeffrey Shaman [contributor]

  • Columbia University in the City of New York [copyright holder, funder]