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'''CovidSim''' is an [[Mathematical modelling of infectious disease|epidemiological model]] for COVID-19 developed by [[Imperial College COVID-19 Response Team]], led by [[Neil Ferguson (epidemiologist)|Neil Ferguson]].<ref name=":1" /> For UK Prime Minister [[Boris Johnson]],<ref name="Bostock2020">{{citation|author=Bill Bostock|title=How 'Professor Lockdown' helped save tens of thousands of lives worldwide — and carried COVID-19 into Downing Street|date=April 25, 2020|url=https://www.businessinsider.com/neil-ferguson-transformed-uk-covid-response-oxford-challenge-imperial-model-2020-4|work=[[Business Insider]]}}</ref> 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.<ref>{{Cite web|title=Report 9 - Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand|url=http://www.imperial.ac.uk/medicine/departments/school-public-health/infectious-disease-epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-9-impact-of-npis-on-covid-19/|access-date=2021-03-10|website=Imperial College London|language=en-GB}}</ref><ref name=":1">{{citation|journal=[[Nature (journal)|Nature]]|title=Special report: The simulations driving the world's response to COVID-19|author=David Adam|date=April 2, 2020|volume=580|issue=7803|page=316|doi=10.1038/d41586-020-01003-6|bibcode=2020Natur.580..316A|s2cid=214771531|url=https://www.nature.com/articles/d41586-020-01003-6}}</ref><ref>{{citation|publisher=Reuters|date=March 17, 2020|title=Sobering coronavirus study prompted Britain to toughen its approach|author=Kate Kelland|url=https://www.reuters.com/article/us-health-coronavirus-britain-research/sobering-coronavirus-study-prompted-britain-to-toughen-its-approach-idUSKBN2141EP}}</ref><ref>{{citation|title= Don't Believe the COVID-19 Models.That's not what they're for.|author=ZEYNEP TUFEKCI|date=April 2, 2020 |work=[[The Atlantic]]|url= https://www.theatlantic.com/technology/archive/2020/04/coronavirus-models-arent-supposed-be-right/609271/ }}</ref>
'''CovidSim''' is an [[Mathematical modelling of infectious disease|epidemiological model]] for COVID-19 developed by [[Imperial College COVID-19 Response Team]], led by [[Neil Ferguson (epidemiologist)|Neil Ferguson]].<ref name=":1" /> For UK Prime Minister [[Boris Johnson]],<ref name="Bostock2020">{{citation|author=Bill Bostock|title=How 'Professor Lockdown' helped save tens of thousands of lives worldwide — and carried COVID-19 into Downing Street|date=April 25, 2020|url=https://www.businessinsider.com/neil-ferguson-transformed-uk-covid-response-oxford-challenge-imperial-model-2020-4|work=[[Business Insider]]}}</ref> 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.<ref>{{Cite web|title=Report 9 - Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand|url=http://www.imperial.ac.uk/medicine/departments/school-public-health/infectious-disease-epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-9-impact-of-npis-on-covid-19/|access-date=2021-03-10|website=Imperial College London|language=en-GB}}</ref><ref name=":1">{{citation|journal=[[Nature (journal)|Nature]]|title=Special report: The simulations driving the world's response to COVID-19|author=David Adam|date=April 2, 2020|volume=580|issue=7803|pages=316–318|doi=10.1038/d41586-020-01003-6|pmid=32242115|bibcode=2020Natur.580..316A|s2cid=214771531|url=https://www.nature.com/articles/d41586-020-01003-6}}</ref><ref>{{citation|publisher=Reuters|date=March 17, 2020|title=Sobering coronavirus study prompted Britain to toughen its approach|author=Kate Kelland|url=https://www.reuters.com/article/us-health-coronavirus-britain-research/sobering-coronavirus-study-prompted-britain-to-toughen-its-approach-idUSKBN2141EP}}</ref><ref>{{citation|title= Don't Believe the COVID-19 Models.That's not what they're for.|author=ZEYNEP TUFEKCI|date=April 2, 2020 |work=[[The Atlantic]]|url= https://www.theatlantic.com/technology/archive/2020/04/coronavirus-models-arent-supposed-be-right/609271/ }}</ref>


