Research article Special Issues

What is the in-host dynamics of the SARS-CoV-2 virus? A challenge within a multiscale vision of living systems

  • Received: 28 December 2023 Revised: 24 May 2024 Accepted: 25 June 2024 Published: 27 June 2024
  • This paper deals with the modeling and simulation of the in-host dynamics of a virus. The modeling approach was developed according to the idea that mathematical models should go beyond deterministic single-scale population dynamics by taking into account the multiscale, heterogeneous features of the complex system under consideration. Here, we considered modeling the competition between the virus, the epithelial cells it infects, and the heterogeneous immune system with evolving activation states that induce a range of different effects on virus particles and infected cells. The subsequent numerical simulations showed different types of model outcomes: from virus elimination, to virus persistence and periodic relapse, to virus uncontrolled growth that triggers a blow-up in the fully activated immune response. The simulations also showed the existence of a threshold in the immune response that separates the regimes of higher re-infections from lower re-infections (compared to the magnitude of the first viral infection).

    Citation: Nicola Bellomo, Raluca Eftimie, Guido Forni. What is the in-host dynamics of the SARS-CoV-2 virus? A challenge within a multiscale vision of living systems[J]. Networks and Heterogeneous Media, 2024, 19(2): 655-681. doi: 10.3934/nhm.2024029

    Related Papers:

  • This paper deals with the modeling and simulation of the in-host dynamics of a virus. The modeling approach was developed according to the idea that mathematical models should go beyond deterministic single-scale population dynamics by taking into account the multiscale, heterogeneous features of the complex system under consideration. Here, we considered modeling the competition between the virus, the epithelial cells it infects, and the heterogeneous immune system with evolving activation states that induce a range of different effects on virus particles and infected cells. The subsequent numerical simulations showed different types of model outcomes: from virus elimination, to virus persistence and periodic relapse, to virus uncontrolled growth that triggers a blow-up in the fully activated immune response. The simulations also showed the existence of a threshold in the immune response that separates the regimes of higher re-infections from lower re-infections (compared to the magnitude of the first viral infection).


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    [1] J. P. Agnelli, B. Buffa, D. A. Knopoff, G. Torres, A spatial kinetic model of crowd evacuation dynamics with infectious disease contagion, Bull Math Biol, 85(2023), 23. https://doi.org/10.1007/s11538-023-01127-6 doi: 10.1007/s11538-023-01127-6
    [2] M. Aguiar, G. Dosi, D. A. Knopoff, M.E. Virgillito, A multiscale network-based model of contagion dynamics: heterogeneity, spatial distancing and vaccination, Math Models Methods Appl Sci, 31 (2021), 2425–2570. https://doi.org/10.1142/S0218202521500524 doi: 10.1142/S0218202521500524
    [3] K. G. Andersen, A. Rambaut, W. Ian Lipkin, E. C. Holmes, R. F. Garry, The proximal origin of SARS-CoV-2, Nat. Med., 26 (2020), 450–452. https://doi.org/10.1038/s41591-020-0820-9 doi: 10.1038/s41591-020-0820-9
    [4] S. Asgari, L. A. Pousaz, Human genetic variants identified that affect Covid susceptibility and severity, Nature, 600 (2021), 690–691. https://doi.org/10.1038/s41586-021-04210-x doi: 10.1038/s41586-021-04210-x
    [5] A. Atifa, M. A. Khan, K. Isakakova, F. S. Al-Duais, I. Ahmad, Mathematical modelling and analysis of the SARS-CoV-2 disease with reinfection, Comput. Biol. Chem., 98 (2022), 107678. https://doi.org/10.1016/j.compbiolchem.2022.107678 doi: 10.1016/j.compbiolchem.2022.107678
