×

zbMATH — the first resource for mathematics

A multiscale model of virus pandemic: heterogeneous interactive entities in a globally connected world. (English) Zbl 07263762

MSC:
92D30 Epidemiology
92C60 Medical epidemiology
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] P. Adhikari, Intra- and intermolecular atomic-scale interaction of the receptor binding domain in SARS-CoV-2 spike protein: Implication for ACE2 receptor binding, preprint (2020).
[2] Albi, G., Bellomo, N., Fermo, L., Ha, S.-Y., Kim, J., Pareschi, L., Poyato, D. and Soler, J., Traffic, crowds, and swarms. From kinetic theory and multiscale methods to applications and research perspectives, Math. Models Methods Appl. Sci.29 (2019) 1901-2005. · Zbl 1431.35211
[3] Andersen, K. G., Rambaut, A., Lipkin, W. Ian, Holmes, E. C. and Garry, R. F., The proximal origin of SARS-CoV-2, Nat. Med.26 (2020) 450-452.
[4] Anderson, R. A. and May, R. M., Population biology of infectious diseases: Part I, Nature280 (1979) 361-367.
[5] Anderson, R. A. and May, R. M., Population biology of infectious diseases: Part II, Nature280 (1979) 455-461.
[6] Anderson, R. M., The Population Dynamics of Infectious Diseases: Theory and Application (Chapman and Hall, 1982).
[7] Aristov, V. V., Biological systems as nonequilibrium structures described by kinetic methods, Res. Phys.13 (2019) 102232.
[8] B. Avishai, The pandemic isn’t a black swan but a portent of a more fragile global system, The New Yorker (2020), https://www.newyorker.com/news/daily-comment/the-pandemic-isnt-a-black-swan-but-a-portent-of-a-more-fragile-global-system.
[9] Aylaj, B., Bellomo, N., Gibelli, L. and Reali, A., On a unified multiscale vision of behavioral crowds, Math. Models Methods Appl. Sci.30 (2020) 1-22. · Zbl 1434.91046
[10] Bailey, N. T. J., Mathematical Tools for Understanding Infectious Disease Dynamics (Griffin, 1975).
[11] Baldwin, P. and Di Mauro, B. W., Economics in the Time of COVID-19 (VoxEU.org Book, 2020).
[12] Ball, P., Why Society is a Complex Matter (Springer-Verlag, 2012).
[13] Bar-On, Y. M., Flamholz, A., Phillips, R. and Milo, R., SARS-CoV-2 (COVID-19) by the numbers, eLife9 (2020) 1-15.
[14] R. J. Barro, J. F. Ursua and J. Weng, The Coronavirus and the Great Influenza Pandemic: Lessons from the “Spanish Flu” for the Coronavirus’s Potential Effects on Mortality and Economic Activity, NBER WP 26866 (2020).
[15] Bellomo, N. and Bellouquid, A., On multiscale models of pedestrian crowds from mesoscopic to macroscopic, Commun. Math. Sci.13 (2015) 1649-1664. · Zbl 1329.90029
[16] Bellomo, N., Bellouquid, A. and Chouhad, N., From a multiscale derivation of nonlinear cross-diffusion models to Keller-Segel models in a Navier-Stokes fluid, Math. Models Methods Appl. Sci.26 (2016) 2041-2069. · Zbl 1353.35038
[17] Bellomo, N., Bellouquid, A. and Knopoff, D., From the micro-scale to collective crowd dynamics, Multiscale Model. Simul.11 (2013) 943-963. · Zbl 1280.90019
[18] Bellomo, N., Bellouquid, A., Gibelli, L. and Outada, N., A Quest Towards a Mathematical Theory of Living Systems (Birkhäuser, 2017). · Zbl 1381.92001
[19] Bellomo, N. and Gibelli, L., Toward a mathematical theory of behavioral-social dynamics for pedestrian crowds, Math. Models Methods Appl. Sci.25 (2015) 2417-2437. · Zbl 1325.91042
[20] Bellomo, N., Gibelli, L. and Outada, N., On the interplay between behavioral dynamics and social interactions in human crowds, Kinet. Rel. Models12 (2019) 397-409. · Zbl 1420.91384
[21] Bellomo, N., Painter, K. J., Tao, Y. and Winkler, M., Occurrence versus absence of taxis-driven instabilities in a May-Nowak model for virus infection, SIAM J. Appl. Math.79 (2019) 1990-2010. · Zbl 1428.35615
[22] Bellouquid, A. and Delitala, M., Modelling Complex Biological Systems — A Kinetic Theory Approach, , (Birkhäuser, 2006). · Zbl 1178.92002
[23] Bernoulli, D., Essai d’une nouvelle analyse de la mortalité causée par la petite vérole et des avantages de l’inoculation pour la prévenir, Mém. Math. Phys. Acad. Sci. Paris1-45 (1760/1766) 1-42.
