Use delay distributions in analysis

Last updated on 2024-06-18 | Edit this page

Overview

Questions

  • How to reuse delays stored in the {epiparameter} library with my existing analysis pipeline?

Objectives

  • Use distribution functions to continuous and discrete distributions stored as <epidist> objects.
  • Convert a continuous to a discrete distribution with {epiparameter}.
  • Connect {epiparameter} outputs with EpiNow2 inputs.

Prerequisites

This episode requires you to be familiar with:

Data science : Basic programming with R.

Statistics : Probability distributions.

Epidemic theory : Epidemiological parameters, time periods, Effective reproductive number.

Introduction

{epiparameter} help us to choose one specific set of epidemiological parameters from the literature, instead of copy/pasting them by hand:

R 

covid_serialint <-
  epiparameter::epidist_db(
    disease = "covid",
    epi_dist = "serial",
    author = "Nishiura",
    single_epidist = TRUE
  )

Now, we have an epidemiological parameter we can use in our analysis! In the chunk below we replaced one of the summary statistics inputs into EpiNow2::LogNormal()

R 

generation_time <- 
  EpiNow2::LogNormal(
    mean = covid_serialint$summary_stats$mean, # replaced!
    sd = covid_serialint$summary_stats$sd, # replaced!
    max = 20
  )

In this episode, we will use the distribution functions that {epiparameter} provides to get a maximum value (max) for this and any other package downstream in your analysis pipeline!

Let’s load the {epiparameter} and EpiNow2 package. For EpiNow2, we’ll set 4 cores to be used in parallel computations. We’ll use the pipe %>%, some dplyr verbs and ggplot2, so let’s also call to the tidyverse package:

R 

library(epiparameter)
library(EpiNow2)
library(tidyverse)

withr::local_options(list(mc.cores = 4))

The double-colon

The double-colon :: in R let you call a specific function from a package without loading the entire package into the current environment.

For example, dplyr::filter(data, condition) uses filter() from the dplyr package.

This help us remember package functions and avoid namespace conflicts.

Distribution functions

In R, all the statistical distributions have functions to access the following:

  • density(): Probability Density function (PDF),
  • cdf(): Cumulative Distribution function (CDF),
  • quantile(): Quantile function, and
  • generate(): Random values from the given distribution.

Functions for the Normal distribution

If you need it, read in detail about the R probability functions for the normal distribution, each of its definitions and identify in which part of a distribution they are located!

The four probability functions for the normal distribution (Jack Weiss, 2012)
The four probability functions for the normal distribution (Jack Weiss, 2012)

If you look at ?stats::Distributions, each type of distribution has a unique set of functions. However, {epiparameter} gives you the same four functions to access each of the values above for any <epidist> object you want!

R 

# plot this to have a visual reference
plot(covid_serialint, day_range = 0:20)

R 

# the density value at quantile value of 10 (days)
density(covid_serialint, at = 10)

OUTPUT 

[1] 0.01911607

R 

# the cumulative probability at quantile value of 10 (days)
cdf(covid_serialint, q = 10)

OUTPUT 

[1] 0.9466605

R 

# the quantile value (day) at a cumulative probability of 60%
quantile(covid_serialint, p = 0.6)

OUTPUT 

[1] 4.618906

R 

# generate 10 random values (days) given
# the distribution family and its parameters
generate(covid_serialint, times = 10)

OUTPUT 

 [1] 1.817744 2.013148 2.871411 1.560954 4.544691 7.315718 2.563274 6.472184
 [9] 8.090210 6.302332

Window for contact tracing and the serial interval

The serial interval is important in the optimisation of contact tracing since it provides a time window for the containment of a disease spread (Fine, 2003). Depending on the serial interval, we can evaluate the need to expand the number of days pre-onset to consider in the contact tracing to include more backwards contacts (Davis et al., 2020).

With the COVID-19 serial interval (covid_serialint) calculate:

  • How much more of the backward cases could be captured if the contact tracing method considered contacts up to 6 days pre-onset compared to 2 days pre-onset?

In Figure 5 from the R probability functions for the normal distribution, the shadowed section represents a cumulative probability of 0.997 for the quantile value at x = 2.

R 

plot(covid_serialint)

R 

cdf(covid_serialint, q = 2)

OUTPUT 

[1] 0.1111729

R 

cdf(covid_serialint, q = 6)

OUTPUT 

[1] 0.7623645

Given the COVID-19 serial interval:

  • A contact tracing method considering contacts up to 2 days pre-onset will capture around 11.1% of backward cases.

  • If this period is extended to 6 days pre-onset, this could include 76.2% of backward contacts.