    [6] B. Avishai, The pandemic isn't a black swan but a portent of a more fragile global system. The New Yorker, 2020. Available from: https://www.newyorker.com/news/daily-comment/the-pandemic-isnt-a-black-swan-but-a-portent-of-a-more-fragile-global-system
    [7] Y. M. Bar-On, A. Flamholz, R. Phillips, R. Milo, SARS-CoV-2 (COVID-19) by the numbers, eLife, 9, e57309, (2020). https://doi.org/10.7554/eLife.57309
    [8] N. Bellomo, R. Bingham, M. Chaplain, G. Dosi, G. Forni, D. Knopoff, et al., A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world, Math Models Methods Appl Sci, 30 (2020), 1591–1651. https://doi.org/10.1142/S0218202520500323 doi: 10.1142/S0218202520500323
    [9] N. Bellomo, F. Brezzi, M. Chaplain, Modelling Virus pandemics in a globally connected world, a challenge towards a mathematics for living lystems, Math Models Methods Appl Sci, 31 (2021), 2391–2397. https://doi.org/10.1142/S0218202521020024 doi: 10.1142/S0218202521020024
    [10] N. Bellomo, D. Burini, G. Dosi, L. Gibelli, D. A. Knopoff, N. Outada, et al., What is life? A perspective of the mathematical kinetic theory of active particles, Math Models Methods Appl Sci, 31 (2021), 1821–1866. https://doi.org/10.1142/S0218202521500408 doi: 10.1142/S0218202521500408
    [11] N. Bellomo, D. Burini, N. Outada, Multiscale models of Covid-19 with mutations and variants, Netw. Heterog. Media., 17 (2022), 293–310. https://doi.org/10.3934/nhm.2022008 doi: 10.3934/nhm.2022008
    [12] N. Bellomo, D. Burini, N. Outada, Pandemics of Mutating Virus and Society: A multi-scale active particles approach, Philos. Trans. Royal Soc. A, 380 (2022), 20210161. https://doi.org/10.1098/rsta.2021.0161 doi: 10.1098/rsta.2021.0161
    [13] N. Bellomo, L. Gibelli, N. Outada, On the interplay between behavioral dynamics and social interactions in human crowds, Kinet. Relat. Models, 12 (2019), 397–409. https://doi.org/10.3934/krm.2019017 doi: 10.3934/krm.2019017
    [14] G. Bertaglia, L. Pareschi, Hyperbolic compartmental models for epidemic spread on networks with uncertain data: application to the emergence of Covid-19 in Italy, Math Models Methods Appl Sci, 31 (2021), 2495–2531. https://doi.org/10.1142/S0218202521500548 doi: 10.1142/S0218202521500548
    [15] G. Bertaglia, A. Bondesan, D. Burini, R. Eftimie, L. Pareschi, G. Toscani, New trends on the systems approach to modeling SARS-CoV-2 pandemics in a globally connected planet, Math Models Methods Appl Sci, (2024). https://doi.org/10.1142/S0218202524500301
    [16] A. L. Bertozzi, E. Franco, G. Mohler, M. B. Short, D. Sledge, The challenges of modeling and forecasting the spread of COVID-19, Proc. Natl. Acad. Sci., 117 (2020), 16732–16738. https://doi.org/10.1073/pnas.2006520117 doi: 10.1073/pnas.2006520117
    [17] J. Borghans, R. M. Ribeiro, The maths of memory, eLife, 6 (2017), e26754. https://doi.org/10.7554/eLife.26754
    [18] D. Burini, D. A. Knopoff, Epidemics and society—A Multiscale vision from the small world to the globally interconnected world, Math Models Methods Appl Sci, 34 (2024), 1564–1594. https://doi.org/10.1142/S0218202524500295 doi: 10.1142/S0218202524500295
    [19] J. D. Challenger, C. Y. Foo, Y. Wu, A. W. C. Yan, M. M. Marjaneh, F. Liew, et al., Modelling upper respiratory viral load dynamics of SARS-CoV-2, BMC Med, 20 (2022), 25. https://doi.org/10.1186/s12916-021-02220-0 doi: 10.1186/s12916-021-02220-0
    [20] R. J. De Boer, D. Homann, A. S. Perelson, Different dynamics of CD4$^{+}$ and CD8$^{+}$ T cell responses during and after acute lymphocytic choriomeningitis virus infection, J Immunol, 171 (2003), 3928–3935. https://doi.org/10.4049/jimmunol.171.8.3928 doi: 10.4049/jimmunol.171.8.3928