== History ==
== History ==
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==== Other applications ====
==== Other applications ====
Additional research is based on the model, e.g. for simulation of effect of school closures on mortality.<ref>{{Cite journal|last=Rice|first=Ken|last2=Wynne|first2=Ben|last3=Martin|first3=Victoria|last4=Ackland|first4=Graeme J.|date=2020-10-07|title=Effect of school closures on mortality from coronavirus disease 2019: old and new predictions|url=https://www.bmj.com/content/371/bmj.m3588|journal=BMJ|language=en|volume=371|doi=10.1136/bmj.m3588|issn=1756-1833|pmid=33028597}}</ref>
Additional research is based on the model, e.g. for simulation of effect of school closures on mortality.<ref>{{Cite journal|last1=Rice|first1=Ken|last2=Wynne|first2=Ben|last3=Martin|first3=Victoria|last4=Ackland|first4=Graeme J.|date=2020-10-07|title=Effect of school closures on mortality from coronavirus disease 2019: old and new predictions|url=https://www.bmj.com/content/371/bmj.m3588|journal=BMJ|language=en|volume=371|pages=m3588|doi=10.1136/bmj.m3588|issn=1756-1833|pmid=33028597|pmc=7536648}}</ref>


==See also==
==See also==

Revision as of 15:17, 13 March 2021

CovidSim is an epidemiological model for COVID-19 developed by Imperial College COVID-19 Response Team, led by Neil Ferguson.[1] For UK Prime Minister Boris Johnson,[2] 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.[3][1][4][5]

History

The codebase for the model was initially constructed c. 2005.[2][6]

Software

In May 2020, a C++ derivative of the code was released to GitHub.[7]

Code reviews and expert opinions

Soundness

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".[8][9]

Shortcomings

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".[10][11] British newspaper The Telegraph reported that some software engineers who reviewed the new code called it "totally unreliable" and a "buggy mess".[12]

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".[13]

Dr Alvin Wilby, FRAeS concluded in a review submitted to UK Parliament Committee[14] that the model and software suffers from a number of deficiencies, including poor documentation, high complexity and false modelling, and that due to inacceptable software quality, the model would fail against accreditation of safety standards such as MISRA.[15]

Model

Reproducibility

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.[16][17][18] A June 2020 editorial in Nature declared the original CovidSim codebase met the requirements of scientific reproducibility.[19]

Uncertainty

Further research exists to identify the following three sources of uncertainty in the simulation:[20][21] parametric uncertainty, model structure uncertainty and scenario uncertainty:[22] The simulation output much depends on the inputs and can change up to 300% based on 940 parameters, wherefrom 19 are considered most sensitive. Model structure and scenario uncertainty must therefore be understood.[22]

Other applications

Additional research is based on the model, e.g. for simulation of effect of school closures on mortality.[23]