[24] Bertozzi, A. L., Rosado, J., Short, M. B. and Wang, L., Contagion shocks in one dimension, J. Stat. Phys.158 (2015) 647-664. · Zbl 1318.35135
[25] Bingham, R., Dykeman, E. and Twarock, R., RNA virus evolution via a quasispecies-based model reveals a drug target with a high barrier to resistance, Viruses9 (2017) 347.
[26] Birchenough, G. M.et al., New developments in goblet cell mucus secretion and function, Mucosal Immunol.8 (2015) 712.
[27] Bolker, B. M. and Grenfell, B. T., Space, persistence and dynamics of measles epidemics, Philos. Trans. R. Soc. B348 (1995) 309-320.
[28] Bolker, B. M. and Grenfell, B. T., Impact of vaccination on the spatial correlation and persistence of measles dynamics, Proc. Natl. Acad. Sci. USA93 (1996) 12648-12653.
[29] Burini, D. and Chouhad, N., A multiscale view of nonlinear diffusion in biology: From, cells to tissues, Math. Models Methods Appl. Sci.29 (2019) 791-823. · Zbl 1427.35291
[30] Burini, D. and De Lillo, S., On the complex interaction between collective learning and social dynamics. Symmetry11 (2019) 967. https://doi.org/10.3390/sym11080967
[31] Burini, D., De Lillo, S. and Gibelli, L., Collective learning dynamics modeling based on the kinetic theory of active particles, Phys. Life Rev.16 (2016) 123-139.
[32] Callaway, E., Coronavirus vaccines: Five key questions as trials begin, Nature579 (2020) 481.
[33] Capasso, V., Grosso, E. and Serio, G., Mathematical models in epidemiological analysis. 1. Application to cholera pandemic in Bari in 1973, Ann. Sclavo19 (1977) 193-208.
[34] V. Capasso and S. L. Paveri-Fontana, A mathematical model for the 1973 cholera epidemic in the European Mediterranean region, Rev. Epidem. Santé Publ.27 (1979) 121-132, Erratam 28 (1980) 390.
[35] Capasso, V. and Maddalena, L., Convergence to equilibrium states for a reaction-diffusion system modelling the spatial spread of a class of bacterial and viral diseases, J. Math. Biol.13 (1981) 173-184. · Zbl 0468.92016
[36] Carloni, A., Poletti, V., Fermo, L., Bellomo, N. and Chilosi, M., Heterogeneous distribution of mechanical stress in human lung: A mathematical approach to evaluate abnormal remodeling, J. Theor. Biol.332 (2013) 136-140. · Zbl 1330.92019
[37] Cecconi, M., Forni, G. and Mantovani, A., COVID-19: An executive report April 2020 update, Accademia Nazionale dei Lincei, Commissione Salute (2020).
[38] Cetrulo, A., Guarascio, D. and Virgillito, M. E., The privilege of working from home at the time of social distancing, Intereconomics55 (2020) 142-147.
[39] Z. J. Cheng and J. Shan, 2019 Novel Coronavirus: Where we are and what we know, doi:10.20944/preprints202001.0381.v1.
[40] Chen, C.-Y.et al., Structure of the SARS coronavirus nucleocapsid protein RNA-binding dimerization domain suggests a mechanism for helical packaging of viral RNA, J. Mol. Biol.368 (2007) 1075-1086.
[41] Cirillo, P. and Taleb, N. N., Tail risk of contagious diseases, Nat. Phys., PERSPECTIVE, https://doi.org/10.1038/s41567-020-0921-x (2020).
[42] Ciupe, S. M. and Heffernan, J. M., In-host modeling, Infect. Dis. Model.2 (2017) 188-202.
[43] Conway, J. M. and Perelson, A. S., A Hepatitis C virus infection model with time-varying drug effectiveness: Solution and analysis, PLoS Comput. Biol.10 (2014) e1003769.
[44] Coronavirus Update (live) — Worldmeter, https://www.worldometers.info/coronavirus/.
