RT Journal Article
SR Electronic
T1 nosoi: a stochastic agent-based transmission chain simulation framework in R
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.03.03.973107
DO 10.1101/2020.03.03.973107
A1 Sebastian Lequime
A1 Paul Bastide
A1 Simon Dellicour
A1 Philippe Lemey
A1 Guy Baele
YR 2020
UL http://biorxiv.org/content/early/2020/03/04/2020.03.03.973107.abstract
AB The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavor, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed. We here introduce nosoi, an open-source R package that offers a complete, tunable, and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations. Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can – for example – be used to validate a wide range of reconstruction methods, including epidemic modeling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a handson exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.