See also

References

  1. ^ a b David Adam (April 2, 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 Bill Bostock (April 25, 2020), "How 'Professor Lockdown' helped save tens of thousands of lives worldwide — and carried COVID-19 into Downing Street", Business Insider
  3. ^ "Report 9 - Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand". Imperial College London. Retrieved 2021-03-10.
  4. ^ Kate Kelland (March 17, 2020), Sobering coronavirus study prompted Britain to toughen its approach, Reuters
  5. ^ ZEYNEP TUFEKCI (April 2, 2020), "Don't Believe the COVID-19 Models.That's not what they're for.", The Atlantic
  6. ^ Ferguson, Neil M.; Cummings, Derek A. T.; Cauchemez, Simon; Fraser, Christophe; Riley, Steven; Meeyai, Aronrag; Iamsirithaworn, Sopon; Burke, Donald S. (September 2005). "Strategies for containing an emerging influenza pandemic in Southeast Asia". Nature. 437 (7056): 209–214. Bibcode:2005Natur.437..209F. doi:10.1038/nature04017. hdl:10722/54275. ISSN 1476-4687. PMID 16079797. S2CID 4415006.
  7. ^ mrc-ide/covid-sim, MRC Centre for Global Infectious Disease Analysis, 2021-03-08, retrieved 2021-03-09
  8. ^ "Codecheck confirms reproducibility of COVID-19 model results | Imperial News | Imperial College London". Imperial News. Retrieved 2021-03-10.
  9. ^ John Carmack [@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.
  10. ^ "Review of Ferguson et al "Impact of non-pharmaceutical interventions..."". New England Complex Systems Institute. Retrieved 2021-03-13.
  11. ^ Jessica Hamzelou (March 23, 2020), "UK's scientific advice on coronavirus is a cause for concern", New Scientist
  12. ^ Boland, Hannah (May 16, 2020). "Coding that led to lockdown was 'totally unreliable' and a 'buggy mess', say experts". The Telegraph. Retrieved May 22, 2020.
  13. ^ Wooldridge, Michael. "The Software that Led to the Lockdown". cacm.acm.org. Retrieved 2021-03-09.
  14. ^ "Committees - UK Parliament". committees.parliament.uk. Retrieved 2021-03-13.
  15. ^ Wilby, Alvin. "Written Evidence Submitted by Dr Alvin Wilby, FRAeS, Visiting Professor, Complex Socio-Technical Systems, University of Bristol (C190050)" (PDF). UK Parliament. Retrieved 2020-03-13.{{cite web}}: CS1 maint: url-status (link)
  16. ^ "Codecheck confirms reproducibility of COVID-19 model results". University of Cambridge. 2020-06-09. Retrieved 2021-03-10.
  17. ^ "Codecheck confirms reproducibility of COVID-19 model results" (June 2, 2020), Mirage News. Retrieved June 6, 2020.
  18. ^ Eglen, Stephen J (29 May 2020). "CODECHECK certificate 2020-010". Geneva, Switzerland. Retrieved 2020-10-14. PDF report available.
  19. ^ Singh Chawla, Dalmeet (18 June 2020). "Critiqued coronavirus simulation gets thumbs up from code-checking efforts" (PDF). Nature. 5 (82): 323–324. Bibcode:2020Natur.582..323S. doi:10.1038/d41586-020-01685-y. PMID 32546864. S2CID 219700526. Retrieved 2020-10-14.
  20. ^ Winsberg, Eric (2019), "Computer Simulations in Science", in Zalta, Edward N. (ed.), The Stanford Encyclopedia of Philosophy (Winter 2019 ed.), Metaphysics Research Lab, Stanford University, retrieved 2021-03-09
  21. ^ Leung, Kathy; Wu, Joseph T. (February 2021). "Quantifying the uncertainty of CovidSim". Nature Computational Science. 1 (2): 98–99. doi:10.1038/s43588-021-00031-0. ISSN 2662-8457.
  22. ^ a b Edeling, Wouter; Arabnejad, Hamid; Sinclair, Robbie; Suleimenova, Diana; Gopalakrishnan, Krishnakumar; Bosak, Bartosz; Groen, Derek; Mahmood, Imran; Crommelin, Daan; Coveney, Peter V. (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. ISSN 2662-8457.
  23. ^ Rice, Ken; Wynne, Ben; Martin, Victoria; Ackland, Graeme J. (2020-10-07). "Effect of school closures on mortality from coronavirus disease 2019: old and new predictions". BMJ. 371: m3588. doi:10.1136/bmj.m3588. ISSN 1756-1833. PMC 7536648. PMID 33028597.

Further reading

External links

Retrieved from "https://en.wikipedia.org/w/index.php?title=CovidSim&oldid=1011910962"