    [21] J. Demongeot, Q. Griette, P. Magal, G. Webb, Vaccine efficacy for COVID-19 outbreak in New York City, Biology, 11 (2022), 345. https://doi.org/10.3390/biology11030345 doi: 10.3390/biology11030345
    [22] M. S. Diamond, T. D. Kanneganti, Innate immunity: the first line of defense against SARS-CoV-2, Nat Immunol, 23 (2022), 165–176. https://doi.org/10.1038/s41590-021-01091-0 doi: 10.1038/s41590-021-01091-0
    [23] G. Dosi, L. Fanti, M. E. Virgillito, Unequal societies in usual times, unjust societies in pandemic ones, J. Ind. Bus. Econ., 47 (2020), 371–389. https://doi.org/10.1007/s40812-020-00173-8 doi: 10.1007/s40812-020-00173-8
    [24] R. Eftimie, Grand challenges in mathematical biology: Integrating multi-scale modeling and data, Front Ecol Environ, 8 (2022), 1010622. https://doi.org/10.3389/fams.2022.1010622 doi: 10.3389/fams.2022.1010622
    [25] I. Eizenberg-Magar, I. Rimer, I. Zaretsky, N. Friedman, Diverse continuum of CD4$^{+}$ T-cell states is determined by hierarchical additive integration of cytokine signals, Proc. Natl. Acad. Sci., 114 (2017), E6447–E6456. https://doi.org/10.1073/pnas.1615590114 doi: 10.1073/pnas.1615590114
    [26] S. El Zein, O. Chehab, A. Kanj, S. Akrawe, S. Alkassis, T. Mishra et al., SARS-CoV-2 infection: Initial viral load (iVL) predicts severity of illness/outcome, and declining trend of iVL in hospitalized patients corresponds with slowing of the pandemic, PLoS One, 16 (2021), e0255981. https://doi.org/10.1371/journal.pone.0255981 doi: 10.1371/journal.pone.0255981
    [27] M. Elemans, N. K. S. Al Basatena, B. Asquith, The efficiency of the human CD8+ T cell response: how should we quantify it, what determines it, and does it matter? Plos Comput Biol, 8 (2012), e1002381. https://doi.org/10.1371/journal.pcbi.1002381
    [28] F. Flandoli, E. La Fauci, M. Riva, Individual-based Markov model of virus diffusion: Comparison with COVID-19 incubation period, serial interval and regional time series, Math Models Methods Appl Sci, 31 (2021), 907–939. https://doi.org/10.1142/S0218202521500226 doi: 10.1142/S0218202521500226
    [29] J. F. Fontanari, A stochastic model for the influence of social distancing on loneliness, Physica A, 584 (2021), 126367.
    [30] M. Gatto, E. Bertuzzo, L. Mari, S. Miccoli, L. Carraro, R. Casagrandi, et al., Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures, Proc. Natl. Acad. Sci., 117 (2020), 10484–10491. https://doi.org/10.1073/pnas.2004978117 doi: 10.1073/pnas.2004978117
    [31] N. M. Gerhards, J. B. W. J. Cornelissen, L. J. M. van Keulen, J. Harders-Westerveen, R. Vloet, B. Smid, et. al., Predictive value of precision-cut lung slices for the susceptibility of three animal species for SARS-CoV-2 and validation in a refined hamster model, Pathogens, 10 (2021), 824. https://doi.org/10.3390/pathogens10070824 doi: 10.3390/pathogens10070824
    [32] G. Gessain, C. Blériot, F. Ginhoux, Non-genetic heterogeneity of macrophages in diseases–a medical perspective, Front. Cell. Dev. Biol., 8 (2020), 613116. https://doi.org/10.3389/fcell.2020.613116 doi: 10.3389/fcell.2020.613116
    [33] C. Franceschi, S. Salvioli, P. Garagnani, M de Eguileor, D. Monti, M.Capri, Immunobiography and the heterogeneity of immune responses in the elderly: a focus on inflammaging and trained immunity, Front. Immunol., 8 (2017), 982. https://doi.org/10.3389/fimmu.2017.00982 doi: 10.3389/fimmu.2017.00982
    [34] J. F. Gianlupi, T. Mapder, T. J. Sego, J. P. Sluka, S. K. Quinney, M. Craig, et al., Multiscale model of antiviral timing, potency, and heterogeneity effects on an epithelial tissue patch infected by SARS-CoV-2, Viruses, 14 (2022), 605. https://doi.org/10.3390/v14030605 doi: 10.3390/v14030605
