CovidSim

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CovidSim
Original author(s)Neil Ferguson
Repositoryhttps://github.com/mrc-ide/covid-sim
Written inC++
LicenseGNU General Public License v3.0

CovidSim is an epidemiological model for COVID-19 developed by Imperial College COVID-19 Response Team, led by Neil Ferguson.[1] The Imperial College study addresses the question: If complete suppression is not feasible, what is the best strategy combining incomplete suppression and control that is feasible and leads to acceptable outcomes?[2]

History[edit]

CovidSim is an agent-based model and was based on an earlier influenza model.[3]

The codebase for the model was initially constructed c. 2005.[4]

Informing policy decisions[edit]

For UK Prime Minister Boris Johnson,[4] it was, according to David Adam writing in The Atlantic, "a critical factor in jolting the UK government into changing its policy on the pandemic" and order a nationwide lockdown to limit the spread of the Coronavirus.[5][1][6][7]

Software[edit]

In May 2020, a C++ derivative of the code was released to GitHub.[8]

As of May 2023, the current release tag is v0.15.0.[9] Additionally, an Anaconda package exists with release v0.8.0[10]

The software should be distinguished from the ICL's COVID-19 Scenario Analysis Tool (currently Version 4[11]), which is hosted under the domain name https://www.covidsim.org, but according to the research documentation is relying on the model combined with a squire model, which is the underlying transmission model in the absence of vaccination.[12][13] Further details are available under ICL's Report 33.[14]

Code reviews and expert opinions[edit]

Note that the mentioned model ships with and is marked with a list of warnings and user information, e.g. no support, stochastic nature/kernel, criticality of input parameters etc.[15][16]

Soundness[edit]

American programmer John Carmack said in April 2020 that he worked on the code before it was released to the public, when it was a single 15,000-line C programming language file and "some of the functions looked like they were machine translated from Fortran", but that "it fared a lot better going through the gauntlet of code analysis tools I hit it with than a lot of more modern code".[17][18]

Shortcomings[edit]

New Scientist reported in March 2020 that one group from the New England Complex Systems Institute reviewing the model suggested that it contained "systematic errors".[19][20] British newspaper The Telegraph reported that some software engineers who reviewed the new code called it "totally unreliable" and a "buggy mess".[21]

In the opinion of University of Oxford computer scientist Michael Wooldridge, the code was "developed without the ceremony and rigor" of professional products, which is not untypical for research software and often intended to be not understood by third parties, or to be reused; and "while the extensive criticism about relaxed software engineering practices is perhaps justified, it was not fundamentally flawed".[22]

Model characteristics[edit]

Reproducibility[edit]

An independent review by Codecheck led by Dr Stephen Eglen of the University of Cambridge confirmed that they were able to reproduce the key findings from the response team's report by using the software.[17][23][24] A June 2020 editorial in Nature declared the original CovidSim codebase met the requirements of scientific reproducibility.[25]

Uncertainty[edit]

Further research exists to identify the following three sources of uncertainty in the simulation:[26][27] parametric uncertainty, model structure uncertainty and scenario uncertainty:[28] The simulation output depends critically on the inputs and can change up to 300% based on 940 parameters, of which 19 are considered most sensitive. Model structure and scenario uncertainty must therefore be understood.[28]

The results obtained by Imperial using the model are consistent with other models that make similar assumptions.[2]

Extensibility[edit]

Calibration of the model has been hampered by the lack of testing, especially the poor understanding of the prevalence of asymptomatic infection, however the Imperial College team makes reasonable assumptions. The model's reliance on a simplified picture of social interactions limits its extensibility to counterfactuals. The general nature of conclusions based on such a model can be expected to be similar to those of a simple compartmental model.[2]

Other applications, related or follow-up research[edit]

  • Additional research is based on the model, e.g. for simulation of effect of school closures on mortality.[29]
  • Wouter Edeling et al. contributed a FabSim3 plug-in called FabCovidSim,[30][31] which is based on EasyVVUQ, a Python 3 library to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations.[32][33]
  • In a recent publication on MedRxiv, which was now accepted by BMJ Open by Laydon et al., the authors utilize the model to "Measure the effects of the Tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern" and conclude that "...interventions at least as stringent as Tier 3 are required to suppress transmission, especially considering more transmissible variants, at least until effective vaccination is widespread or much greater population immunity has amassed."[34]

See also[edit]

References[edit]

