Mostrar el registro sencillo del ítem
dc.contributor.author | Baquero, Fernando | es_ES |
dc.contributor.author | Campos Frances, Marcelino | es_ES |
dc.contributor.author | Llorens, Carlos | es_ES |
dc.contributor.author | Sempere Luna, José María | es_ES |
dc.date.accessioned | 2022-02-28T19:03:13Z | |
dc.date.available | 2022-02-28T19:03:13Z | |
dc.date.issued | 2021-12 | es_ES |
dc.identifier.issn | 2523-8906 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/181143 | |
dc.description.abstract | [EN] In this paper, we present LOIMOS, which is an epidemiological scenario simulator developed in the context of the fight against the pandemic caused by coronavirus SARS-CoV-2 on a global scale. LOIMOS has been fully developed under the paradigm of membrane computing using transition P systems with communication rules, active membranes and a stochastic simulator engine. In this paper we detail the main components of the system and we report some examples of epidemiological scenarios evaluated with LOIMOS. | es_ES |
dc.description.sponsorship | This work has been developed with the financial support of the Ministerio de Ciencia e Innovacion and Instituto de Salud Carlos III, Grant COV20_00067, and the European Union's Horizon 2020 research and innovation programme under Grant agreement no. 952215 corresponding to the TAILOR project. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer | es_ES |
dc.relation.ispartof | Journal of Membrane Computing | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | P systems | es_ES |
dc.subject | Active membranes | es_ES |
dc.subject | Communication rules | es_ES |
dc.subject | Stochastic simulation | es_ES |
dc.subject | Epidemiological simulators | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | P systems in the time of COVID-19 | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s41965-021-00083-1 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/952215/EU/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ISCIII//COV20_00067/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Baquero, F.; Campos Frances, M.; Llorens, C.; Sempere Luna, JM. (2021). P systems in the time of COVID-19. Journal of Membrane Computing. 3(4):246-257. https://doi.org/10.1007/s41965-021-00083-1 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s41965-021-00083-1 | es_ES |
dc.description.upvformatpinicio | 246 | es_ES |
dc.description.upvformatpfin | 257 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 3 | es_ES |
dc.description.issue | 4 | es_ES |
dc.relation.pasarela | S\452734 | es_ES |
dc.contributor.funder | Instituto de Salud Carlos III | es_ES |
dc.contributor.funder | COMISION DE LAS COMUNIDADES EUROPEA | es_ES |
dc.description.references | Baquero, F., Campos, M., Llorens, C. & Sempere J. M. (2018). A model of antibiotic resistance evolution dynamics through P systems with active membranes and communication rule. In Enjoying Natural Computing: Essays Dedicated to Mario de Jesús Pérez-Jiménez on the Occasion of His 70th Birthday, pp. 33–44. Springer. | es_ES |
dc.description.references | Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., & Sledge, D. (2020). The challenges of modeling and forecasting the spread of COVID-19. Proceedings of the National Academy of Sciences, 117(29), 16732–16738. https://doi.org/10.1073/pnas.2006520117 | es_ES |
dc.description.references | Bullard, J., Dust, K., Funk, D., et al. (2020). Predicting infectious severe acute respiratory syndrome coronavirus 2 from diagnostic samples. Clinical Infectious Diseases, 71(10), 2663–2666. https://doi.org/10.1093/cid/ciaa638 | es_ES |
dc.description.references | Campos, M., Capilla, R., Naya, F., Futami, R., Coque, T., Moya, A., Fernandez-Lanza, V., Cantón, R., Sempere, J. M., Llorens, C., & Baquero, F. (2019). Simulating multilevel dynamics of antimicrobial resistance in a membrane computing model. mBio, 10(1), e02460–e024618. https://doi.org/10.1128/mBio.02460-18 | es_ES |
dc.description.references | Campos, M., Llorens, C., Sempere, J. M., Futami, R., Rodríguez, I., Carrasco, P., Capilla, R., Latorre, A., Coque, T., Moya, A., & Baquero, F. (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct, 10(41), 1–13. https://doi.org/10.1186/s13062-015-0070-9 | es_ES |
dc.description.references | Campos, M., San Millán, A., Sempere, J. M., Lanza, V. F., Coque, T., Llorens, C., & Baquero, F. (2020). Simulating the influence of conjugative-plasmid kinetic values on the multilevel dynamics of antimicrobial resistance in a membrane computing model. Antimicrobial Agents and Chemotherapy, 64(8), e00593–e005920. https://doi.org/10.1128/AAC.00593-20 | es_ES |
