nosoi

The aim of nosoi (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora’s jar in the Greek mythology. nosoi is able to take into account the influence of multiple variable 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.

Installation

To get the current released version from CRAN:

To get the latest (and possibly unstable) version, you can use the devtools package:

install.packages("devtools")
devtools::install_github(repo = "slequime/nosoi")

Documentation

You can find package documentation, with reference, tutorials and examples here: http://slequime.github.io/nosoi/ (built with pkgdown).

nosoi usage in published manuscripts

  • Lequime et al. (2020) Modeling intra-mosquito dynamics of Zika virus and its dose-dependence confirms the low epidemic potential of Aedes albopictus. PLoS Pathogens 16(12):e1009068. doi:10.1371/journal.ppat.1009068
  • Aubry, Jacobs & Darmuzey et al. (2021) Recent African strains of Zika virus display higher transmissibility and fetal pathogenicity than Asian strains. Nature Communications 12:916 doi:10.1038/s41467-021-21199-z
  • Giovanetti et al. (2021) SARS-CoV-2 shifting transmission dynamics and hidden reservoirs potentially limit efficacy of public health interventions in Italy. Communications Biology 4:489 doi:10.1038/s42003-021-02025-0
  • Goldstein et al. (2022) Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. PLoS Computational Biology 18(12): e1010696 doi:10.1371/journal.pcbi.1010696
  • Marini et al. (2022) Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for phylogenetics. Bioinformatics 38(3):856–860 doi:https://doi.org/10.1093/bioinformatics/btab725
  • Vignier et al. (2023) Chikungunya intra-vector dynamics in Aedes albopictus from Lyon (France) upon exposure to a human viremia-like dose range reveals vector barrier’s permissiveness and supports local epidemic potential. PCI Journal 3:e96 doi:10.24072/pcjournal.326
  • Bastide et al. (2024) Modeling the velocity of evolving lineages and predicting dispersal patterns. Proceedings of the National Academy of Sciences 121(47):e2411582121 doi:10.1073/pnas.2411582121
  • Magalis et al. (2024) Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods Infection, Genetics and Evolution 124:105661 doi:10.1016/j.meegid.2024.105661
  • Sun et al. (2024) DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction. PLoS Computational Biology 20(4): e1011351 doi:10.1371/journal.pcbi.1011351
  • Sun et al. (2024) Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics. Bioinformatics Advances 4(1):vbae158 doi:10.1093/bioadv/vbae158