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TimTam

There is a companion R package timtamslamR designed to help with timtam.

Installation

There are instructions for the installation of TimTam here.

Tutorials

• Constant Parameters considers a constant parameter birth-death-sampling model.
• Constant Parameters with calendar dates considers a constant parameter birth-death-sampling model where observations have dates: YYYY-MM-DD.
• Constant Parameters and R0 is similar to the tutorial above, but considers an alternative parameterization.
• Constant Parameters and historical prevalence extends the R0 example above to include estimation of historical prevalence.
• Variable Parameters for an SIR Epidemic I extends the history example above to include estimation with a changing R0 to model an epidemic.
• Variable Parameters for an SIR Epidemic II extends the previous example to a model in which there was a delay between the start of the outbreak and the start of surveillance.

While working through the tutorials, it might be helpful to have the BEAST2 XML documentation handy.

Glossary

Where possible this Wiki should use the terminology outlined in this glossary.

Related work

• Manceau et al (2021) The probability distribution of the ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data
• Andréoletti et al (2022) The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology

Citing Timtam

If you use Timtam as part of research that leads to a publication, we ask that you cite the following article:

Zarebski AE, du Plessis L, Parag KV, Pybus OG (2022) A computationally tractable birth-death model that combines phylogenetic and epidemiological data. PLOS Computational Biology 18(2): e1009805. https://doi.org/10.1371/journal.pcbi.1009805

Acknowledgements

• Thank you to Bernardo Gutierrez for carefully working through the tutorials, and providing numerous corrections.
• This would not have been accomplished without the assistance of my co-authors: Louis du Plessis, Kris V. Parag, and Oliver G. Pybus.
• Thank you to Yunjun Zhang from Peking University for reading through and helping to improve documentation on an earlier version of this software.
• Thank you to Tim Vaughan from ETH Zürich for providing helpful advice on numerous occasions.