A place for Epiverse-TRACE developers to share their reflections, learnings, and showcase their work.

Hugo Gruson

Throughout the Epiverse project, we use the renv R package to ensure reproducibility of the training materials and the pipelines we are providing. But we sometimes get reports from users who struggle to rebuild the environment and run the code. In this post, we dissect the source of these issues, explain why in reality renv is not at fault, and how this is caused by the inherent complexity of reproducibility.

Things that can go wrong when using renv

I have recently published a series of blog posts on the reasons why one may want to start using object-oriented programming (and more specifically R S3 classes) to improve interoperability with other tools from the ecosystem. But there are still questions I have not addressed directly, even if they may have been implicitly included sometimes: what makes a good object class? What good practices in class &amp;

Choosing the Right Parent for R Object Classes

James Azam

Adam Kucharski

Updating Reproduction Number Estimates for Mpox in the Democratic Republic of Congo Using Surveillance Data

Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data

Practical considerations for measuring the effective reproductive number, Rt

Description of the first global outbreak of mpox: an analysis of global surveillance data

Estimating epidemiological delay distributions for infectious diseases

There are ongoing outbreaks of mpox globally. The Democratic Republic of Congo (DRC) is so far the worst hit with a total of 7,851 cases and 384 deaths reported between January 1 and May 26, 2024
<sup>
 1
</sup>
. Before 2022, there were few reports of sustained mpox transmission globally.

2024 mpox outbreak: common analytics tasks and available R tools

We are continuing our post series on S3 object orientation and interoperability in R. We have previously discussed what makes a good S3 class and how to choose a good parent for it, as well as when to write or not write a custom method. We have highlighted in particular how classes inheriting from data.frames can simplify user experience because of familiarity, and reduce developer workload due to the pre-existing S3 methods.

Improving Ecosystem Interoperability Iteratively via Progressive Enhancement

Joshua W. Lambert

James Mba Azam

Pratik Gupte

Importance of investing time and money in integrating large language model-based agents into outbreak analytics pipelines

GitHub recently previewed ‘Copilot Workspace’, which aims to use generative AI to assist software developers. Rather than just giving in-line suggestions, as GitHub copilot does, workspace allows users to map out and execute entire projects. We got early preview access to the tool, so decided to see how well it performed for our needs in Epiverse-TRACE.

Some thoughts after a trial run of GitHub’s Copilot Workspace

Epiverse-TRACE developer space

There are ongoing outbreaks of mpox globally. The Democratic Republic of
Congo (DRC) is so far the worst hit with a total of 7,851 cases and 384
deaths reported between January 1 and May 26, 2024 ^1^. Before 2022,
there were few reports of sustained mpox transmission globally. However,
during the following year (Jan 1, 2022, and Jan 29, 2023), 110 countries
in all six WHO Regions had reported a total of 85,473 confirmed cases
and 89 deaths between them [(Laurenson-Schafer et al. 2023)]{.citation
cites="laurenson2023description"}.

Mpox is transmitted through respiratory droplets and direct contact with
infected persons. The disease is characterized by fever, cough, and a
rash, with the mean incubation period estimated to be about 7.8 days
[(Ward et al. 2022)]{.citation cites="ward2022transmission"}. Infected
individuals may experience severe symptoms leading to hospitalisation or
death. There are two genetic clades: clade I and clade II, which also
has subclades IIa and IIb [(Laurenson-Schafer et al. 2023)]{.citation
cites="laurenson2023description"}.

Several analyses of the potential impact of outbreaks at country level
have already emerged in 2024. The US CDC, for example, has analysed the
potential size of outbreaks resulting from transmission within and
between households ^2^ and the risk of Clade 1 mpox outbreaks among some
key populations associated with key transmission routes ^3^. Another
group of researchers have estimated the transmissibility of mpox in the
DRC from more recent (2010 - 2019) surveillance data to update existing
estimates, which are based on old data [(Charniga, McCollum, et al.
2024)]{.citation cites="charniga2024updating"}. However, tackling
ongoing outbreaks around the world will require a coordinated response
from the global health community.