If we exchange the question between days and cumulative probability to:

  • When considering secondary cases, how many days following the symptom onset of primary cases can we expect 55% of symptom onset to occur?

R 

quantile(covid_serialint, p = 0.55)

An interpretation could be:

  • The 55% percent of the symptom onset of secondary cases will happen after 4.2 days after the symptom onset of primary cases.

Discretise a continuous distribution

We are getting closer to the end! EpiNow2::LogNormal() still needs a maximum value (max).

One way to do this is to get the quantile value for the distribution’s 99th percentile or 0.99 cumulative probability. For this, we need access to the set of distribution functions for our <epidist> object.

We can use the set of distribution functions for a continuous distribution (as above). However, these values will be continuous numbers. We can discretise the continuous distribution stored in our <epidist> object to get discrete values from a continuous distribution.

When we epiparameter::discretise() the continuous distribution we get a discrete(-ized) distribution:

R 

covid_serialint_discrete <-
  epiparameter::discretise(covid_serialint)

covid_serialint_discrete

OUTPUT 

Disease: COVID-19
Pathogen: SARS-CoV-2
Epi Distribution: serial interval
Study: Nishiura H, Linton N, Akhmetzhanov A (2020). "Serial interval of novel
coronavirus (COVID-19) infections." _International Journal of
Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060
<https://doi.org/10.1016/j.ijid.2020.02.060>.
Distribution: discrete lnorm
Parameters:
  meanlog: 1.386
  sdlog: 0.568

We identify this change in the Distribution: output line of the <epidist> object. Double check this line:

Distribution: discrete lnorm

While for a continuous distribution, we plot the Probability Density Function (PDF), for a discrete distribution, we plot the Probability Mass Function (PMF):

R 

# continuous
plot(covid_serialint)

# discrete
plot(covid_serialint_discrete)

To finally get a max value, let’s access the quantile value of the 99th percentile or 0.99 probability of the distribution with the prob_dist$q notation, similarly to how we access the summary_stats values.

R 

covid_serialint_discrete_max <-
  quantile(covid_serialint_discrete, p = 0.99)

Length of quarantine and incubation period

The incubation period distribution is a useful delay to assess the length of active monitoring or quarantine (Lauer et al., 2020). Similarly, delays from symptom onset to recovery (or death) will determine the required duration of health care and case isolation (Cori et al., 2017).

Calculate:

  • Within what exact time frame do 99% of individuals exhibiting COVID-19 symptoms exhibit them after infection?

What delay distribution measures the time between infection and the onset of symptoms?

The probability functions for <epidist> discrete distributions are the same that we used for the continuous ones!

R 

# plot to have a visual reference
plot(covid_serialint_discrete, day_range = 0:20)

# density value at quantile value 10 (day)
density(covid_serialint_discrete, at = 10)

# cumulative probability at quantile value 10 (day)
cdf(covid_serialint_discrete, q = 10)

# In what quantile value (days) do we have the 60% cumulative probability?
quantile(covid_serialint_discrete, p = 0.6)

# generate random values
generate(covid_serialint_discrete, times = 10)

R 

covid_incubation <-
  epiparameter::epidist_db(
    disease = "covid",
    epi_dist = "incubation",
    single_epidist = TRUE
  )

OUTPUT 

Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan
B, Kinoshita R, Nishiura H (2020). "Incubation Period and Other
Epidemiological Characteristics of 2019 Novel Coronavirus Infections
with Right Truncation: A Statistical Analysis of Publicly Available
Case Data." _Journal of Clinical Medicine_. doi:10.3390/jcm9020538
<https://doi.org/10.3390/jcm9020538>.. 
To retrieve the citation use the 'get_citation' function

R 

covid_incubation_discrete <- epiparameter::discretise(covid_incubation)

quantile(covid_incubation_discrete, p = 0.99)

OUTPUT 

[1] 19

99% of those who develop COVID-19 symptoms will do so within 16 days of infection.

Now, Is this result expected in epidemiological terms?

From a maximum value with quantile(), we can create a sequence of quantile values as a numeric vector and calculate density() values for each:

R 

# create a discrete distribution visualisation
# from a maximum value from the distribution
quantile(covid_serialint_discrete, p = 0.99) %>%
  # generate quantile values
  # as a sequence for each natural number
  seq(1L, to = ., by = 1L) %>%
  # coerce numeric vector to data frame
  as_tibble_col(column_name = "quantile_values") %>%
  mutate(
    # calculate density values
    # for each quantile in the density function
    density_values =
      density(
        x = covid_serialint_discrete,
        at = quantile_values
      )
  ) %>%
  # create plot
  ggplot(
    aes(
      x = quantile_values,
      y = density_values
    )
  ) +
  geom_col()

Remember: In infections with pre-symptomatic transmission, serial intervals can have negative values (Nishiura et al., 2020). When we use the serial interval to approximate the generation time we need to make this distribution with positive values only!