    [35] C. H. Hansen, D. Michlmayr, S. M. Gubbels, K. Mølbak, S. Ethelberg, Assessment of protection against reinfection with SARS-CoV-2 among 4 million PCR-tested individuals in Denmark in 2020: a population-level observational study, Lancet, 397 (2021), 1204–1212. https://doi.org/10.1016/S0140-6736(21)00575-4 doi: 10.1016/S0140-6736(21)00575-4
    [36] P. Hardy, L. S. Marcolino, J. F. Fontanari, The paradox of productivity during quarantine: an agent-based simulation, Eur. Phys. J. B., 94 (2021), 40. https://doi.org/10.1140/epjb/s10051-020-00016-4 doi: 10.1140/epjb/s10051-020-00016-4
    [37] S. Karimzadeh, R. Bophal, H. N. Tien, Review of infective dose, routes of transmission and outcome of COVID-19 caused by the SARS-CoV-2: comparison with other respiratory viruses, Epidemiol. Infect., 149 (2021), e96. https://doi.org/10.1017/S0950268821000790 doi: 10.1017/S0950268821000790
    [38] R. Karki, B. R. Sharma, S. Tuladhar, E. P. Williams, L. Zalduondo, P. Samir, et al., Synergism of TNF-$\alpha$ and IFN-$\gamma$ triggers inflammatory cell death, tissue damage, and mortality in SARS-CoV-2 infection and cytokine shock syndrome, Cell, 184 (2021), 149–168. https://doi.org/10.1016/j.cell.2020.11.025 doi: 10.1016/j.cell.2020.11.025
    [39] D. Kim, A. Quaini, Coupling kinetic theory approaches for pedestrian dynamics and disease contagion in a confined environment, Math Models Methods Appl Sci, 30 (2020), 1893–1915. https://doi.org/10.1142/S0218202520400126 doi: 10.1142/S0218202520400126
    [40] S. M. Kissler, C. Tedijanto, E. Goldstein, Y. H. Grad, M. Lipsitch, Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period, Science, 368 (2020), 860–868. https://doi.org/10.1126/science.abb5793 doi: 10.1126/science.abb5793
    [41] Z. Liu, P. Magal, O. Seydi, G. Webb, A model to predict COVID-19 epidemics with applications to South Korea, Italy, and Spain, SIAM News (2020). Available from: https://sinews.siam.org/Details-Page/a-model-to-predict-covid-19-epidemics-with-applications-to-south-korea-italy-and-spain
    [42] S. M. Lynch, G. Guo, D. S. Gibson, A. J. Bjourson, T. Singh Ra, Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease, Cells, 10 (2021), 3367–3372. https://doi.org/10.3390/cells10123367 doi: 10.3390/cells10123367
    [43] D. C. Macallan, R. Busch, B. Asquith, Current estimates of T cell kinetics in humans, Curr. Opin. Syst. Biol., 18 (2019), 77–86. https://doi.org/10.1016/j.coisb.2019.10.002 doi: 10.1016/j.coisb.2019.10.002
    [44] A. Mantovani, M. Rescigno, G. Forni, F. Tognon, G. Putoto, J. Ictho, P. Lochoro, COVID-19 vaccines and a perspective on Africa, Trends Immunol, 44 (2023), 172–187. https://doi.org/10.1016/j.it.2023.01.005 doi: 10.1016/j.it.2023.01.005
    [45] J. S. Marshall, R. Warrington, W. Watson, H. L. Kim, An introduction to immunology and immunopathology, Allergy Asthma CL IM, 14 (2018), 49. https://doi.org/10.1186/s13223-018-0289-y doi: 10.1186/s13223-018-0289-y
    [46] M. Massard, R. Eftimie, A. Perasso, B. Saussereau, A multi-strain epidemic model for COVID-19 with infected and asymptomatic cases: application to French data, J. Theor. Biol., 545 (2022), 111117. https://doi.org/10.1016/j.jtbi.2022.111117 doi: 10.1016/j.jtbi.2022.111117
    [47] P. M. Matricardi, R. W. Dal Negro, R. Nisini, The first, holistic immunological model of COVID-19: Implications for prevention, diagnosis, and public health measures, Pediatr Allergy Immunol, 31 (2020), 454–470. https://doi.org/10.1111/pai.13271 doi: 10.1111/pai.13271
    [48] E. Meffre, A. Iwasaki, Interferon deficiency can lead to severe COVID, Nature, 587 (2020), 374–376. https://doi.org/10.1038/d41586-020-03070-1 doi: 10.1038/d41586-020-03070-1
    [49] P. Moss, The T cell immune response against SARS-CoV-2, Nat. Immunol., 23 (2022), 186–193. https://doi.org/10.1038/s41590-021-01122-w doi: 10.1038/s41590-021-01122-w
    [50] P. Musiani, G. Forni, Basic Immunology, Padua: Piccin, 2018.