[45] J. Corum and C. Zimmer, Bad news wrapped in protein: Inside the coronavirus genome, New York Times 2020, https://www.nytimes.com/interactive/2020/04/03/science/coronavirus-genome-bad-news-wrapped-in-protein.html.
[46] Crick, F. H. C. and Watson, J. D., Structure of small viruses, Nature177 (1956) 473-475.
[47] Cyranoski, D., Profile of a killer: The complex biology powering the coronavirus pandemic, Nature581 (2020) 22.
[48] J. le R. d’Alembert, Onzième mémoire, Sur l’application du calcul des probabilités à l’inoculation de la petite vérole, in Opuscules Mathématiques, Tome second, David, (1761), pp. 26-95.
[49] Day, M., Covid-19: Four fifths of cases are asymptomatic, China figures indicate, Br. Med. J.369 (2020) m1375.
[50] Dechant, P. P., Wardman, J., Keef, T. and Twarock, R., Viruses and fullerenes — Symmetry as a common thread?Acta Crystallogr. A, Found. Adv.70 (2014) 162-167. · Zbl 1358.92106
[51] De Lillo, S., Delitala, M. and Salvatori, M., Modelling epidemics and virus mutations by methods of the mathematical kinetic theory for active particles, Math. Models Methods Appl. Sci.19 (2009) 1405-1425. · Zbl 1175.92035
[52] Diekmann, O. and Heesterbeek, J. A. P., Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation, (Wiley, 2000). · Zbl 0997.92505
[53] Diekmann, O., Heesterbeek, J. A. P. and Britton, T., Mathematical Tools for Understanding Infectious Disease Dynamics (Princeton Univ. Press, 2012). · Zbl 1304.92009
[54] Diekmann, O., Limiting behaviour in an epidemic model, Nonlinear Anal., Theory, Methods Appl.1 (1977) 459-470. · Zbl 0371.92024
[55] Dietz, K. and Heesterbeek, J. A. P., Daniel Bernoulli’s epidemiological model revisited, Math. Biosci.180 (2002) 1-21. · Zbl 1019.92028
[56] I. Dubanevics and T. C. B. Mcleish, Computational Analysis of Dynamic Allostery and Control in the SARS-CoV-2 Main Protease, preprint (2020).
[57] Dykeman, E. C., Stockley, P. G. and Twarock, R., Solving a Levinthal’s paradox for virus assembly identifies a unique antiviral strategy, Proc. Natl. Acad. Sci.111 (2014) 5361-5366.
[58] Elmezayen, A. D., Al-Obaidi, A., Tegin, U. Alp and Yelekçi, K., Drug repurposing for coronavirus (COVID-19): In silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes, J. Biomol. Struct. Dyn.0 (2020) 1-13.
[59] Fenner, F.et al., Smallpox and its Eradication, Vol. 6 (World Health OrganizationGeneva, 1988).
[60] Ferrari, M. J., Grenfell, B. T. and Strebel, P. M., Think globally, act locally: The role of local demographics and vaccination coverage in the dynamic response of measles infection to control, Philos. Trans. R. Soc. B368 (2013) 20120141.
[61] Fridell, R. A.et al., Genotypic and phenotypic analysis of variants resistant to hepatitis C virus nonstructural protein 5A replication complex inhibitor BMS-790052 in humans: In vitro and in vivo correlations, Hepatology54 (2011) 1924-1935.
[62] K. Gao, D. D. Nguyen, J. Chen, R. Wang and G.-W. Wei, Repositioning of 8565 existing drugs for COVID-19, arXiv:2005.10028V1.
[63] B. Gates, The next outbreak? We are not ready, https://www.youtube.com/watch?v= 6Af6b-wyiwI.
[64] Ghedin, E.et al., Large-scale sequencing of human influenza reveals the dynamic nature of viral genome evolution, Nature437 (2005) 1162-1166.
[65] Hadfield, J.et al., NextStrain: Real-time tracking of pathogen evolution, Bioinformatics34 (2018) 4121-4123.
[66] Grenfell, B. T., Bjornstad, O. N. and Kappey, J., Travelling waves and spatial hierarchies in measles epidemics, Nature414 (2001) 716-723.
[67] Guedj, J., Dahari, H., Rong, L., Sansone, N. D., Nettles, R. E., Cotler, S. J., Layden, T. J., Uprichard, S. L. and Perelson, A. S., Modeling shows that the NS5A inhibitor daclatasvir has two modes of action and yields a shorter estimate of the hepatitis C virus half-life, Proc. Natl. Acad. Sci.110 (2013) 3991-3996.