Plug-in {epiparameter} to {EpiNow2}

Now we can plug everything into the EpiNow2::LogNormal() function!

  • the summary statistics mean and sd of the distribution,
  • a maximum value max,
  • the distribution name.

When using EpiNow2::LogNormal() to define a log normal distribution like the one in the covid_serialint object we can specify the mean and sd as parameters. Alternatively, to get the “natural” parameters for a log normal distribution we can convert its summary statistics to distribution parameters named meanlog and sdlog. With {epiparameter} we can directly get the distribution parameters using epiparameter::get_parameters():

R 

covid_serialint_parameters <-
  epiparameter::get_parameters(covid_serialint)

Then, we have:

R 

serial_interval_covid <-
  EpiNow2::LogNormal(
    meanlog = covid_serialint_parameters["meanlog"],
    sdlog = covid_serialint_parameters["sdlog"],
    max = covid_serialint_discrete_max
  )

serial_interval_covid

OUTPUT 

- lognormal distribution (max: 14):
  meanlog:
    1.4
  sdlog:
    0.57

Assuming a COVID-19 scenario, let’s use the first 60 days of the example_confirmed data set from the EpiNow2 package as reported_cases and the recently created serial_interval_covid object as inputs to estimate the time-varying reproduction number using EpiNow2::epinow().

R 

epinow_estimates_cg <- epinow(
  # cases
  data = example_confirmed[1:60],
  # delays
  generation_time = generation_time_opts(serial_interval_covid)
)

OUTPUT 

WARN [2024-06-18 17:33:18] epinow: There were 19 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 [2024-06-18 17:33:18] epinow: Examine the pairs() plot to diagnose sampling problems
 -

R 

base::plot(epinow_estimates_cg)

The plot() output includes the estimated cases by date of infection, which are reconstructed from the reported cases and delays.

Warning

Using the serial interval instead of the generation time is an alternative that can propagate bias in your estimates, even more so in diseases with reported pre-symptomatic transmission. (Chung Lau et al., 2021)

Adjusting for reporting delays

Estimating \(R_t\) requires data on the daily number of new infections. Due to lags in the development of detectable viral loads, symptom onset, seeking care, and reporting, these numbers are not readily available. All observations reflect transmission events from some time in the past. In other words, if \(d\) is the delay from infection to observation, then observations at time \(t\) inform \(R_{t−d}\), not \(R_t\). (Gostic et al., 2020)

Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From Marinović et al., 2015
Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From Marinović et al., 2015

The delay distribution could be inferred jointly with the underlying times of infection or estimated as the sum of the incubation period distribution and the distribution of delays from symptom onset to observation from line list data (reporting delay). For EpiNow2, we can specify these two complementary delay distributions in the delays argument.

R_{t} is a measure of transmission at time t. Observations after time t must be adjusted. ICU, intensive care unit. From Gostic et al., 2020
\(R_{t}\) is a measure of transmission at time \(t\). Observations after time \(t\) must be adjusted. ICU, intensive care unit. From Gostic et al., 2020

Use an incubation period for COVID-19 to estimate Rt

Estimate the time-varying reproduction number for the first 60 days of the example_confirmed data set from EpiNow2. Access to an incubation period for COVID-19 from {epiparameter} to use it as a reporting delay.

Use the last epinow() calculation using the delays argument and the delay_opts() helper function.

The delays argument and the delay_opts() helper function are analogous to the generation_time argument and the generation_time_opts() helper function.

R 

epinow_estimates <- epinow(
  # cases
  reported_cases = example_confirmed[1:60],
  # delays
  generation_time = generation_time_opts(covid_serial_interval),
  delays = delay_opts(covid_incubation_time)
)

R 

# generation time ---------------------------------------------------------

# get covid serial interval
covid_serialint <-
  epiparameter::epidist_db(
    disease = "covid",
    epi_dist = "serial",
    author = "Nishiura",
    single_epidist = TRUE
  )

# adapt epidist to epinow2
covid_serialint_discrete_max <- covid_serialint %>%
  epiparameter::discretise() %>%
  quantile(p = 0.99)

covid_serialint_parameters <-
  epiparameter::get_parameters(covid_serialint)

covid_serial_interval <-
  EpiNow2::LogNormal(
    meanlog = covid_serialint_parameters["meanlog"],
    sdlog = covid_serialint_parameters["sdlog"],
    max = covid_serialint_discrete_max
  )