The Epiverse-TRACE team is developing a set of analytical tools that
could help support decision-makers during outbreaks. This post provides
an overview of the tasks that such tools can be applied to in the
context of the ongoing mpox outbreaks.

:::::::::::::::::::::::::::::::::: {#common-outbreak-analytics-tasks .section .level2}
## Common outbreak analytics tasks {.anchored anchor-id="common-outbreak-analytics-tasks"}

Outbreak analytics in the context of the ongoing mpox outbreak involves
several tasks that can be handled by existing and emerging R tools. Some
of the tasks include estimating the transmission potential, forecasting
infection dynamics, estimating severity, and assessing the impact of
interventions.

Here, we briefly describe some common tasks, data required, and the
ready R tools/packages developed by the Epiverse-TRACE team and the
wider community.

::::::: {#cleaning-and-validating-data .section .level3}
### Cleaning and validating data {.anchored anchor-id="cleaning-and-validating-data"}

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Data cleaning is often the first task in outbreak analytics. This
usually involves identifying and correcting errors in the data,
standardizing the format of key variables, and ensuring that the data is
in a format that is fit for analysis. Data validation is also important
to ensure that the data is accurate.
:::
:::::
::::::

[`{cleanepi}`](https://epiverse-trace.github.io/cleanepi/) is useful for
cleaning individual-level datasets, and
[`{listlist}`](https://epiverse-trace.github.io/linelist/) can be used
to tag and validate key variables in datasets that might change over
time. The [`{numberize}`](https://epiverse-trace.github.io/numberize/)
package can also be used to convert numbers written as text. It
currently has functionality for English, Spanish, and French.
:::::::

::::::::::::: {#estimating-transmission-potential .section .level3}
### Estimating transmission potential {.anchored anchor-id="estimating-transmission-potential"}

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A key initial question during emerging outbreaks is the transmission
potential of the disease. This is typically quantified using parameters
such as: the basic reproduction number,
![](https://latex.codecogs.com/png.latex?R_0); the time-varying
reproduction number, ![](https://latex.codecogs.com/png.latex?R_t); and
![](https://latex.codecogs.com/png.latex?k), which captures individual
heterogeneity in transmission (i.e. "superspreading" potential). These
quantities are useful to assess the potential for further spread of the
disease and the impact of interventions.
:::
:::::
::::::

::: {#population-level-transmissibility-r_0-and-r_t .section .level4}
#### Population-level transmissibility (![](https://latex.codecogs.com/png.latex?R_0) and ![](https://latex.codecogs.com/png.latex?R_t)) {#population-level-transmissibility-and .anchored anchor-id="population-level-transmissibility-r_0-and-r_t"}

The basic reproduction number,
![](https://latex.codecogs.com/png.latex?R_0), is the average number of
secondary cases produced by a single infected individual in a completely
susceptible population. The time-varying reproduction number,
![](https://latex.codecogs.com/png.latex?R_t), on the other hand, is the
average number of secondary cases produced by a single infected
individual at time ![](https://latex.codecogs.com/png.latex?t) in a
partially susceptible population.
![](https://latex.codecogs.com/png.latex?R_t) is a more useful quantity
during an outbreak as it accounts for the impact of interventions and
changes in population immunity.

If data is available on the daily number of reported cases,
[`{EpiNow2}`](https://epiforecasts.io/EpiNow2/) and
[`{EpiEstim}`](https://mrc-ide.github.io/EpiEstim/) can be used to
estimate ![](https://latex.codecogs.com/png.latex?R_t). These packages
require data on the time scale of transmission (i.e. the generation
time, or the serial interval, which is [commonly used as a
proxy](https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0756)
for this). While `{EpiEstim}` focuses on retrospective estimation of
![](https://latex.codecogs.com/png.latex?R_t), `{EpiNow2}` is designed
for both retrospective and real-time estimation.

