+ Access epidemiological delay distributions
+Last updated on 2024-06-18 | + + Edit this page
+ + + +Overview
+Questions
+- How to get access to disease delay distributions from a +pre-established database for use in analysis? +
Objectives
+- Get delays from a literature search database with
+
{epiparameter}.
+ - Get distribution parameters and summary statistics of delay +distributions. +
Introduction +
+Infectious diseases follow an infection cycle, which usually includes +the following phases: presymptomatic period, symptomatic period and +recovery period, as described by their natural history. These time +periods can be used to understand transmission dynamics and inform +disease prevention and control interventions.
+
Definitions +
+Look at the glossary for the definitions +of all the time periods of the figure above!
+However, early in an epidemic, modelling efforts can be delayed by
+the lack of a centralised resource that summarises input parameters for
+the disease of interest (Nash et al., 2023).
+Projects like {epiparameter} and {epireview}
+are building online catalogues following literature synthesis protocols
+that can help parametrise models by easily accessing a comprenhensive
+library of previously estimated epidemiological parameters from past
+outbreaks.
To exemplify how to use the {epiparameter} R package in
+your analysis pipeline, our goal in this episode will be to access one
+specific set of epidemiological parameters from the literature, instead
+of copying-and-pasting them by hand, to plug them into an
+EpiNow2 analysis workflow.
Let’s start loading the {epiparameter} package. We’ll
+use the pipe %>% to connect some of their functions,
+some tibble and dplyr functions, so let’s
+also call to the tidyverse package:
R +
+
+library(epiparameter)
+library(tidyverse)
+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.
+The problem +
+If we want to estimate the transmissibility of an infection, it’s
+common to use a package such as EpiEstim or
+EpiNow2. However, both require some epidemiological
+information as an input. For example, in EpiNow2 we use
+EpiNow2::Gamma() to specify a generation time as a
+probability distribution adding its mean, standard
+deviation (sd), and maximum value (max).
To specify a generation_time that follows a
+Gamma distribution with mean \(\mu =
+4\), standard deviation \(\sigma =
+2\), and a maximum value of 20, we write:
R +
+
+generation_time <-
+ EpiNow2::Gamma(
+ mean = 4,
+ sd = 2,
+ max = 20
+ )
+It is a common practice for analysts to manually search the available
+literature and copy and paste the summary statistics or
+the distribution parameters from scientific
+publications. A challenge that is often faced is that the reporting of
+different statistical distributions is not consistent across the
+literature. {epiparameter}’s objective is to facilitate the
+access to reliable estimates of distribution parameters for a range of
+infectious diseases, so that they can easily be implemented in outbreak
+analytic pipelines.
In this episode, we will access the summary statistics of
+generation time for COVID-19 from the library of epidemiological
+parameters provided by {epiparameter}. These metrics can be
+used to estimate the transmissibility of this disease using
+EpiNow2 in subsequent episodes.
Let’s start by looking at how many entries are available in the
+epidemiological distributions database in
+{epiparameter} using epidist_db() for the
+epidemiological distribution epi_dist called generation
+time with the string "generation":
R +
+
+epiparameter::epidist_db(
+ epi_dist = "generation"
+)
+OUTPUT +
+Returning 1 results that match the criteria (1 are parameterised).
+Use subset to filter by entry variables or single_epidist to return a single entry.
+To retrieve the citation for each use the 'get_citation' functionOUTPUT +
+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: 3.180Currently, in the library of epidemiological parameters, we have one
+"generation" time entry for Influenza. Instead, we can look
+at the serial intervals for COVID-19. Let find
+what we need to consider for this!
Generation time vs serial interval +
+The generation time, jointly with the reproduction number (\(R\)), provide valuable insights on the +strength of transmission and inform the implementation of control +measures. Given a \(R>1\), the +shorter the generation time, the earlier the incidence of disease cases +will grow.
+
In calculating the effective reproduction number (\(R_{t}\)), the generation time +distribution is often approximated by the serial interval distribution. +This frequent approximation is because it is easier to observe and +measure the onset of symptoms than the onset of infectiousness.