# incubation time ---------------------------------------------------------

# get covid incubation period
covid_incubation <- epiparameter::epidist_db(
  disease = "covid",
  epi_dist = "incubation",
  author = "Natalie",
  single_epidist = TRUE
)

# adapt epiparameter to epinow2
covid_incubation_discrete_max <- covid_incubation %>%
  epiparameter::discretise() %>%
  quantile(p = 0.99)

covid_incubation_parameters <-
  epiparameter::get_parameters(covid_incubation)

covid_incubation_time <-
  EpiNow2::LogNormal(
    meanlog = covid_incubation_parameters["meanlog"],
    sdlog = covid_incubation_parameters["sdlog"],
    max = covid_incubation_discrete_max
  )

# epinow ------------------------------------------------------------------

# run epinow
epinow_estimates_cgi <- epinow(
  # cases
  data = example_confirmed[1:60],
  # delays
  generation_time = generation_time_opts(covid_serial_interval),
  delays = delay_opts(covid_incubation_time)
)

OUTPUT 

WARN [2024-06-18 17:35:46] epinow: There were 6 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 [2024-06-18 17:35:46] epinow: Examine the pairs() plot to diagnose sampling problems
 -

R 

base::plot(epinow_estimates_cgi)

Try to complement the delays argument with a reporting delay like the reporting_delay_fixed object of the previous episode.

How much has it changed?

After adding the incubation period, discuss:

  • Does the trend of the model fit in the “Estimate” section change?
  • Has the uncertainty changed?
  • How would you explain or interpret any of these changes?

Compare all the EpiNow2 figures generated previously.

Challenges

A code completion tip

If we write the [] next to the object covid_serialint_parameters[], within [] we can use the Tab key ↹ for code completion feature

This gives quick access to covid_serialint_parameters["meanlog"] and covid_serialint_parameters["sdlog"].

We invite you to try this out in code chunks and the R console!

Ebola’s effective reproduction number adjusted by reporting delays

Download and read the Ebola dataset:

  • Estimate the effective reproduction number using EpiNow2
  • Adjust the estimate by the available reporting delays in {epiparameter}
  • Why did you choose that parameter?

To calculate the \(R_t\) using EpiNow2, we need:

  • Aggregated incidence data, with confirmed cases per day, and
  • The generation time distribution.
  • Optionally, reporting delays distributions when available (e.g., incubation period).

To get delay distribution using {epiparameter} we can use functions like:

  • epiparameter::epidist_db()
  • epiparameter::parameter_tbl()
  • discretise()
  • quantile()

R 

# read data
# e.g.: if path to file is data/raw-data/ebola_cases.csv then:
ebola_confirmed <-
  read_csv(here::here("data", "raw-data", "ebola_cases.csv"))

# list distributions
epiparameter::epidist_db(disease = "ebola") %>%
  epiparameter::parameter_tbl()

R 

# generation time ---------------------------------------------------------

# subset one distribution for the generation time
ebola_serial <- epiparameter::epidist_db(
  disease = "ebola",
  epi_dist = "serial",
  single_epidist = TRUE
)

# adapt epiparameter to epinow2
ebola_serial_discrete <- epiparameter::discretise(ebola_serial)

serial_interval_ebola <-
  EpiNow2::Gamma(
    mean = ebola_serial$summary_stats$mean,
    sd = ebola_serial$summary_stats$sd,
    max = quantile(ebola_serial_discrete, p = 0.99)
  )

# incubation time ---------------------------------------------------------

# subset one distribution for delay of the incubation period
ebola_incubation <- epiparameter::epidist_db(
  disease = "ebola",
  epi_dist = "incubation",
  single_epidist = TRUE
)

# adapt epiparameter to epinow2
ebola_incubation_discrete <- epiparameter::discretise(ebola_incubation)

incubation_period_ebola <-
  EpiNow2::Gamma(
    mean = ebola_incubation$summary_stats$mean,
    sd = ebola_incubation$summary_stats$sd,
    max = quantile(ebola_serial_discrete, p = 0.99)
  )

# epinow ------------------------------------------------------------------

# run epinow
epinow_estimates_egi <- epinow(
  # cases
  data = ebola_confirmed,
  # delays
  generation_time = generation_time_opts(serial_interval_ebola),
  delays = delay_opts(incubation_period_ebola)
)

OUTPUT 

WARN [2024-06-18 17:38:01] epinow: There were 7 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 [2024-06-18 17:38:01] epinow: Examine the pairs() plot to diagnose sampling problems
 -

R 

plot(epinow_estimates_egi)

What to do with Weibull distributions?

