000079082 001__ 79082
000079082 005__ 20191127155455.0
000079082 0247_ $$2doi$$a10.1016/j.jtbi.2018.05.004
000079082 0248_ $$2sideral$$a106377
000079082 037__ $$aART-2018-106377
000079082 041__ $$aeng
000079082 100__ $$aArias, J.H.
000079082 245__ $$aEpidemics on plants: Modeling long-range dispersal on spatially embedded networks
000079082 260__ $$c2018
000079082 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079082 5203_ $$aHere we develop an epidemic model that accounts for long-range dispersal of pathogens between plants. This model generalizes the classical compartmental models–Susceptible-Infected-Susceptible (SIS) and Susceptible-Infected-Recovered (SIR)–to take into account those factors that are key to understand epidemics in real plant populations. These ingredients are the spatial characteristics of the plots and fields in which plants are embedded and the effect of long-range dispersal of pathogens. The spatial characteristics are included through the use of random rectangular graphs which allow to consider the effects of the elongation of plots and fields, while the long-range dispersal is implemented by considering transformations, such as the Mellin and Laplace transforms, of a generalization of the adjacency matrix of the geometric graph. Our results point out that long-range dispersal favors the propagation of pathogens while the elongation of plant plots increases the epidemic threshold and decreases dramatically the number of affected plants. Interestingly, our model is able of reproducing the existence of patchy regions of infected plants and the absence of a clear propagation front centered in the initial infected plants, as it is observed in real plant epidemics.
000079082 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/E19$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P
000079082 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000079082 590__ $$a1.875$$b2018
000079082 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b24 / 59 = 0.407$$c2018$$dQ2$$eT2
000079082 591__ $$aBIOLOGY$$b39 / 87 = 0.448$$c2018$$dQ2$$eT2
000079082 592__ $$a0.711$$b2018
000079082 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2018$$dQ1
000079082 593__ $$aApplied Mathematics$$c2018$$dQ1
000079082 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2018$$dQ1
000079082 593__ $$aStatistics and Probability$$c2018$$dQ1
000079082 593__ $$aMedicine (miscellaneous)$$c2018$$dQ1
000079082 593__ $$aModeling and Simulation$$c2018$$dQ1
000079082 593__ $$aImmunology and Microbiology (miscellaneous)$$c2018$$dQ1
000079082 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000079082 700__ $$0(orcid)0000-0002-3484-6413$$aGómez-Gardeñes, J.$$uUniversidad de Zaragoza
000079082 700__ $$0(orcid)0000-0001-6202-3302$$aMeloni, S.$$uUniversidad de Zaragoza
000079082 700__ $$aEstrada, E.
000079082 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000079082 7102_ $$12003$$2395$$aUniversidad de Zaragoza$$bDpto. Física Materia Condensa.$$cÁrea Física Materia Condensada
000079082 773__ $$g453 (2018), 1-13$$pJ. theor. biol.$$tJournal of Theoretical Biology$$x0022-5193
000079082 8564_ $$s1668215$$uhttps://zaguan.unizar.es/record/79082/files/texto_completo.pdf$$yPostprint
000079082 8564_ $$s46517$$uhttps://zaguan.unizar.es/record/79082/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000079082 909CO $$ooai:zaguan.unizar.es:79082$$particulos$$pdriver
000079082 951__ $$a2019-11-27-15:47:15
000079082 980__ $$aARTICLE