    [51] M. G. Netea, J. Dominguez-Andrés, L. B. Barreiro, T. Chavakis, M. Divangahi, E. Fuchs, et al., Defining trained immunity and its role in health and disease, Nat. Rev. Immunol., 20 (2020), 375–388. https://doi.org/10.1038/s41577-020-0285-6 doi: 10.1038/s41577-020-0285-6
    [52] A. H. Newton, A. Cardani, T. J. Braciale, The host immune response in respiratory virus infection: balancing virus clearance and immunopathology, Semin. Immunol., 38 (2016), 471–482. https://doi.org/10.1007/s00281-016-0558-0 doi: 10.1007/s00281-016-0558-0
    [53] J. Niessl, T. Sekine, M. Buggert, T cell immunity to SARS-CoV-2, Seminars in Immunology, 55 (2021), 101505. https://doi.org/10.1016/j.smim.2021.101505 doi: 10.1016/j.smim.2021.101505
    [54] A. Paolini, R. Borella, S. De Biasi, A. Neroni, M. Mattioli, D. Lo Tartaro, et al., Cell death in coronavirus infections: uncovering its role during COVID-19, Cells, 10 (2021), 1585. https://doi.org/10.3390/cells10071585 doi: 10.3390/cells10071585
    [55] B. Perthame, Transport Equations in Biology, Boston: Birkhäuser Basel, 2006.
    [56] J. J. Pinney, F. Rivera-Escalera, C. C. Chu, H. E. Whitehead, K.R. VanDerMeid, A.M. Nelson, et al., Macrophage hypophagia as a mechanism of innate immune exhaustion in mAb-induced cell clearance, Blood, 136 (2020), 2065–2079. https://doi.org/10.1182/blood.2020005571 doi: 10.1182/blood.2020005571
    [57] D. Pople, E. J. M. Monk, S. Evans, S. Foulkes, J. Islam, E. Wllington, et al., Burden of SARS-CoV-2 infection in healthcare workers during second wave in England and impact of vaccines: prospective multicentre cohort study (SIREN) and mathematical model, BMJ, 378 (2022), e070379. https://doi.org/10.1136/bmj-2022-070379 doi: 10.1136/bmj-2022-070379
    [58] M. Renardy, C. Hult, S. Evans, J. J. Linderman, D. E. Kirschner, Global sensitivity analysis of biological multiscale models, Curr. Opin. Biomed. Eng., 11 (2019), 109–116. https://doi.org/10.1016/j.cobme.2019.09.012 doi: 10.1016/j.cobme.2019.09.012
    [59] D. Ricci, M. P. Etna, F. Rizzo, S. Sandini, M. Severa, E. M. Coccia, Innate immune response to SARS-CoV-2 infection: from cells to soluble mediators, Int. J. Mol. Sci., 22 (2021), 7017. https://doi.org/10.3390/ijms22137017 doi: 10.3390/ijms22137017
    [60] R. Robinot, M. Hubert, G. Dias de Melo, F. Lazarini, T. Bruel, N. Smith, et al., SARS-CoV-2 infection induces the dedifferentiation of multiciliated cells and impairs mucociliary clearance, Nat Commun, 12 (2021), 4354. https://doi.org/10.1038/s41467-021-24521-x doi: 10.1038/s41467-021-24521-x
    [61] Royal Society (Coordinator), RAMP: A call for assistance, addressed to the scientific modelling community. Coordinated by Mark Chaplain, 2021. Available from: https://epcced.github.io/ramp/
    [62] G. Seminara, B. Carli, G. Forni, S. Fuzzi, A. Mazzino, A. Rinaldo, Biological fluid dynamics of airborne COVID.19 infection, Rend. Fis. Acc. Lincei, 31 (2020), 505–537. https://doi.org/10.1007/s12210-020-00938-2 doi: 10.1007/s12210-020-00938-2