[68] Guedj, J., Rong, L., Dahari, H. and Perelson, A. S., A perspective on modelling hepatitis C virus infection, J. Viral Hepatitis17 (2010) 825-833.
[69] Groupe d’études géopolitiques de la ENS, La Observatoire du Covid-19, Le Grand Continent, https://legrandcontinent.eu/fr/observatoire-coronavirus/.
[70] Hartwell, H. L., Hopfield, J. J., Leibler, S. and Murray, A. W., From molecular to modular cell biology, Nature402 (1999) c47-c52.
[71] Heesterbeek, H., Anderson, R. M., Andreasen, V., Bansal, S., De Angelis, D., Dye, C., Eames, K. T. D., Edmunds, W. J., Frost, S. D. W., Funk, S., Hollingsworth, T. D., House, T., Isham, V., Klepac, P., Lessler, J., Lloyd-Smith, J. O., Metcalf, C. J. E., Mollison, D., Pellis, L., Pulliam, J. R. C., Roberts, M. G. and Viboud, C., Isaac Newton Institute IDD Collaboration, Modeling infectious disease dynamics in the complex landscape of global health, Science347 (2015) aaa4339.
[72] Hilbert, D., Mathematische Probleme, Göttinger Nach. (1900) 253-297.
[73] Hilbert, D., Mathematische Probleme, Arch. Math. Phys.1 (1901) 44-63; 213-237.
[74] Hilbert, D., Mathematical problems, Bull. Am. Math. Soc.8 (1902) 437-479. · JFM 33.0976.07
[75] Hoffmann, M.et al., SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor, Cell181 (2020) 271.
[76] https://www.euractiv.com/section/economy-jobs/news/ilo-warns-of-devastating-consequences-of-covid-19-on-labour-markets/.
[77] Indelicato, G., Cermelli, P. and Twarock, R., A coarse-grained model of the expansion of the human rhinovirus 2 capsid reveals insights in genome release, J. R. Soc. Interface16 (2019) 20190044.
[78] International Monetary Fund, World Economic Outlook, April 2020: The Great Lockdown (2020).
[79] Johns Hopkins University, Mortality Analyses, https://coronavirus.jhu.edu/data/mortality (2020).
[80] Kallen, A., Arcuri, P. and Murray, J. D., A simple model for the spatial spread and control of rabies, J. Theor. Biol.116 (1985) 377-393.
[81] Keef, T., Twarock, R. and Elsawy, K. M., Blueprints for viral capsids in the family of Polyomaviridae, J. Theor. Biol.253 (2008) 808-816. · Zbl 1398.92294
[82] Keef, T., Wardman, J. P., Ranson, N. A., Stockley, P. G. and Twarock, R., Structural constraints on the three-dimensional geometry of simple viruses: Case studies of a new predictive tool, Acta Crystallogr. A, Found. Crystallogr.69 (2013) 140-150.
[83] Kermack, W. O. and McKendrick, A. G., A contribution to the mathematical theory of epidemics, Proc. R. Soc. London A115 (1927) 700-721. · JFM 53.0517.01
[84] Kim, D., Lee, J.-Y., Yang, J.-S., Kim, J. W., Kim, V. N. and Chang, H., The architecture of SARS-CoV-2 transcriptome, Cell181 (2020) 914-921. e10.
[85] Kim, D. and Quaini, A., A kinetic theory approach to model pedestrian dynamics in bounded domains with obstacles, Kinet. Relat. Models12 (2019) 1273-1296. · Zbl 1434.35248
[86] Kim, D. and Quaini, A., Coupling kinetic theory approaches for pedestrian dynamics and disease contagion in a confined environment, Math. Models Methods Appl. Sci.30 (2020), arXiv:2003.08357v1[physics.soc-ph].
[87] Knopoff, D., On the modeling of migration phenomena on small networks, Math. Models Methods Appl. Sci.23 (2013) 541-563. · Zbl 1357.91035
[88] Knopoff, D., On a mathematical theory of complex systems on networks with application to opinion formation, Math. Models Methods Appl. Sci.24 (2014) 405-426. · Zbl 1281.91134
[89] Knopoff, D. and Trucco, F., A compartmental model for antibiotic resistant bacterial infections over networks, Int. J. Biomath.13 (2020), Article Id: 2050001, 16pp. · Zbl 1432.92093
[90] K. Kupferschmid, Why do some COVID-19 patients infect many others, whereas most don’t spread the virus at all? Science, doi:10.1126/science.abc8931.