dc.description.references | Campos, M., Sempere, J. M., Galán, J. C., et al. (2021). Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model. microLife, 2, uqab011. https://doi.org/10.1093/femsml/uqab011 | es_ES |
dc.description.references | Dogra, P., Ruiz-Ramírez, J., Sinha, K., et al. (2021). Innate immunity plays a key role in controlling viral load in Covid-19: mechanistic insights from a whole-body infection dynamics model. ACS Pharmacology and Translational Science, 4(1), 248–265. https://doi.org/10.1021/acsptsci.0c00183 | es_ES |
dc.description.references | Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., et al. (2020). Clinical characteristics of coronavirus disease 2019 in China. The New England Journal of Medicine, 382(18), 1708–1720. https://doi.org/10.1056/NEJMoa2002032 | es_ES |
dc.description.references | Huang, A. T., Garcia-Carreras, B., Hitchings, M. D. T., et al. (2020). A systematic review of antibody mediated immunity to coronaviruses: Kinetics, correlates of protection, and association with severity. Nature Communications, 11(1), 4704. https://doi.org/10.1038/s41467-020-18450-4 | es_ES |
dc.description.references | Instituto Nacional de Estadística de España. (2020). Statistical information for the analysis of the impact of the COVID-19 crisis. https://www.ine.es/en/covid/covid_inicio_en.htm | es_ES |
dc.description.references | van Kampen, J. J. A., van de Vijver, D. A. M. C., Fraaij, P. L. A., et al. (2021). Duration and key determinants of infectious virus shedding in hospitalized patients with coronavirus disease-2019 (COVID-19). Nature Communications, 12, 267. https://doi.org/10.1038/s41467-020-20568-4 | es_ES |
dc.description.references | Kim, Y., Kim, S., Park, S., et al. (2021). Critical role of neutralizing antibody for SARS-CoV-2 reinfection and transmission. Emerging Microbes and Infections, 10(1), 152–160. https://doi.org/10.1080/22221751.2021.1872352 | es_ES |
dc.description.references | Kermack, W. O., & McKendrick, G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115, 700–721. https://doi.org/10.1098/rspa.1927.0118 | es_ES |
dc.description.references | La Scola, B., Le Bideau, M., Andreani, J., Hoang, V. T., Grimaldier, C., Colson, P., Gautret, P., & Raoult, D. (2020). Viral RNA load as determined by cell culture as a management tool for discharge of SARS-CoV-2 patients from infectious disease wards. European Journal of Clinical Microbiology and Infectious Diseases, 39(6), 1059–1061. https://doi.org/10.1007/s10096-020-03913-9 | es_ES |
dc.description.references | Lee, S., Kim, T., Lee, E., Lee, C., et al. (2020). Clinical course and molecular viral shedding among asymptomatic and symptomatic patients with SARS-CoV-2 infection in a Community Treatment Center in the Republic of Korea. JAMA Internal Medicine, 180(11), 1447–1452. https://doi.org/10.1001/jamainternmed.2020.3862 | es_ES |
dc.description.references | Martínez del Amor, M. A., Pérez-Hurtado, I., García-Quismondo, M., Macías-Ramos, L. F., Valencia-Cabrera, L., Romero-Jiménez, A., et al. (2013). DCBA: Simulating population dynamics P systems with proportional object distribution. In E. Csuhaj-Varju, M. Gheorghe, G. Rozenberg, A. Salomaa, & G. Vaszil (Eds.), Membrane computing, LNCS (Vol. 7762, pp. 257–276). Springer. | es_ES |
dc.description.references | Newman, M. E. J. (2010). Networks. An introduction. Oxford University Press. | es_ES |
dc.description.references | Păun, Gh. (2000). Computing with membranes. Journal of Computer and System Sciences, 61(1), 108–143. | es_ES |
dc.description.references | Păun, G. (2002). Membrane computing. An introduction. Springer. | es_ES |
dc.description.references | Păun, G., Rozenberg, G., & Salomaa, A. (Eds.). (2010). The Oxford handbook of membrane computing. Oxford University Press. | es_ES |
dc.description.references | Romero-Campero, F. J., & Pérez-Jiménez, M. J. (2008). A model of the quorum sensing system in Vibrio fischeri using P systems. Artificial Life, 14, 95–109. https://doi.org/10.1162/artl.2008.14.1.95 | es_ES |
dc.description.references | Singanayagam, A., Patel, M., Charlett, A., Lopez Bernal, J., Saliba, V., Ellis, J., Ladhani, S., Zambon, M., & Gopal, R. (2020). Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020. Eurosurveillance. https://doi.org/10.2807/1560-7917.ES.2020.25.32.2001483 | es_ES |
dc.description.references | Wong, G. N., Weiner, Z. J., Tkachenko, A. V., et al. (2020). Modeling COVID-19 dynamics in Illinois under nonpharmaceutical interventions. Physical Review X, 10(4), 041033. https://doi.org/10.1103/PhysRevX.10.041033 | es_ES |
dc.subject.ods | 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades | es_ES |