Instructor Notes

Instructor notes

Access epidemiological delay distributions

Instructor Note

The objective of the assessment above is to assess the interpretation of a larger or shorter generation time.

R

# ebola serial interval
ebola_serial <-
  epiparameter::epiparameter_db(
    disease = "ebola",
    epi_name = "serial",
    single_epiparameter = 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' function

R

ebola_serial

OUTPUT

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 (days)
Parameters:
  shape: 2.188
  scale: 6.490

R

# get the sd
ebola_serial$summary_stats$sd

OUTPUT

[1] 9.6

R

# get the sample_size
ebola_serial$metadata$sample_size

OUTPUT

[1] 305

Try to visualise this distribution using plot().

Also, explore all the other nested elements within the <epiparameter> object.

Share about:

What elements do you find useful for your analysis?
What other elements would you like to see in this object? How?

Instructor Note

An interesting element is the method_assess nested entry, which refers to the methods used by the study authors to assess for bias while estimating the serial interval distribution.

R

covid_serialint$method_assess

OUTPUT

$censored
[1] TRUE

$right_truncated
[1] TRUE

$phase_bias_adjusted
[1] FALSE

We will explore these concepts following episodes!

In the context of user interfaces and graphical user interfaces (GUIs), like the Distribution Zoo shiny app, a slider is a graphical control element that allows users to adjust a value by moving a handle along a track or bar. Conceptually, it provides a way to select a numeric value within a specified range by visually sliding or dragging a pointer (the handle) along a continuous axis.

Quantifying transmission

Instructor Note

This tutorial illustrates the usage of epinow() to estimate the time-varying reproduction number and infection times. Learners should understand the necessary inputs to the model and the limitations of the model output.

Instructor Note

Refer to the prior probability distribution and the posterior probability distribution.

In the “Expected change in reports” callout, by “the posterior probability that \(R_t < 1\)”, we refer specifically to the area under the posterior probability distribution curve.

Use delay distributions in analysis

Instructor Note

Access to the reference documentation (Help files) for these functions is accessible with the three double-colon notation: epiparameter:::

?epiparameter:::density.epiparameter()
?epiparameter:::cdf.epiparameter()
?epiparameter:::quantile.epiparameter()
?epiparameter:::generate.epiparameter()

Create a short-term forecast

Estimation of outbreak severity

Instructor Note

The periods are relevant: Period 1 – 15 days where CFR is zero to indicate this is due to no reported deaths; Period from Mar 15 – Apr 26 where CFR appears to be rising; Period Apr 30 – May 30 where the CFR estimate stabilises.

Instructor Note

We can showcase this last bias using the concept described in this {cfr} vignette.

Instructor Note

There is almost one month of difference.

Note that the estimate has considerable uncertainty at the beginning of the time series. After two weeks, the delay-adjusted CFR approaches the overall CFR estimate at the outbreak’s end.

Is this pattern similar to other outbreaks? We can use the data sets in this episode’s challenges. We invite you to find it out!

Account for superspreading

Instructor Note

R

# estimate the probability of
# having a cluster size of 5, 10, or 25
# secondary cases from a primary case,
# given known reproduction number and
# dispersion parameter.
superspreading::proportion_cluster_size(
  R = ebola_offspring$estimate["mu"],
  k = ebola_offspring$estimate["size"],
  cluster_size = c(5, 10, 25)
)

OUTPUT

          R         k prop_5 prop_10 prop_25
1 0.3675993 0.8539443  2.64%      0%      0%

The probability of having clusters of five people is 1.8%. At this stage, given this offspring distribution parameters, a backward strategy may not increase the probability of contain and quarantine more onward cases.