How to quantify the time-varying reproduction number (R_t)?

Published

November 1, 2024

Ingredients

Simulated outbreak data of Ebola Virus Disease that matches some key properties of the West African Ebola outbreak of 2014-2015 from the package {outbreaks}.
The {linelist} package to keep tagged and validated columns in a line list data set.
The {incidence2} package to generate aggregated incidence data with the daily number of reported cases.
The {epiparameter} package to access to the serial interval estimated by the WHO Ebola Response Team in 2015.
Assume that the serial interval distribution approximates the generation time.
The {EpiNow2} package to estimate the time-varying reproduction number.

Steps in code

# Load packages
library(cleanepi)
library(linelist)
library(incidence2)
library(epiparameter)
library(EpiNow2)
library(tidyverse)

# Read data
dat <- subset(outbreaks::ebola_sim_clean$linelist ,!is.na(hospital)) %>% 
  dplyr::as_tibble()

# Print data
dat
#> # A tibble: 4,373 × 11
#>    case_id generation date_of_infection date_of_onset date_of_hospitalisation
#>    <chr>        <int> <date>            <date>        <date>                 
#>  1 d1fafd           0 NA                2014-04-07    2014-04-17             
#>  2 53371b           1 2014-04-09        2014-04-15    2014-04-20             
#>  3 f5c3d8           1 2014-04-18        2014-04-21    2014-04-25             
#>  4 0f58c4           2 2014-04-22        2014-04-26    2014-04-29             
#>  5 f9149b           3 NA                2014-05-03    2014-05-04             
#>  6 881bd4           3 2014-04-26        2014-05-01    2014-05-05             
#>  7 e66fa4           2 NA                2014-04-21    2014-05-06             
#>  8 20b688           3 NA                2014-05-05    2014-05-06             
#>  9 40ae5f           4 2014-05-04        2014-05-07    2014-05-08             
#> 10 f547d6           3 2014-05-02        2014-05-07    2014-05-08             
#> # ℹ 4,363 more rows
#> # ℹ 6 more variables: date_of_outcome <date>, outcome <fct>, gender <fct>,
#> #   hospital <fct>, lon <dbl>, lat <dbl>

# Get a linelist object
dat_linelist <- dat %>% 
  # create a linelist class object
  linelist::make_linelist(
    id = "case_id",
    date_onset = "date_of_onset", 
    gender = "gender",
    location = "hospital"
  ) %>% 
  # validate tagged variables
  linelist::validate_linelist() %>% 
  # keep tagged and validated variables
  linelist::tags_df()

# Print validated linelist
dat_linelist
#> # A tibble: 4,373 × 4
#>    id     date_onset gender location                                    
#>    <chr>  <date>     <fct>  <fct>                                       
#>  1 d1fafd 2014-04-07 f      Military Hospital                           
#>  2 53371b 2014-04-15 m      Connaught Hospital                          
#>  3 f5c3d8 2014-04-21 f      other                                       
#>  4 0f58c4 2014-04-26 f      other                                       
#>  5 f9149b 2014-05-03 f      Connaught Hospital                          
#>  6 881bd4 2014-05-01 f      Princess Christian Maternity Hospital (PCMH)
#>  7 e66fa4 2014-04-21 m      other                                       
#>  8 20b688 2014-05-05 m      Rokupa Hospital                             
#>  9 40ae5f 2014-05-07 f      Connaught Hospital                          
#> 10 f547d6 2014-05-07 f      Connaught Hospital                          
#> # ℹ 4,363 more rows

# Get incidence object
dat_incidence <- dat_linelist %>% 
  # aggregate cases by date of onset by days
  incidence2::incidence(
    date_index = "date_onset",
    interval = "day", 
    # rename column outputs for interoperability with {epinow2}
    date_names_to = "date",
    count_values_to = "confirm",
  ) %>%
  # keep date range between June and November 2014
  dplyr::filter(date>="2014-06-01" & date<"2014-10-01") %>%
  # drop column for interoperability with {epinow2}
  dplyr::select(-count_variable)