[91] K. Kupferschmid, Can plasma from COVID-19 survivors help save others? Science, doi:10.1126/science.abd0355.
[92] Kwon, H. R. and Silva, E. A., Mapping the landscape of behavioral theories: Systematic literature review, J. Plan. Lit.35 (2020) 161-179.
[93] Laoukili, J.et al., IL-13 alters mucociliary differentiation and ciliary beating of human respiratory epithelial cells, J. Clin. Invest.108 (2001) 18174.
[94] Li, L., Liu, H. and Han, Y., An approach to congestion analysis in crowd dynamics models, Math. Models Methods Appl. Sci.30 (2020) 867-890.
[95] Z. Liu, P. Magal, O. Seydi and G. Webb, Understanding unreported cases in the 2019-nCov epidemic outbreak in Wuhan, China, and the importance of major public health interventions, 2020 by the author(s). Distributed under a Creative Commons CC BY license.
[96] Lorenzo-Redondo, R.et al., Persistent HIV-1 replication maintains the tissue reservoir during therapy, Nature530 (2016) 51-56.
[97] Matricardi, P. M., Negro, R. W. Dal and Nisini, R., The first, holistic immunological model of COVID-19: Implications for prevention, diagnosis, and public health measures, Pediatr. Allergy Immunol. (2020). https://doi.org/10.1111/pai.13271
[98] MacLean, O. A., Orton, R. J., Singer, J. B. and Robertson, D. L., No evidence for distinct types in the evolution of SARS-CoV-2, Virus Evol.6 (2020) 034.
[99] May, R. M. and Anderson, R. A., Transmission dynamics of HIV infection, Nature362 (1987) 137-142.
[100] Moore, J. B. and June, C. H., Cytokine release syndrome in severe COVID-19, Science368 (2020) 473.
[101] Moreno-Gamez, S., Hill, A. L., Rosenbloom, D. I. S., Petrov, D. A., Nowak, M. A. and Pennings, P. S., Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance, Proc. Natl. Acad. Sci.112 (2015) E2874-E2883.
[102] Murray, J. D., Mathematical Biology II: Spatial Models and Biomedical Applications, 3rd edn. (Springer-Verlag, 2003), pp. 661-721.
[103] Nowak, M. A., Anderson, R. A., McLean, A. R., Wolfs, T. F. W., Goudsmit, J. and May, R. M., Antigenic diversity thresholds and the development of AIDS, Science254 (1991) 963-969.
[104] Nowak, M. A. and May, R. M., Mathematical biology of HIV infections: Antigenic variation and diversity threshold, Math. Biosci.106 (1991) 1-21. · Zbl 0738.92008
[105] Ojosnegros, S., Perales, C., Mas, A. and Domingo, E., Quasispecies as a matter of fact: Viruses and beyond, Virus Res.162 (2011) 203-215.
[106] N. M. A. Okba et al., Antibody responses in coronavirus disease 2019 patients, Emerg. Infect. Dis.267, doi:10.3201/eid2607.200841.
[107] Pawelec, G., Age and immunity: What is “Immunosenescence”?Exp. Gerontol.105 (2018) 4.
[108] Pareschi, L. and Toscani, G., Interacting Multiagent Systems: Kinetic Equations and Monte Carlo Methods (Oxford Univ. Press, 2013). · Zbl 1330.93004
[109] Perelson, A. S., Neumann, A. U., Markowitz, M., Leonard, J. M. and Ho, D. D., HIV-1 dynamics in vivo: Virion clearance rate, infected cell life-span, and viral generation time, Science271 (1996) 1582-1586.
[110] Perelson, A. S. and Nelson, P. W., Mathematical analysis of HIV-1 dynamics in vivo, SIAM Rev.41 (1999) 3-44. · Zbl 1078.92502
[111] Perelson, A. S., Modelling viral and immune system dynamics, Nat. Rev.. Immunol.2 (2002) 28-36.
[112] H. Rahmandad and J. Sterman, Heterogeneity and network structure in the dynamics of diffusion, Manag. Sci.54 (2008) 998-1014; 1 (2008) 249-278.
[113] Rouzine, I. M., Weinberger, A. D. and Weinberger, L. S., An evolutionary role for HIV latency in enhancing viral transmission, Cell160 (2015) 1002-1012.