In estimating ![](https://latex.codecogs.com/png.latex?R_t), one
practical consideration is the impact of various delays (biological and
reporting) on the estimates [(Charniga, Park, et al. 2024; Park et al.
2024; Katelyn M. Gostic 2020)]{.citation
cites="charniga2024best park2024estimating gostic2020practical"}.
`{EpiNow2}` adjusts for these delays in various ways. For example, it
accounts for the symptom onset and reporting delays by taking the
incubation period and reporting delay as inputs. Moreover, `{EpiNow2}`
can estimate the reporting delay from the data if data on incidence by
date of onset and report are available.

Furthermore, dedicated packages have emerged for estimating
epidemiological delays from data using best practices.
[`{epidist}`](https://epidist.epinowcast.org/articles/epidist.html)
offers the ability to estimate delay distributions, accounting for
issues such as truncation (i.e., not all disease outcomes will yet be
known in real-time).

If delay data are not available, published estimates of the incubation
period and serial interval can be used. The
[`{epiparameter}`](https://epiverse-trace.github.io/epiparameter/)
package collates a database of epidemiological distributions from the
literature and provides functions for interacting with the database. You
can view the
[database](https://epiverse-trace.github.io/epiparameter/dev/articles/database)
for currently available parameters (more entries are planned).
Additionally, if only summary statistics are available (e.g. range and
median), `{epiparameter}` can be used to extract the distribution
parameters.
:::

::::::: {#individual-level-transmissibility-superspreading .section .level4}
#### Individual-level transmissibility (superspreading) {.anchored anchor-id="individual-level-transmissibility-superspreading"}

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The individual-level transmission heterogeneity (superspreading), often
denoted as ![](https://latex.codecogs.com/png.latex?k), is an important
measure for tailoring interventions at the individual level.
:::
:::::
::::::

If we have data on the distribution of sizes of transmission clusters,
the [`{epichains}`](https://epiverse-trace.github.io/epichains/) package
provides functions to set up the likelihood function to estimate
![](https://latex.codecogs.com/png.latex?R_0) and
![](https://latex.codecogs.com/png.latex?k). The user inputs the
negative binomial offspring, which assumes individuals exhibit
heterogeneity in transmission. The parameters of the negative offspring
distribution can then be estimated using existing maximum likelihood or
bayesian frameworks.

Furthermore, if we have individual-level transmission chain data, the
[`{superspreading}`](https://epiverse-trace.github.io/superspreading/)
package can be used to estimate
![](https://latex.codecogs.com/png.latex?R_0) and
![](https://latex.codecogs.com/png.latex?k) from the offspring
distribution. This package also provides functions to estimate the
probability that an outbreak will not go extinct in its early stages
because of randomness in transmission (e.g. if the primary spillover
case(s) does not infect others).

If we have data on sexual contacts and the secondary attack rate, then
we can also use `{superspreading}` to [calculate
![](https://latex.codecogs.com/png.latex?R_0) accounting for network
effects](https://epiverse-trace.github.io/superspreading/articles/heterogeneous_network_outbreaks.html).
:::::::
:::::::::::::

::::::: {#forecasting-and-nowcasting-infection-dynamics .section .level3}
### Forecasting and nowcasting infection dynamics {.anchored anchor-id="forecasting-and-nowcasting-infection-dynamics"}

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::: {.callout-body-container}
Forecasting and nowcasting of infections are crucial for planning and
resource allocation during an outbreak. Forecasting is the prediction of
future cases, deaths, or other outcomes, while nowcasting is the
prediction of the current outbreak situation. These predictions can help
public health authorities to anticipate the trajectory of the outbreak
and to implement timely interventions.
:::
:::::
::::::

[`{EpiNow2}`](https://epiforecasts.io/EpiNow2/) and
[`{epinowcast}`](https://package.epinowcast.org/) provide functions to
forecast and nowcast the number of cases. The data required for
`{EpiNow2}` has already been described in the previous section. The
`{epinowcast}` package similarly requires data on the number of cases
reported per date. `{epinowcast}` does not currently support forecasting
but there are plans to add this functionality in future versions.
:::::::

::::::: {#estimating-disease-severity .section .level3}
### Estimating disease severity {.anchored anchor-id="estimating-disease-severity"}

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The case fatality risk (CFR) is often used to assess the severity of a
disease. CFR here refers to the proportion of deaths among confirmed
cases.
:::
:::::
::::::

With incidence data on the number of cases reported and the number of
deaths reported, the [`{cfr}`](https://epiverse-trace.github.io/cfr/)
package can be used to estimate the case fatality rate and its
uncertainty. Importantly, it accounts for the delay between the onset of
symptoms and death, which is crucial for accurate estimation of the case
fatality rate.