Use the influenza_england_1978_school dataset from the outbreaks package to calculate the effective reproduction number using EpiNow2 adjusting by the available reporting delays in {epiparameter}.

EpiNow2::NonParametric() accepts Probability Mass Functions (PMF) from any distribution family. Read the reference guide on Probability distributions.

R 

# What parameters are available for Influenza?
epiparameter::epidist_db(disease = "influenza") %>%
  epiparameter::parameter_tbl() %>%
  count(epi_distribution)

OUTPUT 

# Parameter table:
# A data frame:    3 × 2
  epi_distribution      n
  <chr>             <int>
1 generation time       1
2 incubation period    15
3 serial interval       1

R 

# generation time ---------------------------------------------------------

# Read the generation time
influenza_generation <-
  epiparameter::epidist_db(
    disease = "influenza",
    epi_dist = "generation"
  )

influenza_generation

OUTPUT 

Disease: Influenza
Pathogen: Influenza-A-H1N1
Epi Distribution: generation time
Study: Lessler J, Reich N, Cummings D, New York City Department of Health and
Mental Hygiene Swine Influenza Investigation Team (2009). "Outbreak of
2009 Pandemic Influenza A (H1N1) at a New York City School." _The New
England Journal of Medicine_. doi:10.1056/NEJMoa0906089
<https://doi.org/10.1056/NEJMoa0906089>.
Distribution: weibull
Parameters:
  shape: 2.360
  scale: 3.180

R 

# EpiNow2 currently accepts Gamma or LogNormal
# other can pass the PMF function

influenza_generation_discrete <-
  epiparameter::discretise(influenza_generation)

influenza_generation_max <-
  quantile(influenza_generation_discrete, p = 0.99)

influenza_generation_pmf <-
  density(
    influenza_generation_discrete,
    at = 1:influenza_generation_max
  )

influenza_generation_pmf

OUTPUT 

[1] 0.06312336 0.22134988 0.29721220 0.23896828 0.12485164 0.04309454

R 

# EpiNow2::NonParametric() can also accept the PMF values
generation_time_influenza <-
  EpiNow2::NonParametric(
    pmf = influenza_generation_pmf
  )

# incubation period -------------------------------------------------------

# Read the incubation period
influenza_incubation <-
  epiparameter::epidist_db(
    disease = "influenza",
    epi_dist = "incubation",
    single_epidist = TRUE
  )

# Discretize incubation period
influenza_incubation_discrete <-
  epiparameter::discretise(influenza_incubation)

influenza_incubation_max <-
  quantile(influenza_incubation_discrete, p = 0.99)

influenza_incubation_pmf <-
  density(
    influenza_incubation_discrete,
    at = 1:influenza_incubation_max
  )

influenza_incubation_pmf

OUTPUT 

[1] 0.05749151 0.16687705 0.22443092 0.21507632 0.16104546 0.09746609 0.04841928

R 

# EpiNow2::NonParametric() can also accept the PMF values
incubation_time_influenza <-
  EpiNow2::NonParametric(
    pmf = influenza_incubation_pmf
  )

# epinow ------------------------------------------------------------------

# Read data
influenza_cleaned <-
  outbreaks::influenza_england_1978_school %>%
  select(date, confirm = in_bed)

# Run epinow()
epinow_estimates_igi <- epinow(
  # cases
  data = influenza_cleaned,
  # delays
  generation_time = generation_time_opts(generation_time_influenza),
  delays = delay_opts(incubation_time_influenza)
)

plot(epinow_estimates_igi)

Next steps

How to get distribution parameters from statistical distributions?

How to get the mean and standard deviation from a generation time with only distribution parameters but no summary statistics like mean or sd for EpiNow2::Gamma() or EpiNow2::LogNormal()?

Look at the {epiparameter} vignette on parameter extraction and conversion and its use cases!

How to estimate delay distributions for Disease X?

Refer to this excellent tutorial on estimating the serial interval and incubation period of Disease X accounting for censoring using Bayesian inference with packages like rstan and coarseDataTools.

Then, after you get your estimated values, you can manually create your own<epidist> class objects with epiparameter::epidist()! Take a look at its reference guide on “Create an <epidist> object”!

Key Points

  • Use distribution functions with <epidist> objects to get summary statistics and informative parameters for public health interventions like the Window for contact tracing and Length of quarantine.
  • Use discretise() to convert continuous to discrete delay distributions.
  • Use {epiparameter} to get reporting delays required in transmissibility estimates.