# Print incidence data
dat_incidence
#> # A tibble: 118 × 2
#>    date       confirm
#>    <date>       <int>
#>  1 2014-06-02       3
#>  2 2014-06-03       1
#>  3 2014-06-05       1
#>  4 2014-06-06       3
#>  5 2014-06-07       4
#>  6 2014-06-08       1
#>  7 2014-06-09       4
#>  8 2014-06-10       1
#>  9 2014-06-11       2
#> 10 2014-06-13       1
#> # ℹ 108 more rows

# Generation time ---------------------------------------------------------

# Get serial interval delay
serial_interval <-
  epiparameter::epiparameter_db(
    disease = "ebola",
    epi_name = "serial interval",
    single_epiparameter = TRUE
  )

# Print serial interval metadata
serial_interval
#> Disease: Ebola Virus Disease
#> Pathogen: Ebola Virus
#> Epi Parameter: serial interval
#> Study: WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I,
#> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson
#> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D,
#> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H,
#> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L,
#> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van
#> Kerkhove M, Varsaneux O, Kannangarage N (2015). "West African Ebola
#> Epidemic after One Year — Slowing but Not Yet under Control." _The New
#> England Journal of Medicine_. doi:10.1056/NEJMc1414992
#> <https://doi.org/10.1056/NEJMc1414992>.
#> Distribution: gamma
#> Parameters:
#>   shape: 2.188
#>   scale: 6.490

# Get distribution parameters from delay 
serial_interval_param <- epiparameter::get_parameters(serial_interval)

# Adapt {epiparameter} to the {EpiNow2} distribution interface
serial_interval_gamma <- EpiNow2::Gamma(
  shape = serial_interval_param["shape"],
  scale = serial_interval_param["scale"]
)

# Print EpiNow2 output interface
serial_interval_gamma
#> - gamma distribution:
#>   shape:
#>     2.2
#>   rate:
#>     0.15

# Delays from infection to observed data ----------------------------------

# Get fixed delay from infection to symptom onset
incubation_period <- epiparameter::epiparameter_db(
  disease = "ebola",
  epi_name = "incubation",
  single_epiparameter = TRUE
)

# Print incubation period metadata
incubation_period
#> Disease: Ebola Virus Disease
#> Pathogen: Ebola Virus
#> Epi Parameter: incubation period
#> Study: WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I,
#> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson
#> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D,
#> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H,
#> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L,
#> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van
#> Kerkhove M, Varsaneux O, Kannangarage N (2015). "West African Ebola
#> Epidemic after One Year — Slowing but Not Yet under Control." _The New
#> England Journal of Medicine_. doi:10.1056/NEJMc1414992
#> <https://doi.org/10.1056/NEJMc1414992>.
#> Distribution: gamma
#> Parameters:
#>   shape: 1.578
#>   scale: 6.528

# Get distribution parameters from delay 
incubation_period_param <- epiparameter::get_parameters(incubation_period)

# Adapt {epiparameter} to the {EpiNow2} distribution interface
incubation_period_gamma <- EpiNow2::Gamma(
  shape = incubation_period_param["shape"],
  scale = incubation_period_param["scale"]
)

# Print EpiNow2 output interface
incubation_period_gamma
#> - gamma distribution:
#>   shape:
#>     1.6
#>   rate:
#>     0.15

# Estimate transmissibility -----------------------------------------------

# Configure parallel computation
withr::local_options(base::list(mc.cores = 4))

# WAIT this takes around 5 minutes
# tictoc::tic()
estimates <- EpiNow2::epinow(
  data = dat_incidence,
  generation_time = EpiNow2::generation_time_opts(serial_interval_gamma),
  delays = EpiNow2::delay_opts(incubation_period_gamma)
)
#> WARN [2024-11-01 14:32:55] epinow: There were 151 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them. - 
#> WARN [2024-11-01 14:32:55] epinow: Examine the pairs() plot to diagnose sampling problems
#>  - 
#> WARN [2024-11-01 14:32:58] epinow: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess - 
#> WARN [2024-11-01 14:33:00] epinow: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess -
# tictoc::toc()

# Plot estimates
plot(estimates)

Steps in detail

pending

Please note that the code assumes the necessary packages are already installed. If they are not, you can install them using first the install.packages("pak") function and then the pak::pak() function for both packages in CRAN or GitHub before loading them with library().

Additionally, make sure to adjust the serial interval distribution parameters according to the specific outbreak you are analyzing.

Ingredients

Steps in code

Steps in detail

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