Here again, `{EpiNow2}` can be used to estimate the time-varying case
fatality ratio using the same data as for the reproduction number.
`{EpiNow2}` can estimate other severity metrics, such as the case
hospitalisation ratio, given data on cases and hospitalisations, and the
hospitalisation fatality ratio, if data on hospitalisations and
associated deaths are available.
:::::::

::::::: {#assessing-the-impact-of-interventions .section .level3}
### Assessing the impact of interventions {.anchored anchor-id="assessing-the-impact-of-interventions"}

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mpox can be mitigated with behaviour change, treatment, and vaccination.
Here, a few tools are available to assess the impact of intervention
scenarios.
:::
:::::
::::::

[`{epidemics}`](https://epiverse-trace.github.io/epidemics/) provides
ready compartmental models to estimate the impact of vaccination and
non-pharmaceutical interventions like behaviour change, which can
conceptually be modelled as a reduction in the transmission rate through
changes in the population contact structure.

If we want to explore population-level outbreak dynamics, `{epidemics}`
allows for stratifying the population into arbitrary groups, specifying
the contact structure between these groups, and rates of interventions.
The data required to run these models include: population structure,
contact structure, and timing and magnitude of interventions. Data on
social contact matrices can be obtained from the
[`{socialmixr}`](https://github.com/epiforecasts/socialmixr) package.
:::::::
::::::::::::::::::::::::::::::::::

::: {#summary .section .level2}
## Summary {.anchored anchor-id="summary"}

In this post, we have outlined common outbreak analytics tasks relevant
to the mpox outbreak, the data required, and R packages/tools that are
currently available to facilitate these tasks. The tools described here
are being developed by the Epiverse-TRACE team and the wider community,
with the aim of ensuring high standards of research software
development, and validation from end users, including epidemiologists,
clinicians, and policy makers. The tools are designed to be
user-friendly and well integrated, enabling one analysis task to easily
feed into another. We [would therefore be keen to
hear](https://epiverse-trace.github.io/get-involved.html) from other
groups interested in potentially collaborating or contributing on this
growing ecosystem of tools.

*Thanks to [Karim Mane](https://github.com/Karim-Mane) and [Chris
Hartgerink](https://github.com/chartgerink) for their valuable comments
on earlier drafts of this post.*
:::

:::::::::::::::::::: {#quarto-appendix .default}
:::::::::: {#quarto-bibliography .section .quarto-appendix-contents}
## References {#references .anchored .quarto-appendix-heading}

::::::::: {#refs .references .csl-bib-body .hanging-indent entry-spacing="0"}
::: {#ref-charniga2024updating .csl-entry}
Charniga, Kelly, Andrea M McCollum, Christine M Hughes, Benjamin Monroe,
Joelle Kabamba, Robert Shongo Lushima, Toutou Likafi, et al. 2024.
"Updating Reproduction Number Estimates for Mpox in the Democratic
Republic of Congo Using Surveillance Data." *The American Journal of
Tropical Medicine and Hygiene* 110 (3): 561.
https://doi.org/<https://doi.org/10.4269/ajtmh.23-0215>.
:::

::: {#ref-charniga2024best .csl-entry}
Charniga, Kelly, Sang Woo Park, Andrei R Akhmetzhanov, Anne Cori,
Jonathan Dushoff, Sebastian Funk, Katelyn M Gostic, et al. 2024. "Best
Practices for Estimating and Reporting Epidemiological Delay
Distributions of Infectious Diseases Using Public Health Surveillance
and Healthcare Data." *arXiv Preprint arXiv:2405.08841*.
https://doi.org/
[https://doi.org/10.48550/arXiv.2405.08841](%0Ahttps://doi.org/10.48550/arXiv.2405.08841).
:::

