Issue
Math. Model. Nat. Phenom.
Volume 15, 2020
Coronavirus: Scientific insights and societal aspects
Article Number 31
Number of page(s) 13
DOI https://doi.org/10.1051/mmnp/2020016
Published online 14 May 2020
  1. M. Ajelli, B. Gonçalves, D. Balcan, V. Colizza, H. Hu, J.J. Ramasco, S. Merler and A. Vespignani, Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect. Dis. 10 (2010) 190. [CrossRef] [PubMed] [Google Scholar]
  2. L.J. Allen, Some discrete-time si, sir, and sis epidemic models. Math. Biosci. 124 (1994) 83–105. [Google Scholar]
  3. M. Banerjee, A. Tokarev and V. Volpert, Immuno-epidemiological model of two-stage epidemic growth. Preprint arXiv:2003.14152 (2020). [Google Scholar]
  4. A. Bouchnita and A. Jebrane, A hybrid multi-scale model of covid-19 transmission dynamics to assess the potential of non-pharmaceutical interventions. Preprint medRxiv 10.1101/2020.04.05.20054460 (2020). [Google Scholar]
  5. W. Dong, K. Heller and A.S. Pentland, Modeling infection with multi-agent dynamics, in International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer, Berlin (2012) 172–179. [Google Scholar]
  6. N.M. Ferguson, D.A. Cummings, C. Fraser, J.C. Cajka, P.C. Cooley and D.S. Burke, Strategies for mitigating an influenza pandemic. Nature 442 (2006) 448–452. [Google Scholar]
  7. S. Flaxman, S. Mishra, A. Gandy, H. Unwin, H. Coupland, T. Mellan, H. Zhu, T. Berah, J. Eaton, P. Perez Guzman, et al. Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on covid-19 in 11 Europeancountries (2020). [Google Scholar]
  8. W. Garira, A complete categorization of multiscale models of infectious disease systems. J. Biol. Dyn. 11 (2017) 378–435. [CrossRef] [PubMed] [Google Scholar]
  9. D. Helbing and P. Molnar, Social force model for pedestrian dynamics. Phys. Rev. E 51 (1995) 4282. [Google Scholar]
  10. D.S. Hui, E.I. Azhar, T.A. Madani, F. Ntoumi, R. Kock, O. Dar, G. Ippolito, T.D. Mchugh, Z.A. Memish, C. Drosten, et al. The continuing 2019-ncov epidemic threat of novel coronaviruses to global health–thelatest 2019 novel coronavirus outbreak in Wuhan, China. Int. J. Infect. Dis. 91 (2020) 264. [CrossRef] [PubMed] [Google Scholar]
  11. B. Kabalan, P. Argoul, A. Jebrane, G. Cumunel and S. Erlicher, A crowd movement model for pedestrian flow through bottlenecks. Ann. Solid Struct. Mech. 8 (2016) 1–15. [Google Scholar]
  12. S.A. Lauer, K.H. Grantz, Q. Bi, F.K. Jones, Q. Zheng, H.R. Meredith, A.S. Azman, N.G. Reich and J. Lessler, The incubation period of coronavirusdisease 2019 (covid-19) from publicly reported confirmed cases: estimation and application. Ann. Internal Med. 172 (2020) 577–582. [Google Scholar]
  13. N.M. Linton, T. Kobayashi, Y. Yang, K. Hayashi, A.R. Akhmetzhanov, S.-m. Jung, B. Yuan, R. Kinoshita and H. Nishiura, Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: a statistical analysis of publicly available case data. J. Clin. Med. 9 (2020) 538. [Google Scholar]
  14. Y. Liu, A.A. Gayle, A. Wilder-Smith and J. Rocklöv, The reproductive number of covid-19 is higher compared to sars coronavirus. J. Travel Med. 27 (2020) taaa021. [CrossRef] [PubMed] [Google Scholar]
  15. M. Ministère de la santé, Bulletin hebdomadaire covid 19. Available at: http://www.covidmaroc.ma/ (2020). [Google Scholar]
  16. S. Namilae, A. Srinivasan, A. Mubayi, M. Scotch and R. Pahle, Self-propelled pedestrian dynamics model: Application to passenger movement and infection propagation in airplanes. Physica A 465 (2017) 248–260. [Google Scholar]
  17. J. Rocklöv, H. Sjödin and A. Wilder-Smith, Covid-19 outbreak on the diamond princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. To appear in: J. Travel Medicine (2020) taaa030. [Google Scholar]
  18. V. Surveillances, The epidemiological characteristics of an outbreakof 2019 novel coronavirus diseases (covid-19)–China, 2020. China CDC Weekly 2 (2020) 113–122. [Google Scholar]
  19. N. van Doremalen, T. Bushmaker, D.H. Morris, M.G. Holbrook, A. Gamble, B.N. Williamson, A. Tamin, J.L. Harcourt, N.J. Thornburg, S.I. Gerber, et al., Aerosol and surface stability of sars-cov-2 as compared with sars-cov-1. New Engl. J. Med. 382 (2020) 1564–1567. [Google Scholar]
  20. V. Volpert, M. Banerjee and S. Petrovskii, On a quarantine model of coronavirus infection and data analysis. MMNP 15 (2020) 24. [EDP Sciences] [Google Scholar]
  21. World Health Organization, Coronavirus disease 2019 (covid-19) situation report-41. [Google Scholar]
  22. World Health Organization, Coronavirus disease 2019 (covid-19) situation report-73. [Google Scholar]

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