nowcastingR {EpiLPS}

Nowcasting the reproduction number

Description

This routine can be used to nowcast the time-varying reproduction number. Daily cases are typically subject to reporting delays, so that the reported number of infected individuals is not always reflecting the true epidemic status. Nowcasting aims to correct this underreporting phenomenon by estimating the number of infections that have occurred but that have not yet been reported. The latter number is then combined with the already reported cases and interpreted as a nowcast or prediction for the true epidemic status regarding the number of daily cases. The routine is anchored around Laplacian-P-splines in an epidemic context (Gressani et al. 2022) and the detailed methodology can be found in Sumalinab et al. (2023). Two different models can be fitted, named M3 (the default) and M2. M3 uses a joint approach that simultaneously models the delay dimension and the time-varying reproduction number. M2 uses reported cases and a nowcast of the not yet reported cases. See Sumalinab et al. (2023) and the vignette https://epilps.com/NowcastingRt.html for more details.

Usage

nowcastingR(data, day.effect = TRUE, ref.day = "Monday", si, method = c("M3", "M2"))

Arguments

data

A data frame containing the data for each time and delay combination with the following 6 columns. The first column is a numeric variable associated to the calendar date. The second column is a numeric variable indicating the delay of reporting. The third column corresponds to the calendar date of the event (e.g. death) and the fourth column to the calendar date at which the event of interest was reported. The fifth column indicates the number of cases for each time and delay combination. Finally, the sixth column indicates whether the cases are already reported or not yet reported. To see an example of such a data structure type data("cov19incidence2022") and then head(cov19incidence2022). This will illustrate the required data structure for nowcasting the reproduction number based on incidence data for Belgium in 2022.

day.effect

If TRUE (default), include the day of the week effect.

ref.day

If day.effect = TRUE, then the reference category for the day of the week must be specified. The default is "Monday".

si

The (discrete) serial interval distribution.

method

The model to be fitted, either M3 (default) or M2.

Value

A list with the following components:

• data: The data frame used as an input.

• Rnow: A data frame containing the nowcasted reproduction number.

• lambda_estim: Estimated penalty parameters of the P-splines model.

• phi_estim: Estimated overdispersion parameter from the negative binomial model.

• method: The model choice, i.e. either M3 or M2.

Author(s)

Bryan Sumalinab (writing) and Oswaldo Gressani (editing).

References

Gressani, O., Wallinga, J., Althaus, C. L., Hens, N. and Faes, C. (2022). EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number. Plos Computational Biology, 18(10): e1010618.

Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2023). An efficient approach to nowcasting the time-varying reproduction number. MedRxiv preprint.

Examples

# data("cov19incidence2022")
# si_covid <- c(0.344, 0.316, 0.168, 0.104, 0.068) # serial interval distribution
# Sys.setlocale("LC_TIME", "English")              # set system locale to English
# Rnowfit <- nowcastingR(data = cov19incidence2022, si = si_covid)
# tail(Rnowfit$Rnow)
# plot(Rnowfit)