Epiverse community involvement and software sustainability for research software | R-Bloggers

The R -PACKAGE

The R package in question is {Ringbp}. The package has two pieces of functionality: 1) to simulate an outbreak of infectious diseases using a branching process model with non-pharmaceutical interventions; and 2) to calculate the share of simulated outbreaks that have been included (ie not a large persistent human-to-human epidemic). The usefulness of the general model framework of the package has been demonstrated by serving as a template for other epidemiological examination such as such as such as Prophylaxis post-exposure” Network effects on control (Firth et al. 2020) and the Impact of self-reporting and isolation therapy compliance (Davis et al. 2020).

The problem

It is understandable that because {ringbp} has been hurriedly written to produce insights to inform Pandemic Response, it did not hold on to all best practices for software. Usability, documentation, testing, code style and (computational) performance can be improved. Certain aspects of model code, such as parameterizations, were hard coded, so that users did not offer full flexibility that could allow the model.

Epiverse

In recent months, epiverse has collaborated with {ringbp} developers Seb Funk (also a member of Epiverse) and Carl Pearson (external employee), based on the London School of Hygiene and Tropical Medicine and University of North Carolina, to try the R-package, both users. The following sections provide short summaries of some of the cooperation developments.

scenario_sim(
  n.sim = 5,
  num.initial.cases = 5,
  cap_max_days = 365,
  cap_cases = 2000,
  r0isolated = 0,
  r0community = 2.5,
  disp.iso = 1,
  disp.com = 0.16,
  k = 0.7,
  delay_shape = 2.5,
  delay_scale = 5,
  prop.asym = 0,
  prop.ascertain = 0
)

scenario_sim(
 n = 5,
 initial_cases = 5,
 offspring = offspring_opts(
   community = \(n) rnbinom(n = n, mu = 2.5, size = 0.16),
   isolated = \(n) rnbinom(n = n, mu = 0, size = 1),
   asymptomatic = \(n) rnbinom(n = n, mu = 2.5, size = 0.16)
  ),
 delays = delay_opts(
   incubation_period = \(n) rweibull(n = n, shape = 2.32, scale = 6.49),
   onset_to_isolation = \(n) rweibull(n = n, shape = 2.5, scale = 5)
  ),
 event_probs = event_prob_opts(
   asymptomatic = 0,
   presymptomatic_transmission = 0.3,
   symptomatic_ascertained = 0
  ),
 interventions = intervention_opts(quarantine = TRUE),
 sim = sim_opts(
   cap_max_days = 365,
   cap_cases = 2000
  )
)

The new API gives the user more control over the parameterization of the model. The incubation period is now specified by the user instead of being set to an estimate for COVID-19. The way in which descendants and delay distribution functions are specified also means that any distributional or non-parametric shape can be supplied, so that the assumption is relaxed that the beginning to any insulation must be a Weibull distribution.

Users can now specify the share of presymptomatic transmission instead of the skewed normal parameterization that is used by the simulation model, making it easier to get started with the new users package.

Finally, the changes in user -oriented changes, naming and style of job arguments are standardized for consistent use of snakecase Style and abbreviations.

@param sim a `list` with class ``: the simulation control
  options for the \pkg{ringbp} model, returned by [sim_opts()]

Conclusion

The {ringbp} r package implements a simple but informative model for transmission and interventions of infectious diseases. When it was originally written, it included many well -developed aspects, but the time limitations of real -time outbreak response meant that different improvements were possible.

Epiverse-Trace has the opportunity to not only develop new tooling for pandemic readiness and reaction, but also to contribute to the ecosystem of open-source software in epidemiology of infectious diseases. We hope that by covering the cooperation developments of {ringbp}, it can illustrate the benefits of making software with best practices, and making tools available, accessible and robust when a new epidemic or pandemic occurs, hopefully the need for redeveloping similar software in the future.

Improving the accessibility of software for users and developers by improving documentation and user interface will hopefully offer a gateway for more external contributors to enter into the project. In the public health landscape of temporary stimulus in capacity and priorities, it could be better to enable community contributions to open-source software, to support the sustainability of the software.

All changes discussed in this blog post can be found in the {Ringbp} News. For more information about developments, see the Trek request history of {ringbp} on github.

Recognition

Thanks to Seb Funk and Carl Pearson for useful feedback in the preparation of this message and for their collaboration on the {Ringbp} project.