+
However, using the serial interval as an approximation of +the generation time is primarily valid for diseases in which +infectiousness starts after symptom onset (Chung +Lau et al., 2021). In cases where infectiousness starts before +symptom onset, the serial intervals can have negative values, which is +the case for diseases with pre-symptomatic transmission (Nishiura +et al., 2020).
+ +From time periods to probability +distributions. +
+When we calculate the serial interval, we see that not all +case pairs have the same time length. We will observe this variability +for any case pair and individual time period, including the incubation period and infectious period.
+
To summarise these data from individual and pair time periods, we can +find the statistical distributions that best fit the +data (McFarland +et al., 2023).
+ +
Statistical distributions are summarised in terms of their +summary statistics like the location (mean and +percentiles) and spread (variance or standard deviation) of the +distribution, or with their distribution parameters +that inform about the form (shape and rate/scale) of the +distribution. These estimated values can be reported with their +uncertainty (95% confidence intervals).
+| Gamma | +mean | +shape | +rate/scale | +
|---|---|---|---|
| MERS-CoV | +14.13(13.9–14.7) | +6.31(4.88–8.52) | +0.43(0.33–0.60) | +
| COVID-19 | +5.1(5.0–5.5) | +2.77(2.09–3.88) | +0.53(0.38–0.76) | +
| Weibull | +mean | +shape | +rate/scale | +
|---|---|---|---|
| MERS-CoV | +14.2(13.3–15.2) | +3.07(2.64–3.63) | +16.1(15.0–17.1) | +
| COVID-19 | +5.2(4.6–5.9) | +1.74(1.46–2.11) | +5.83(5.08–6.67) | +
| Log normal | +mean | +mean-log | +sd-log | +
|---|---|---|---|
| MERS-CoV | +14.08(13.1–15.2) | +2.58(2.50–2.68) | +0.44(0.39–0.5) | +
| COVID-19 | +5.2(4.2–6.5) | +1.45(1.31–1.61) | +0.63(0.54–0.74) | +
Serial interval +
+Assume that COVID-19 and SARS have similar reproduction number values +and that the serial interval approximates the generation time.
+Given the serial interval of both infections in the figure below:
+- Which one would be harder to control? +
- Why do you conclude that? +
The peak of each curve can inform you about the location of the mean +of each distribution. The larger the mean, the larger the serial +interval.
+Which one would be harder to control?
+COVID-19
+Why do you conclude that?
+COVID-19 has the lowest mean serial interval. The approximate mean +value for the serial interval of COVID-19 is around four days, and SARS +is about seven days. Thus, COVID-19 will likely have newer generations +in less time than SARS, assuming similar reproduction numbers.
+Choosing epidemiological parameters +
+In this section, we will use {epiparameter} to obtain
+the serial interval for COVID-19, as an alternative to the generation
+time.
Let’s ask now how many parameters we have in the epidemiological
+distributions database (epidist_db()) with the
+disease named covid-19. Run this locally!
R +
+
+epiparameter::epidist_db(
+ disease = "covid"
+)
+From the {epiparameter} package, we can use the
+epidist_db() function to ask for any disease
+and also for a specific epidemiological distribution
+(epi_dist). Run this in your console:
R +
+
+epiparameter::epidist_db(
+ disease = "COVID",
+ epi_dist = "serial"
+)
+With this query combination, we get more than one delay distribution.
+This output is an <epidist> class object.
CASE-INSENSITIVE +
+epidist_db is case-insensitive.
+This means that you can use strings with letters in upper or lower case
+indistinctly. Strings like "serial",
+"serial interval" or "serial_interval" are
+also valid.
As suggested in the outputs, to summarise an
+<epidist> object and get the column names from the
+underlying parameter database, we can add the
+epiparameter::parameter_tbl() function to the previous code
+using the pipe %>%:
R +
+
+epiparameter::epidist_db(
+ disease = "covid",
+ epi_dist = "serial"
+) %>%
+ epiparameter::parameter_tbl()
+OUTPUT +
+Returning 4 results that match the criteria (3 are parameterised).