::: {#ref-gostic2020practical .csl-entry}
Katelyn M. Gostic, Edward B. Baskerville, Lauren McGough. 2020.
"Practical Considerations for Measuring the Effective Reproductive
Number, Rt." *PLoS Computational Biology* 16 (12): e1008409.
https://doi.org/<https://doi.org/10.1371/journal.pcbi.1008409>.
:::

::: {#ref-laurenson2023description .csl-entry}
Laurenson-Schafer, Henry, Nikola Sklenovská, Ana Hoxha, Steven M Kerr,
Patricia Ndumbi, Julia Fitzner, Maria Almiron, et al. 2023. "Description
of the First Global Outbreak of Mpox: An Analysis of Global Surveillance
Data." *The Lancet Global Health* 11 (7): e1012--23.
https://doi.org/<https://doi.org/10.1016/S2214-109X(23)00198-5>.
:::

::: {#ref-park2024estimating .csl-entry}
Park, Sang Woo, Andrei R Akhmetzhanov, Kelly Charniga, Anne Cori,
Nicholas G Davies, Jonathan Dushoff, Sebastian Funk, et al. 2024.
"Estimating Epidemiological Delay Distributions for Infectious
Diseases." *medRxiv*, 2024--01.
https://doi.org/<https://doi.org/10.1101/2024.01.12.24301247>.
:::

::: {#ref-ward2022transmission .csl-entry}
Ward, Thomas, Rachel Christie, Robert S Paton, Fergus Cumming, and
Christopher E Overton. 2022. "Transmission Dynamics of Monkeypox in the
United Kingdom: Contact Tracing Study." *Bmj* 379.
https://doi.org/[http://dx.doi.org/10.1136/
bmj-2022-073153](http://dx.doi.org/10.1136/%20bmj-2022-073153).
:::
:::::::::
::::::::::

::: {#footnotes .section .footnotes .footnotes-end-of-document}
## Footnotes {.anchored .quarto-appendix-heading}

1.  ::: {#fn1}
    [WHO Disease Outbreak
    News](https://www.who.int/emergencies/disease-outbreak-news/item/2024-DON522)↩︎
    :::

2.  ::: {#fn2}
    [Modeling Household Transmission of Clade I Mpox in the United
    States](https://www.cdc.gov/forecast-outbreak-analytics/about/modeling-forecasting/mpox-transmission.html)↩︎
    :::

3.  ::: {#fn3}
    [Risk of Clade 1 Mpox Outbreaks Among Gay, Bisexual, and Other Men
    Who Have Sex With Men in the United
    States](https://www.cdc.gov/forecast-outbreak-analytics/about/modeling-forecasting/mpox-gbmsm-technical-brief.html)↩︎
    :::
:::

::::: {#quarto-reuse .section .quarto-appendix-contents}
## Reuse {#reuse .anchored .quarto-appendix-heading}

:::: {.quarto-appendix-contents}
<div>

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/){rel="license"}

</div>
::::
:::::

::::::: {#quarto-citation .section .quarto-appendix-contents}
## Citation {#citation .anchored .quarto-appendix-heading}

<div>

::: {.quarto-appendix-secondary-label}
BibTeX citation:
:::

``` {.sourceCode .code-with-copy .quarto-appendix-bibtex}
@online{azam2024,
  author = {Azam, James and Gruson, Hugo and Kucharski, Adam},
  title = {2024 Mpox Outbreak: Common Analytics Tasks and Available {R}
    Tools},
  date = {2024-07-04},
  url = {https://epiverse-trace.github.io/posts/mpox-preparedness/},
  langid = {en}
}
```

::: {.quarto-appendix-secondary-label}
For attribution, please cite this work as:
:::

::: {#ref-azam2024 .csl-entry .quarto-appendix-citeas}
Azam, James, Hugo Gruson, and Adam Kucharski. 2024. "2024 Mpox Outbreak:
Common Analytics Tasks and Available R Tools." July 4, 2024.
<https://epiverse-trace.github.io/posts/mpox-preparedness/>.
:::

</div>
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