    [63] A. Seller, C. Hackenbruch, J. S. Walz, A. Nelde, J. S. Heitmann, Long-term follow-up of COVID-19 convalescents–immune response associated with reinfection rate and symptoms, Viruses, 15 (2023), 2100. https://doi.org/10.3390/v15102100 doi: 10.3390/v15102100
    [64] R. Sender, Y. M. Bar-On, S. Gleizer, B. Bernshtein, A. Flamholz, R. Phillips, et al., The total number and mass of SARS-CoV-2 virions, Proc. Natl. Acad. Sci., 118 (2021), e2024815118. https://doi.org/10.1073/pnas.2024815118 doi: 10.1073/pnas.2024815118
    [65] A. Sette, S. Crotty, Adaptive immunity to SARS-CoV-2 and COVID-19, Cell, 184 (2021), 861–880. https://doi.org/10.1016/j.cell.2021.01.007 doi: 10.1016/j.cell.2021.01.007
    [66] H. Shen, D. Chen, C. Li, T. Huang, W. Ma, A mini review of reinfection with the SARS-CoV-2 Omicron variant, Health Sci. Rep., 7 (2024), e2016. https://doi.org/10.1002/hsr2.2016 doi: 10.1002/hsr2.2016
    [67] A. T. Tan, M. Linster, C. W. Tan, N. L. Bert, W. N. Chia, K. Kunasegaran, et al., Early induction of functional SARS-CoV-2-specific T cells associates with rapid viral clearance and mild disease in COVID-19 patients, Cell Rep., 34 (2021), 108728. https://doi.org/10.1016/j.celrep.2021.108728 doi: 10.1016/j.celrep.2021.108728
    [68] G. Toscani, P. Sen, S. Biswas, Kinetic exchange models of societies and economies, Philos. Trans. Royal Soc. A, 380 (2022), 20210170. https://doi.org/10.1098/rsta.2021.0170 doi: 10.1098/rsta.2021.0170
    [69] The University of Edinburgh, Review: what is the infectious dose of SARS-CoV-2? Usher Institute, (2021). Available from: https://www.ed.ac.uk/files/atoms/files/uncover_029-01_review_infectious_dose_of_covid-19.pdf.
    [70] J. D. Van Belleghem, P. L. Bollyky, Macrophages and innate immune memory against Staphylococcus skin infections, Proc. Natl. Acad. Sci., 115 (2018), 11865–11867. https://doi.org/10.1073/pnas.1816935115 doi: 10.1073/pnas.1816935115
    [71] E. Vazquez-Alejo, L. Tarancon-Diez, M. de la Sierra Espinar-Buitrago, M. Genebat, A. Calderón, G. Pérez-Cabeza, et al., Persistent exhausted T-cell immunity after severe COVID-19: 6-month evaluation in a prospective observational study, J. Clin. Med., 12 (2023), 3539. https://doi.org/10.3390/jcm12103539 doi: 10.3390/jcm12103539
    [72] D. J. Verdon, M. Mulazzani, M.R. Jenkins, Cellular and molecular mechanisms of CD8$^{+}$ T cell differentiation, dysfunction and exhaustion, Int. J. Mol. Sci., 21 (2020), 7357. https://doi.org/10.3390/ijms21197357 doi: 10.3390/ijms21197357
    [73] W. Van Damme, R. Dahake, R. van de Pas, G. Vanham, Y. Assefa, COVID-19: Does the infectious inoculum dose-response relationship contribute to understanding heterogeneity in disease severity and transmission dynamics? Med. Hypotheses, 146 (2021), 110431. https://doi.org/10.1016/j.mehy.2020.110431
    [74] S. Wang, M. Hao, Z. Pan, J. Lei, X. Zou, Data-driven multiscale mathematical modeling of SARS-CoV-2 infection revels heterogeneity among COVID-19 patients, PLoS Comput. Biol., 17 (2021), e1009587. https://doi.org/10.1371/journal.pcbi.1009587 doi: 10.1371/journal.pcbi.1009587
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