+Use subset to filter by entry variables or single_epidist to return a single entry.
+To retrieve the citation for each use the 'get_citation' functionOUTPUT +
+# Parameter table:
+# A data frame: 4 × 7
+ disease pathogen epi_distribution prob_distribution author year sample_size
+ <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
+1 COVID-19 SARS-CoV… serial interval <NA> Alene… 2021 3924
+2 COVID-19 SARS-CoV… serial interval lnorm Nishi… 2020 28
+3 COVID-19 SARS-CoV… serial interval weibull Nishi… 2020 18
+4 COVID-19 SARS-CoV… serial interval norm Yang … 2020 131In the epiparameter::parameter_tbl() output, we can also
+find different types of probability distributions (e.g., Log-normal,
+Weibull, Normal).
{epiparameter} uses the base R naming
+convention for distributions. This is why Log normal is
+called lnorm.
Entries with a missing value (<NA>) in the
+prob_distribution column are non-parameterised
+entries. They have summary statistics but no probability distribution.
+Compare these two outputs:
R +
+
+# get an <epidist> object
+distribution <-
+ epiparameter::epidist_db(
+ disease = "covid",
+ epi_dist = "serial"
+ )
+
+distribution %>%
+ # pluck the first entry in the object class <list>
+ pluck(1) %>%
+ # check if <epidist> object have distribution parameters
+ is_parameterised()
+
+# check if the second <epidist> object
+# have distribution parameters
+distribution %>%
+ pluck(2) %>%
+ is_parameterised()
+Parameterised entries have an Inference method
+As detailed in ?is_parameterised, a parameterised
+distribution is the entry that has a probability distribution associated
+with it provided by an inference_method as shown in
+metadata:
R +
+
+distribution[[1]]$metadata$inference_method
+distribution[[2]]$metadata$inference_method
+distribution[[4]]$metadata$inference_method
+Find your delay distributions! +
+Take 2 minutes to explore the {epiparameter}
+library.
Choose a disease of interest (e.g., Influenza, +Measles, etc.) and a delay distribution (e.g., the incubation period, +onset to death, etc.).
+Find:
+How many delay distributions are for that disease?
+How many types of probability distribution (e.g., gamma, log +normal) are for a given delay in that disease?
+
Ask:
+Do you recognise the papers?
+Should
{epiparameter}literature review consider any +other paper?
+
The epidist_db() function with disease
+alone counts the number of entries like:
- studies, and +
- delay distributions. +
The epidist_db() function with disease and
+epi_dist gets a list of all entries with:
- the complete citation, +
- the type of a probability distribution, and +
- distribution parameter values. +
The combo of epidist_db() plus
+parameter_tbl() gets a data frame of all entries with
+columns like:
- the type of the probability distribution per delay, +and +
- author and year of the study. +
We choose to explore Ebola’s delay distributions:
+R +
+
+# we expect 16 delays distributions for ebola
+epiparameter::epidist_db(
+ disease = "ebola"
+)
+OUTPUT +
+Returning 17 results that match the criteria (17 are parameterised).
+Use subset to filter by entry variables or single_epidist to return a single entry.
+To retrieve the citation for each use the 'get_citation' functionOUTPUT +
+# List of 17 <epidist> objects
+Number of diseases: 1
+❯ Ebola Virus Disease
+Number of epi distributions: 9
+❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ serial interval
+[[1]]
+Disease: Ebola Virus Disease
+Pathogen: Ebola Virus
+Epi Distribution: offspring distribution
+Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). "Superspreading and
+the effect of individual variation on disease emergence." _Nature_.
+doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>.
+Distribution: nbinom
+Parameters:
+ mean: 1.500
+ dispersion: 5.100
+
+[[2]]
+Disease: Ebola Virus Disease
+Pathogen: Ebola Virus-Zaire Subtype
+Epi Distribution: incubation period
+Study: Eichner M, Dowell S, Firese N (2011). "Incubation period of ebola
+hemorrhagic virus subtype zaire." _Osong Public Health and Research
+Perspectives_. doi:10.1016/j.phrp.2011.04.001
+<https://doi.org/10.1016/j.phrp.2011.04.001>.
