This is a dynamic version of Table 2 from the review “Real-time estimation of the reproduction number: scoping review of the applications and challenges” by Nash et al (currently under review - the URL will be made available here upon publication). This table summarises the R packages and tool identified in the paper that modify the approach used in EpiEstim or have incorporated additional features, such as the ability to estimate the reproduction number for multiple regions simultaneously. We intend for this table to be updated once new packages or package versions are released and hope this will help guide users when deciding which software would be most suitable for their specific data or analysis. If you are the author(s) of any of these R packages or tool and wish to edit or update this table please feel free to contact us or create a pull request.
The ticks (✓) indicate whether an R package/tool includes that modification type. The final 3 rows summarise additional exploration by the authors of the paper to assess how easily each package/tool can be installed and used. Full details of how each classification (very good = ✓✓, good = ✓, poor = ✗) was defined, will be made available in the supplementary material of the Nash et al paper upon publication (Table S2 and the associated text).
| Theme | Modification Type | APEestim | bayEStim | earlyR | epicontacts | Epidemia | EpiFilter | EpiNow2 |
|---|---|---|---|---|---|---|---|---|
| Incidence | Account for case reporting delay or missing data | ✓ | ✓ | ✓ | ||||
| Incidence | Remove weekly administrative noise | ✓ | ||||||
| Other input data or method modification | Low incidence or early R estimation | ✓ | ✓ | |||||
| Other input data or method modification | Alternative prior | ✓ | ✓ | ✓ | ||||
| Other input data or method modification | Alternative way of temporal smoothing | ✓ | ✓ | ✓ | ✓ | |||
| Other input data or method modification | Alternative method to estimate the SI | ✓ | ||||||
| Geographical factors | Model different regions simultaneously | ✓ | ✓ | |||||
| Practical/logistical | Extrinsic factors | ✓ | ||||||
| Practical/logistical | Disease elimination | ✓ | ||||||
| Full details of how the classifications (very good = ✓✓, good = ✓, poor = ✗) were defined can be found in the supplementary material of the Nash et al paper. | ||||||||
| Usability | Ease of installation | ✓ | ✗ | ✓✓ | ✓✓ | ✗ | ✗ | ✓ |
| Usability | Documentation and tutorials | ✗ | ✗ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| Usability | Speed of R estimation* (*or SI estimation for epicontacts) | ✓✓ | NA | ✓✓ | ✓✓ | ✓ | ✓✓ | ✗ |
| Note: | ||||||||
|
The CRAN package documentation or github repository for
these R packages or tool* can be found by following the links
below: APEestim (v 0.0.1): https://github.com/kpzoo/APEestim bayEStim (v 0.0.1): https://github.com/thlytras/bayEStim earlyR (v 0.0.5): https://CRAN.R-project.org/package=earlyR epicontacts (v 1.1.2): https://CRAN.R-project.org/package=epicontacts Epidemia (v 1.0.0): https://github.com/ImperialCollegeLondon/epidemia *EpiFilter: https://github.com/kpzoo/EpiFilter EpiNow2 (v 1.3.2): https://CRAN.R-project.org/package=EpiNow2 |
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