  1. ^ a b Adam D (April 2020). "Special report: The simulations driving the world's response to COVID-19". Nature. 580 (7803): 316–318. Bibcode:2020Natur.580..316A. doi:10.1038/d41586-020-01003-6. PMID 32242115. S2CID 214771531.
  2. ^ a b c Eubank S, Eckstrand I, Lewis B, Venkatramanan S, Marathe M, Barrett CL (April 2020). "Commentary on Ferguson, et al., "Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand"". Bulletin of Mathematical Biology. 82 (4): 52. doi:10.1007/s11538-020-00726-x. PMC 7140590. PMID 32270376. Text was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
  3. ^ Panovska-Griffiths J, Kerr CC, Waites W, Stuart RM (January 2021). "Mathematical modeling as a tool for policy decision making: Applications to the COVID-19 pandemic". Handbook of Statistics. 44: 291–326. doi:10.1016/bs.host.2020.12.001. ISBN 9780323852005. PMC 7857083.
  4. ^ a b Bostock B (April 25, 2020), "How 'Professor Lockdown' helped save tens of thousands of lives worldwide — and carried COVID-19 into Downing Street", Business Insider
  5. ^ "Report 9 - Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand". Imperial College London. Retrieved 2021-03-10.
  6. ^ Kelland K (March 17, 2020). "Sobering coronavirus study prompted Britain to toughen its approach". Reuters.
  7. ^ Tufekci Z (April 2, 2020). "Don't Believe the COVID-19 Models.That's not what they're for". The Atlantic.
  8. ^ mrc-ide/covid-sim, MRC Centre for Global Infectious Disease Analysis, 2021-03-08, retrieved 2021-03-09
  9. ^ "Release v0.15.0 · mrc-ide/covid-sim". GitHub. Retrieved 2021-04-05.
  10. ^ "Covid Sim :: Anaconda.org". anaconda.org. Retrieved 2021-04-05.
  11. ^ "Covid-19 Scenario Analysis". covidsim.org. Retrieved 2021-04-05.
  12. ^ "SEIR transmission model of COVID-19". mrc-ide.github.io. Retrieved 2021-04-05.
  13. ^ Consulting, Bio Nano. "Covid-19 Scenario Analysis". covidsim.org. Retrieved 2021-04-05.
  14. ^ "Report 33 - Modelling the allocation and impact of a COVID-19 vaccine". Imperial College London. Retrieved 2021-04-05.
  15. ^ "mrc-ide/covid-sim". GitHub. Retrieved 2021-04-05.
  16. ^ "COVID-19 planning tools". Imperial College London. Retrieved 2021-04-05.
  17. ^ a b Scheuber A, van Elsland SL (June 2020). "Codecheck confirms reproducibility of COVID-19 model results". Imperial News. Imperial College London. Retrieved 2021-03-10.
  18. ^ @ID_AA_Carmack (April 27, 2020). "Before the GitHub team started working on the code it was a single 15k line C file that had been worked on for a decade, and some of the functions looked like they were machine translated from Fortran" (Tweet) – via Twitter.
  19. ^ Shen C, Taleb NN, Bar-Yam Y (17 March 2020). "Review of Ferguson et al "Impact of non-pharmaceutical interventions..."". New England Complex Systems Institute. Retrieved 2021-03-13.
  20. ^ Hamzelou J (March 23, 2020), "UK's scientific advice on coronavirus is a cause for concern", New Scientist
  21. ^ Boland H (May 16, 2020). "Coding that led to lockdown was 'totally unreliable' and a 'buggy mess', say experts". The Telegraph. Retrieved May 22, 2020.
  22. ^ Wooldridge M. "The Software that Led to the Lockdown". cacm.acm.org. Retrieved 2021-03-09.
  23. ^ "Codecheck confirms reproducibility of COVID-19 model results". Mirage News. 2 June 2020. Retrieved 6 June 2020.
  24. ^ Eglen SJ (29 May 2020). "CODECHECK certificate 2020-010". Geneva, Switzerland. doi:10.5281/zenodo.3865491. Retrieved 2020-10-14. {{cite journal}}: Cite journal requires |journal= (help) PDF report available.
  25. ^ Singh Chawla D (June 2020). "Critiqued coronavirus simulation gets thumbs up from code-checking efforts" (PDF). Nature. 582 (7812): 323–324. Bibcode:2020Natur.582..323S. doi:10.1038/d41586-020-01685-y. PMID 32546864. S2CID 219700526. Retrieved 2020-10-14.
  26. ^ Winsberg E (2019), "Computer Simulations in Science", in Zalta EN (ed.), The Stanford Encyclopedia of Philosophy (Winter 2019 ed.), Metaphysics Research Lab, Stanford University, retrieved 2021-03-09
  27. ^ Leung K, Wu JT (February 2021). "Quantifying the uncertainty of CovidSim". Nature Computational Science. 1 (2): 98–99. doi:10.1038/s43588-021-00031-0. ISSN 2662-8457.
  28. ^ a b Edeling W, Arabnejad H, Sinclair R, Suleimenova D, Gopalakrishnan K, Bosak B, et al. (February 2021). "The impact of uncertainty on predictions of the CovidSim epidemiological code". Nature Computational Science. 1 (2): 128–135. doi:10.1038/s43588-021-00028-9. hdl:11245.1/4bf2b164-6e12-40aa-93f0-54e41abf1d42.
  29. ^ Rice K, Wynne B, Martin V, Ackland GJ (October 2020). "Effect of school closures on mortality from coronavirus disease 2019: old and new predictions". BMJ. 371: m3588. doi:10.1136/bmj.m3588. PMC 7536648. PMID 33028597.
  30. ^ "An automation toolkit for complex simulation tasks — FabSim3 Initial Release documentation". fabsim3.readthedocs.io. Retrieved 2021-04-02.
  31. ^ Wouter Edeling; Hamid Arabnejad; Robert C Sinclair; Diana Suleimenova; Krishnakumar Gopalakrishnan; Bartosz Bosak; Derek Groen; Imran Mahmood Qureshi Hashmi; Daan Crommelin (2021-01-16), FabCovidSim, doi:10.5281/zenodo.4445290, retrieved 2021-04-02
  32. ^ Richardson, Robin A.; Wright, David W.; Edeling, Wouter; Jancauskas, Vytautas; Lakhlili, Jalal; Coveney, Peter V. (2020-04-29). "EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing". Journal of Open Research Software. 8: 11. doi:10.5334/jors.303. ISSN 2049-9647.
  33. ^ "wedeling/EasyVVUQ". GitHub. Retrieved 2021-04-02.
  34. ^ Laydon, Daniel J.; Mishra, Swapnil; Hinsley, Wes R.; Samartsidis, Pantelis; Flaxman, Seth; Gandy, Axel; Ferguson, Neil M.; Bhatt, Samir (2021-02-24). "Impact of the Tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns". medRxiv 10.1101/2021.02.23.21252277v1.

Further reading[edit]

External links[edit]

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