| Issue |
Math. Model. Nat. Phenom.
Volume 17, 2022
Coronavirus: Scientific insights and societal aspects
|
|
|---|---|---|
| Article Number | 8 | |
| Number of page(s) | 17 | |
| DOI | https://doi.org/10.1051/mmnp/2022014 | |
| Published online | 20 May 2022 | |
- A. Alexanderian, P.A. Gremaud and R.C. Smith, Variance-based sensitivity analysis for time-dependent processes. Reliab. Eng. Syst. Saf. 196 (2020) 106722. [CrossRef] [Google Scholar]
- S.N. Arifin, G.R. Madey and F.H. Collins, Spatial agent-based simulation modeling in public health: design, implementation, and applications for malaria epidemiology. John Wiley & Sons (2016). [Google Scholar]
- D. Bigoni, Uncertainty Quantification with Applications to Engineering Problems Ph.D. thesis, Technical University of Denmark (2015). [Google Scholar]
- C. Canuto, M.Y. Hussaini, A. Quarteroni and T.A. Zang, Spectral methods: fundamentals in single domains. Springer Science & Business Media (2007). [Google Scholar]
- V. Capasso and V. Capasso, vol. 88 of Mathematical structures of epidemic systems. Springer (1993). [CrossRef] [Google Scholar]
- W. Edeling, H. Arabnejad, R. Sinclair, D. Suleimenova, K. Gopalakrishnan, B. Bosak, D. Groen, I. Mahmood, D. Crommelin and P.V. Coveney, The impact of uncertainty on predictions of the CovidSim epidemiological code. Nat. Comput. Sci. 1 (2021) 128–135. [CrossRef] [Google Scholar]
- B. Efron, The bootstrap and markov-chain monte carlo. J. Biopharmaceut. Stat. 21 (2011) 1052–1062. [CrossRef] [PubMed] [Google Scholar]
- P. Fine, K. Eames and D.L. Heymann, “Herd Immunity": A Rough Guide. Clin. Infect. Dis. 52 (2011) 911–916. [CrossRef] [PubMed] [Google Scholar]
- J. Friedman, T. Hastie, R. Tibshirani et al., The elements of statistical learning. Springer series in statistics New York (2001), Vol. 1. [Google Scholar]
- R. Ghanem and P.D. Spanos, Polynomial chaos in stochastic finite elements. J. Appl. Mech. 57 (1990) 197–202. [CrossRef] [Google Scholar]
- K. Græsbpll, L.E. Christiansen, U.H. Thygesen and C. Kirkeby, Delaying the peak of the COVID-19 epidemic with travel restrictions. Epidemiol. Methods 10 (2021) 20200042. [CrossRef] [Google Scholar]
- H.W. Hethcote, The Mathematics of Infectious Diseases. SIAM Rev. 42 (2000) 599–653. [CrossRef] [MathSciNet] [Google Scholar]
- T. House, A. Ford, S. Lan, S. Bilson, E. Buckingham-Jeffery and M. Girolami, Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry. J. Royal Soc. Interface 13 (2016) 20160279. [CrossRef] [PubMed] [Google Scholar]
- Q. Lin, S. Zhao, D. Gao, Y. Lou, S. Yang, S.S. Musa, M.H. Wang, Y. Cai, W. Wang, L. Yang et al., A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int. J. Infectious Diseases 93 (2020) 211–216. [CrossRef] [Google Scholar]
- J.S. Liu and J.S. Liu, Vol. 10 of Monte Carlo strategies in scientific computing. Springer (2001). [Google Scholar]
- J.O. Lloyd-Smith, S.J. Schreiber, P.E. Kopp and W.M. Getz, Superspreading and the effect of individual variation on disease emergence. Nature 438 (2005) 355–359. [CrossRef] [PubMed] [Google Scholar]
- T.E. Oliphant, vol. 1 of A guide to NumPy. Trelgol Publishing USA (2006). [Google Scholar]
- A. Olivares and E. Staffetti, Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy. Chaos Solitons Fractals 146 (2021) 110895. [CrossRef] [PubMed] [Google Scholar]
- K. Sneppen and L. Simonsen, Impact of superspreaders on dissemination and mitigation of COVID-19. Proc. of the National Academy of Sciences 11(2021) e2016623118. [CrossRef] [PubMed] [Google Scholar]
- B. Sudret, Global sensitivity analysis using polynomial chaos expansions. Reliab. Eng. Syst. Saf. 93 (2008) 964–979. [CrossRef] [Google Scholar]
- L. Taghizadeh, A. Karimi and C. Heitzinger, Uncertainty quantification in epidemiological models for the COVID-19 pandemic. Comput. Biol. Med. 125 (2020) 104011. [CrossRef] [Google Scholar]
- E. Unlu, H. Léger, O. Motornyi, A. Rukubayihunga, T. Ishacian and M. Chouiten, Epidemic analysis of COVID-19 outbreak and counter-measures in France. medRxiv (2020) https://doi.org/10.1101/2020.04.27.20079962 [Google Scholar]
- D. Xiu, Numerical methods for stochastic computations: a spectral method approach. Princeton University Press (2010). [Google Scholar]
- D. Xiu and G.E. Karniadakis, The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24 (2002) 619–644. [CrossRef] [MathSciNet] [Google Scholar]
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