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nowcastr

R package for nowcasting with non-cumulative chain-ladder method.

• 1 main nowcast function
  • nowcast_cl() returns object with all intermediary results
• 4 plots
  • plot_nc_input(option = "triangle") / plot(which = "data", option = "triangle")
  • plot_nc_input(option = "millipede") / plot(which = "data", option = "millipede")
  • plot_delays() / plot(which = "delays")
  • plot_nowcast() / plot(which = "results")
• 3 utility functions
  • calculate_retro_score(): Calculate retro-scores for all groups
  • rm_repeated_values(): Remove duplicated reported values in reporting matrix
  • fill_future_reported_values(): Fill future reported values with last known values

Installation

pak::pak("whocov/nowcastr")

Quick Start

library(nowcastr)

# Run nowcast with built-in demo data
result <- nowcast_demo %>% 
  nowcast_cl(
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group"
  )

# View results
print(result@results)
plot(result, which = "results")

Data Requirements

Dataset with at least 2 date columns and a value column. The dataset can also have multiple group-by columns for batch processing.

Note that the delays (difference between the 2 dates) should have constant intervals, i.e., multiples of 1 day or 7 days.

print(nowcast_demo)

# # A tibble: 1,624 × 4
#     value date_occurrence date_report group        
#     <dbl> <date>         <date>      <chr>
#  1 251563 2024-12-16     2025-05-26  Syndromic ARI
#  2 219818 2024-12-23     2025-05-26  Syndromic ARI
#  3 219815 2024-12-23     2025-06-02  Syndromic ARI
#  4 253451 2024-12-30     2025-05-26  Syndromic ARI
#  5 253454 2024-12-30     2025-06-09  Syndromic ARI
#  6 311660 2025-01-06     2025-05-26  Syndromic ARI
#  7 311666 2025-01-06     2025-06-02  Syndromic ARI
#  8 311654 2025-01-06     2025-06-09  Syndromic ARI
#  9 311657 2025-01-06     2025-06-16  Syndromic ARI
# 10 313798 2025-01-13     2025-05-26  Syndromic ARI
# # ℹ 1,614 more rows

Usage

Data Preparation

## Visualize input data
nowcast_demo %>%
  plot_nc_input(
    option = "triangle",
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group"
  )

## Fill the missing (optional)
data <-
  nowcast_demo %>%
  fill_future_reported_values(
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group",
    max_delay = "auto"
  )

## Visualize the change
data %>%
  plot_nc_input(
    option = "triangle",
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group"
  )

Nowcast

nowcast <- data %>%
  nowcast_cl(
    col_date_occurrence = date_occurrence,
    col_date_reporting = date_report,
    col_value = value,
    group_cols = "group",
    time_units = "weeks",
    do_model_fitting = TRUE,
  )

Plotting Results

print(nowcast@delays)
nowcast %>% plot(which = "delays")

print(nowcast@results)
nowcast %>% plot(which = "results")

Output Object

nowcast_cl() returns an S7 object of class nowcast_results with the following properties:

Access properties with $:

nowcast$delays
nowcast$results
nowcast$params

Available methods: - print(nowcast) - Summary of nowcast results - plot(nowcast, which = "delays") - Delay distribution - plot(nowcast, which = "results") - Nowcast time series

Methods

Summary:

1. Input Data: Ensure three core columns: observed_value / date_of_reporting / date_of_occurrence (e.g. date_of_event / date_of_onset)
2. Calculate the reporting_delay (= date_of_reporting - date_of_occurrence)
3. Compute the completeness (= observed_value / true_value (approximated by last_reported_value))
4. Aggregate the avg_completeness for each reporting_delay
5. Optional: Fit a curve through that
6. Apply Nowcast: nowcast = observed_value / avg_completeness

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.