Evidence map›Paper›PMID 32983401›Full record

ArticleMethods in ecology and evolution2020

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

Sebastian Lequime, Paul Bastide, Simon Dellicour, Philippe Lemey, Guy Baele

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Modeling the velocity of evolving lineages and predicting dispersal patterns.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. VGsim: scalable viral genealogy simulator for global pandemic.medRxiv : the preprint server for health sciences · 2021
    Article
  11. Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sebastian LequimeDepartment of Microbiology, Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium.ORCID https://orcid.org/0000-0002-3140-0651
Paul BastideDepartment of Microbiology, Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium.ORCID https://orcid.org/0000-0002-8084-9893
Simon DellicourDepartment of Microbiology, Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium.ORCID https://orcid.org/0000-0001-9558-1052
Philippe LemeyDepartment of Microbiology, Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium.ORCID https://orcid.org/0000-0003-2826-5353
Guy BaeleDepartment of Microbiology, Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium.ORCID https://orcid.org/0000-0002-1915-7732

Funding

Wellcome Trust
6 · The paper itself

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.

Indexed as

agent‐based simulationinfectious diseasepathogenr packagesimulatorstochastic modeltransmission chain

Identifiers

PMID32983401
PMCPMC7496779

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.