[114] S. Roy, Dynamical asymmetry exposes 2019-nCoV prefusion spike, bioRxiv: https://doi.org/10.1101/2020.04.20.46.144.
[115] Sanjuán, R. and Domingo-Calap, P., Mechanisms of viral mutation, Cell. Mol. Life Sci.73 (2016) 4433-4448.
[116] Schrödinger, E., What is Life? The Physical Aspect of the Living Cell (Cambridge Univ. Press, 1944).
[117] Service, RF, NAS letter suggests “Normal Breathing” can expel coronavirus, Science368 (2020) 119.
[118] Sfakianakis, N., Madzvamuse, A. and Chaplain, M. A. J., A hybrid multiscale model for cancer invasion of the extracellular matrix, Multiscale Model. Simul.18(2) (2020) 824-850. · Zbl 1443.92080
[119] Scienza in Rete, On Line Journal, Gruppo2003 for Scientific Research, ed. L. Carra, https://www.zadig.it/progetti/scienza-in-rete-giornale-online-sulla-ricerca-scientifica/.
[120] Faraz, A. Syed, Quadeer, A. A. and McKay, M. R., Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies, Viruses12 (2020) 254.
[121] Taleb, N. N., The Black Swan: The Impact of the Highly Improbable (Random House, 2007).
[122] P. Terna, G. Pescarmona, A. Acquadro, P. Pescarmona, G. Russo and S. Terna, An agent-based model of the diffusion of covid-19 using NetLogo, https://terna.to.it/simul/SIsaR.html.
[123] S. Toki et al., TSLP and IL-33 reciprocally promote each other’s lung protein expression and ILC2 receptor expression to enhance innate type-2 airway inflammation, Allergy, doi:10.1111/all.14196.
[124] Twarock, R., A tiling approach to virus capsid assembly explaining a structural puzzle in virology, J. Theor. Biol.226 (2004) 477-482. · Zbl 1439.92146
[125] Twarock, R., Mathematical models for tubular structures in the family of Papovaviridae, Bul. Math. Biol.67 (2005) 973-987. · Zbl 1334.92151
[126] Twarock, R., A mathematical physicist’s approach to the structure and assembly of viruses, Philos. Trans. R. Soc. Lond. A365 (2006) 3357-3374. · Zbl 1154.92314
[127] Twarock, R., Geometry as a weapon in the fight against viruses, Math. TodayOctober (2019) 184-187.
[128] Twarock, R. and Luque, A., Structural puzzles in virology solved with an overarching icosahedral design principle, Nat. Commun.10 (2019) 4414.
[129] Twarock, R., Bingham, R. J., Dykeman, E. C. and Stockley, P. G., A modelling paradigm for RNA virus assembly, Curr. Opin. Virol.31 (2018) 74-81.
[130] Twarock, R., Leonov, G. and Stockley, P. G., Hamiltonian path analysis of viral genomes, Nat. Commun.9(2018) (2021).
[131] N. Vabret et al., Immunology of COVID-19: Current state of the science, Immunity, https://doi.org/10.1016/j.immuni.2020.05.002.
[132] van Doremalen, N.et al., Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV, New England J. Med.382 (2020) 1564.
[133] P. Vergilius Maro, Aeneid II, line 390.
[134] Wadman, M., Couzin-Frankel, J., Kaiser, J. and Matacic, C., A rampage through the body, Science368 (2020) 356.
[135] Wang, L., Short, M. B. and Bertozzi, A. L., Efficient numerical methods for multiscale crowd dynamics with emotional contagion, Math. Models Methods Appl. Sci.27 (2017) 205-230. · Zbl 1359.35197
[136] Wang, W.et al., Detection of SARS-CoV-2 in different types of clinical specimens, J. Am. Med. Assoc.323 (2020) 1843.
[137] Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C. L., Abiona, O., Graham, B. S. and McLellan, J. S., Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation, Science367 (2020) 1260-1263.
[138] Z. Zhou et al., Heightened innate immune responses in the respiratory tract of COVID-19 patients, Cell Host Microbes, https://doi.org/10.1016/j.chom.2020.04.017.
[139] Zhou, P.et al., A pneumonia outbreak associated with a new coronavirus of probable bat origin, Nature579 (2020) 270-273.
[140] Zhou, Y., Hou, Y., Shen, J., Huang, Y., Martin, W. and Cheng, F., Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2, Cell Discov.6 (2020) 14.
[141] Ziegler, C. G. K.et al., SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues, Cell181 (2020) 1016.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.