Epidemiological Agent-Based Modelling Software (Epiabm)
References
- Adam D. Special report: The simulations driving the world’s response to COVID-19. Nature. Apr. 2020; 580(7803): 316–318. DOI: 10.1038/d41586-020-01003-6
- Birrell P, et al. Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave. Philosophical Transactions of the Royal Society B: Biological Sciences. 2021; 376(1829). DOI: 10.1098/rstb.2020.0279
- Crick T, Hall BA, Ishtiaq S. Reproducibility in Research: Systems, Infrastructure, Culture. Journal of Open Research Software. Nov. 2017; 5. DOI: 10.5334/jors.73
- Danon L, et al. A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality. Philosophical Transactions of the Royal Society B: Biological Sciences. May 2021; 376(1829). DOI: 10.1098/rstb.2020.0272
- Davies NG, et al. Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. The Lancet Public Health. 2020; 5(7): e375–e385. ISSN: 2468–2667. DOI: 10.1016/S2468-2667(20)30133-X
- Eglen SJ. CODECHECK certificate 2020-010. en (2020). DOI: 10.5281/ZENODO.3865490
- Ferguson NM, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand; 2020. DOI: 10.25561/77482
- Ferguson NM, Nedjati Gilani G, Laydon, D. COVID-19 CovidSim microsimulation model. Computer Software. R Foundation for Statistical Computing; Apr. 2020.
- Ferguson NM, et al. Strategies for mitigating an influenza pandemic. Nature. Apr. 2006. 442(7101): 448–452. DOI: 10.1038/nature04795
- He S, Peng Y, Sun K. SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dynamics. June 2020; 101(3): 1667–1680. DOI: 10.1007/s11071-020-05743-y
- Hunter E, Mac Mamee B, Kelleher JD. A Comparison of Agent-Based Models and Equation Based Models for Infectious Disease Epidemiology; 2018. DOI: 10.18564/jasss.3414
- Keeling MJ, et al. Predictions of COVID-19 dynamics in the UK: Shortterm forecasting and analysis of potential exit strategies. PLOS Computational Biology. Jan. 2021. 17(1): 1–20. DOI: 10.1371/journal.pcbi.1008619
- Lyra W, et al. COVID-19 pandemics modeling with modified determinist SEIR, social distancing, and age stratification. The effect of vertical confinement and release in Brazil. PLOS ONE. Sept. 2020. 15(9):
e0237627 , Samy AM (ed.). DOI: 10.1371/journal.pone.0237627 - Shen C, Taleb NN, Bar-Yam Y. Review of Ferguson et al. ‘Impact of non-pharmaceutical interventions…’ New England Complex Systems Institute; Mar. 2020.
- Stodden V, Miguez S. Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research. Journal of Open Research Software. July 2014; 2(1). DOI: 10.5334/jors.ay
- Wooldridge M. The Software that Led to the Lockdown. July 2020.
- Wu JT, Cowling BJ. The use of mathematical models to inform influenza pandemic preparedness and response. Experimental Biology and Medicine. Aug. 2011; 236(8): 955–961. DOI: 10.1258/ebm.2010.010271
DOI: https://doi.org/10.5334/jors.449 | Journal eISSN: 2049-9647
Language: English
Submitted on: Nov 24, 2022
Accepted on: Feb 2, 2024
Published on: Mar 5, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2024 Kit Gallagher, Ioana Bouros, Nicholas Fan, Elizabeth Hayman, Luke Heirene, Patricia Lamirande, Annabelle Lemenuel-Diot, Ben Lambert, David Gavaghan, Richard Creswell, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.