+Distribution: lnorm
+Parameters:
+ meanlog: 2.487
+ sdlog: 0.330
+
+[[3]]
+Disease: Ebola Virus Disease
+Pathogen: Ebola Virus-Zaire Subtype
+Epi Distribution: onset to death
+Study: The Ebola Outbreak Epidemiology Team, Barry A, Ahuka-Mundeke S, Ali
+Ahmed Y, Allarangar Y, Anoko J, Archer B, Abedi A, Bagaria J, Belizaire
+M, Bhatia S, Bokenge T, Bruni E, Cori A, Dabire E, Diallo A, Diallo B,
+Donnelly C, Dorigatti I, Dorji T, Waeber A, Fall I, Ferguson N,
+FitzJohn R, Tengomo G, Formenty P, Forna A, Fortin A, Garske T,
+Gaythorpe K, Gurry C, Hamblion E, Djingarey M, Haskew C, Hugonnet S,
+Imai N, Impouma B, Kabongo G, Kalenga O, Kibangou E, Lee T, Lukoya C,
+Ly O, Makiala-Mandanda S, Mamba A, Mbala-Kingebeni P, Mboussou F,
+Mlanda T, Makuma V, Morgan O, Mulumba A, Kakoni P, Mukadi-Bamuleka D,
+Muyembe J, Bathé N, Ndumbi Ngamala P, Ngom R, Ngoy G, Nouvellet P, Nsio
+J, Ousman K, Peron E, Polonsky J, Ryan M, Touré A, Towner R, Tshapenda
+G, Van De Weerdt R, Van Kerkhove M, Wendland A, Yao N, Yoti Z, Yuma E,
+Kalambayi Kabamba G, Mwati J, Mbuy G, Lubula L, Mutombo A, Mavila O,
+Lay Y, Kitenge E (2018). "Outbreak of Ebola virus disease in the
+Democratic Republic of the Congo, April–May, 2018: an epidemiological
+study." _The Lancet_. doi:10.1016/S0140-6736(18)31387-4
+<https://doi.org/10.1016/S0140-6736%2818%2931387-4>.
+Distribution: gamma
+Parameters:
+ shape: 2.400
+ scale: 3.333
+
+# ℹ 14 more elements
+# ℹ Use `print(n = ...)` to see more elements.
+# ℹ Use `parameter_tbl()` to see a summary table of the parameters.
+# ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/dev/articles/database.htmlNow, from the output of epiparameter::epidist_db(), What
+is an offspring
+distribution?
We choose to find Ebola’s incubation periods. This output list all +the papers and parameters found. Run this locally if needed:
+R +
+
+epiparameter::epidist_db(
+ disease = "ebola",
+ epi_dist = "incubation"
+)
+We use parameter_tbl() to get a summary display of
+all:
R +
+
+# we expect 2 different types of delay distributions
+# for ebola incubation period
+epiparameter::epidist_db(
+ disease = "ebola",
+ epi_dist = "incubation"
+) %>%
+ parameter_tbl()
+OUTPUT +
+Returning 5 results that match the criteria (5 are parameterised).
+Use subset to filter by entry variables or single_epidist to return a single entry.
+To retrieve the citation for each use the 'get_citation' functionOUTPUT +
+# Parameter table:
+# A data frame: 5 × 7
+ disease pathogen epi_distribution prob_distribution author year sample_size
+ <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
+1 Ebola Vi… Ebola V… incubation peri… lnorm Eichn… 2011 196
+2 Ebola Vi… Ebola V… incubation peri… gamma WHO E… 2015 1798
+3 Ebola Vi… Ebola V… incubation peri… gamma WHO E… 2015 49
+4 Ebola Vi… Ebola V… incubation peri… gamma WHO E… 2015 957
+5 Ebola Vi… Ebola V… incubation peri… gamma WHO E… 2015 792We find two types of probability distributions for this query: +log normal and gamma.
+How does {epiparameter} do the collection and review of
+peer-reviewed literature? We invite you to read the vignette on “Data
+Collation and Synthesis Protocol”!
