Packages for simulating, analysing, and handling transmission chains

[jamesmbaazam]

As far as we know, below are the R packages that are the most related to bpmodels in terms of simulating and handling transmission chains. If you are aware of any that are not on this list, please feel free to link them in the comments below.

It is our vision to make these packages interoperable in the near future. If you are interested in contributing to this vision in any way, do reach out either by email (james.azam@lshtm.ac.uk) or comment below.

1. epicontacts: handling, analysing, and visualizing transmission chains and contact tracing data/linelists.
2. simulacr: simulates outbreaks for specified values of reproduction number, incubation period, duration of infectiousness, and optionally reporting delays. Outputs a linelist stored as a data.frame with the class outbreak, including information on transmission chains; the output can be converted to epicontacts objects for visualisation, and plotted using plot(...).
3. ringbp: a branching process model, parameterised to the 2019-nCoV outbreak, and used to quantify the potential effectiveness of contact tracing and isolation of cases.
4. outbreakr: implements a Bayesian approach for reconstructing outbreak data from pathogen genome sequences. It also implements a tool for outbreak simulation.
5. outbreakr2: a Bayesian framework for integrating epidemiological and genetic data to reconstruct transmission trees of densely sampled outbreaks. It re-implements, generalises and replaces the model of outbreaker, and uses a modular approach which enables fine customisation of priors, likelihoods and parameter movements.
6. o2geosocial: integrates geographical and social contact data to reconstruct transmission chains. It combines the age group, location, onset date and genotype of cases to infer their import status, and their likely infector.
7. nosoi: simulates agent-based transmission chains by taking into account the influence of multiple variables on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
8. covidhm: code for simulating COVID-19 dynamics in a range of scenarios across a real-world social network. Model is conceptually based on ringbp.