ArticleMethods in ecology and evolution2020
nosoi: A stochastic agent-based transmission chain simulation framework in r.
Article in Methods in ecology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- e3SIM: Epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.Methods in ecology and evolution · 2026Article
- SIMPLICITY is an agent-based, multi-scale mathematical model to study SARS-CoV-2 intra- and between-host evolution.Communications biology · 2026Article
- Dengue-mediated changes in the vectorial capacity of Aedes aegypti (Diptera: Culicidae): manipulation of transmission or infection by-product?Journal of medical entomology · 2025Review
- Modeling the velocity of evolving lineages and predicting dispersal patterns.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Modeling the velocity of evolving lineages and predicting dispersal patterns.bioRxiv : the preprint server for biology · 2024Article
- DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.PLoS computational biology · 2024Article
- Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics.Bioinformatics advances · 2024Article
- Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission.PLoS computational biology · 2022Article
- VGsim: Scalable viral genealogy simulator for global pandemic.PLoS computational biology · 2022Article
- VGsim: scalable viral genealogy simulator for global pandemic.medRxiv : the preprint server for health sciences · 2021Article
- SARS-CoV-2 shifting transmission dynamics and hidden reservoirs potentially limit efficacy of public health interventions in Italy.Communications biology · 2021Article
- Recent African strains of Zika virus display higher transmissibility and fetal pathogenicity than Asian strains.Nature communications · 2021Article
- Modeling intra-mosquito dynamics of Zika virus and its dose-dependence confirms the low epidemic potential of Aedes albopictus.PLoS pathogens · 2020Article
- nosoi: A stochastic agent-based transmission chain simulation framework in r.Methods in ecology and evolution · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
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 endeavour, 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 modelling 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 hands-on 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.
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