Select a single distribution +
+The epiparameter::epidist_db() function works as a
+filtering or subset function. Let’s use the author argument
+to filter Hiroshi Nishiura parameters:
R +
+
+epiparameter::epidist_db(
+ disease = "covid",
+ epi_dist = "serial",
+ author = "Hiroshi"
+) %>%
+ epiparameter::parameter_tbl()
+OUTPUT +
+Returning 2 results that match the criteria (2 are parameterised).
+Use subset to filter by entry variables or single_epidist to return a single entry.
+To retrieve the citation for each use the 'get_citation' functionOUTPUT +
+# Parameter table:
+# A data frame: 2 × 7
+ disease pathogen epi_distribution prob_distribution author year sample_size
+ <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
+1 COVID-19 SARS-CoV… serial interval lnorm Nishi… 2020 28
+2 COVID-19 SARS-CoV… serial interval weibull Nishi… 2020 18We still get more than one epidemiological parameter. We can set the
+single_epidist argument to TRUE to only
+one:
R +
+
+epiparameter::epidist_db(
+ disease = "covid",
+ epi_dist = "serial",
+ author = "Hiroshi",
+ single_epidist = TRUE
+)
+OUTPUT +
+Using 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>..
+To retrieve the citation use the 'get_citation' functionOUTPUT +
+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: lnorm
+Parameters:
+ meanlog: 1.386
+ sdlog: 0.568How does ‘single_epidist’ works? +
+Looking at the help documentation for
+?epiparameter::epidist_db():
- If multiple entries match the arguments supplied and
+
single_epidist = TRUE, then the parameterised +<epidist>with the largest sample size will +be returned.
+ - If multiple entries are equal after this sorting, the first +entry will be returned. +
What is a parametrised <epidist>? Look at
+?is_parameterised.
Let’s assign this <epidist> class object to the
+covid_serialint object.
R +
+
+covid_serialint <-
+ epiparameter::epidist_db(
+ disease = "covid",
+ epi_dist = "serial",
+ author = "Nishiura",
+ single_epidist = TRUE
+ )
+You can use plot() to <epidist>
+objects to visualise:
- the Probability Density Function (PDF) and +
- the Cumulative Distribution Function (CDF). +
R +
+
+# plot <epidist> object
+plot(covid_serialint)
+
With the day_range argument, you can change the length
+or number of days in the x axis. Explore what this looks
+like:
R +
+
+# plot <epidist> object
+plot(covid_serialint, day_range = 0:20)
+Extract the summary statistics +
+We can get the mean and standard deviation
+(sd) from this <epidist> diving into the
+summary_stats object:
R +
+
+# get the mean
+covid_serialint$summary_stats$mean
+OUTPUT +
+[1] 4.7Now, we have an epidemiological parameter we can reuse! Given that
+the covid_serialint is a lnorm or log normal
+distribution, we can replace the summary statistics
+numbers we plug into the EpiNow2::LogNormal() function:
R +
+
+generation_time <-
+ EpiNow2::LogNormal(
+ mean = covid_serialint$summary_stats$mean, # replaced!
+ sd = covid_serialint$summary_stats$sd, # replaced!
+ max = 20
+ )
+In the next episode we’ll learn how to use EpiNow2 to
+correctly specify distributions, estimate transmissibility. Then, how to
+use distribution functions to get a maximum value
+(max) for EpiNow2::LogNormal() and use
+{epiparameter} in your analysis.
Log normal distributions +
+If you need the log normal distribution parameters
+instead of the summary statistics, we can use
+epiparameter::get_parameters():
R +
+
+covid_serialint_parameters <-
+ epiparameter::get_parameters(covid_serialint)
+
+covid_serialint_parameters
+OUTPUT +
+ meanlog sdlog
+1.3862617 0.5679803 This gets a vector of class <numeric> ready to use
+as input for any other package!
Challenges +
+R +
+
+# ebola serial interval
+ebola_serial <-
+ epiparameter::epidist_db(
+ disease = "ebola",
+ epi_dist = "serial",
+ single_epidist = TRUE
+ )
+OUTPUT +
+Using 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>..
