outbreaker2: a modular platform for outbreak reconstruction

Abstract

BackgroundReconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort.ResultsHere we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort.Conclusionsoutbreaker2 provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2 joins a growing body of tools for outbreak analysis.

Probabilistic reconstruction of measles transmission clusters from routinely collected surveillance data

“…We wrote the R package o2geosocial to conduct inference on individual-level data using this model. It is based on the package outbreaker2 and is designed for outbreaks with partial sampling of cases, or uninformative genetic sequences, such as measles outbreaks [9,39]. We used the likelihood of transmission links between different cases to estimate their importation status.…”

nosoi: A stochastic agent‐based transmission chain simulation framework in r

“…In that context, a simulated dataset is extremely useful as the exact transmission history is known and can be compared to the histories inferred from different software packages. The last decade has seen the development of several integrated epidemic and genetic simulation tools that can be used to assess the performance of some of these models, such as TreeSim (Stadler & Bonhoeffer, 2013), Seedy (Worby & Read, 2015), ouTbreaker2 (Campbell et al, 2018) or faviTeS (Moshiri, Ragonnet-Cronin, Wertheim, & Mirarab, 2019).…”

Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016

“…Much work has been done on developing algorithms to identify transmission clusters of cases using large datasets [1]. Existing algorithms focus on cluster identification in time [2][3][4][5][6][7][8][9], in space or space-time [10][11][12], in genetics [13][14][15], or by combining all three data dimensions [16][17][18].…”