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pathogen.jl
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#685206
Simulation, visualization, and inference tools for modelling the spread of infectious diseases with Julia
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EIDM »
EIDM
EIDM ☜The Emerging Infectious Diseases Modelling Initiative (EIDM) – by the Public Health Agency of Canada and NSERC – aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. [1]☜F1CEB7
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pathogen.jl
pathogen.jl☜Simulation, visualization, and inference tools for modelling the spread of infectious diseases with Julia☜C78CB7
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Julia »
Julia
Julia☜☜FFFACD
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Data analysis »
Data analysis
Data analysis☜☜FFFACD
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Fitting dynamical models to data »
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Rob Deardon »
Rob Deardon
Rob Deardon☜Associate Professor in the Department of Production Animal Health in the Faculty of Veterinary Medicine and the Department of Mathematics and Statistics in the Faculty of Science at the University of Calgary.☜FFFACD
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Simulation »
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Pathogens »
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Pathogens☜☜FFFACD
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+Citations (
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[1]
pathogen.jl
Cited by:
David Price
10:11 PM 26 September 2021 GMT
URL:
https://github.com/jangevaare
Link
[2]
Pathogen.jl: Infectious Disease Transmission Network Modelling with Julia
Author:
Justin Angevaare, Zeny Feng, Rob Deardon
Publication date:
25 August 2021
Publication info:
arXiv:2002.05850v3
Cited by:
David Price
2:45 PM 15 September 2022 GMT
URL:
https://debategraph.org/handler.ashx?path=ROOT%2fu2928%2f2002.05850.pdf&att=1
DOI:
https://doi.org/10.48550/arXiv.2002.05850
Excerpt / Summary
We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs can be used to jointly infer transmission networks, event times, and model parameters within a Bayesian framework via Markov chain Monte Carlo (MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and our implementation of these strategies in the Julia package, Pathogen.jl, which leverages key features of the Julia language. We provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, and then perform inference using observations that were generated from that epidemic. We also demonstrate the functionality of Pathogen.jl with an application of TN-ILMs to data from a measles outbreak that occurred in Hagelloch, Germany in 1861(Pfeilsticker 1863; Oesterle 1992).
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David Price
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#685206
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Entry date (GMT):
9/26/2021 9:21:00 PM
Last edit date (GMT):
9/26/2021 9:21:00 PM
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