+To retrieve the citation use the 'get_citation' functionR +
+
+ebola_serial
+OUTPUT +
+Disease: Ebola Virus Disease
+Pathogen: Ebola Virus
+Epi Distribution: 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: 6.490R +
+
+# get the sd
+ebola_serial$summary_stats$sd
+OUTPUT +
+[1] 9.6R +
+
+# get the sample_size
+ebola_serial$metadata$sample_size
+OUTPUT +
+[1] 305Try to visualise this distribution using plot().
Also, explore all the other nested elements within the
+<epidist> object.
Share about:
+- What elements do you find useful for your analysis? +
- What other elements would you like to see in this object? How? +
Ebola’s severity parameter +
+A severity parameter like the duration of hospitalisation could add +to the information needed about the bed capacity in response to an +outbreak (Cori +et al., 2017).
+ +For Ebola:
+- What is the reported point estimate of the mean duration of +health care and case isolation? +
An informative delay should measure the time from symptom onset to +recovery or death.
+Find a way to access the whole {epiparameter} database
+and find how that delay may be stored. The parameter_tbl()
+output is a dataframe.
R +
+
+# one way to get the list of all the available parameters
+epidist_db(disease = "all") %>%
+ parameter_tbl() %>%
+ as_tibble() %>%
+ distinct(epi_distribution)
+OUTPUT +
+Returning 122 results that match the criteria (99 are parameterised).
+Use subset to filter by entry variables or single_epidist to return a single entry.
+To retrieve the citation for each use the 'get_citation' functionOUTPUT +
+# A tibble: 12 × 1
+ epi_distribution
+ <chr>
+ 1 incubation period
+ 2 serial interval
+ 3 generation time
+ 4 onset to death
+ 5 offspring distribution
+ 6 hospitalisation to death
+ 7 hospitalisation to discharge
+ 8 notification to death
+ 9 notification to discharge
+10 onset to discharge
+11 onset to hospitalisation
+12 onset to ventilation R +
+
+ebola_severity <- epidist_db(
+ disease = "ebola",
+ epi_dist = "onset to discharge"
+)
+OUTPUT +
+Returning 1 results that match the criteria (1 are parameterised).
+Use subset to filter by entry variables or single_epidist to return a single entry.
+To retrieve the citation for each use the 'get_citation' functionR +
+
+# point estimate
+ebola_severity$summary_stats$mean
+OUTPUT +
+[1] 15.1Check that for some {epiparameter} entries you will also
+have the uncertainty around the point estimate of each
+summary statistic:
R +
+
+# 95% confidence intervals
+ebola_severity$summary_stats$mean_ci
+OUTPUT +
+[1] 95R +
+
+# limits of the confidence intervals
+ebola_severity$summary_stats$mean_ci_limits
+OUTPUT +
+[1] 14.6 15.6The distribution zoo +
+Explore this shinyapp called The Distribution +Zoo!
+Follow these steps to reproduce the form of the COVID serial interval
+distribution from {epiparameter}
+(covid_serialint object):
- Access the https://ben18785.shinyapps.io/distribution-zoo/ shiny +app website, +
- Go to the left panel, +
- Keep the Category of distribution:
+
Continuous Univariate,
+ - Select a new Type of distribution:
+
Log-Normal,
+ - Move the sliders, i.e. the graphical control
+element that allows you to adjust a value by moving a handle along a
+horizontal track or bar to the
covid_serialint+parameters.
+
Replicate these with the distribution object and all its
+list elements: [[2]], [[3]], and
+[[4]]. Explore how the shape of a distribution changes when
+its parameters change.
Share about:
+- What other features of the website do you find helpful? +
Key Points +
+- Use
{epiparameter}to access the literature catalogue +of epidemiological delay distributions.
+ - Use
epidist_db()to select single delay +distributions.
+ - Use
parameter_tbl()for an overview of multiple delay +distributions.
+ - Reuse known estimates for unknown disease in the early stage of an +outbreak when no contact tracing data is available. +