000058456 001__ 58456 000058456 005__ 20200221144317.0 000058456 0247_ $$2doi$$a10.1103/PhysRevE.94.052316 000058456 0248_ $$2sideral$$a97460 000058456 037__ $$aART-2016-97460 000058456 041__ $$aeng 000058456 100__ $$aEstrada, E. 000058456 245__ $$aEpidemic spreading in random rectangular networks 000058456 260__ $$c2016 000058456 5060_ $$aAccess copy available to the general public$$fUnrestricted 000058456 5203_ $$aThe use of network theory to model disease propagation on populations introduces important elements of reality to the classical epidemiological models. The use of random geometric graphs (RGGs) is one of such network models that allows for the consideration of spatial properties on disease propagation. In certain real-world scenarios - like in the analysis of a disease propagating through plants - the shape of the plots and fields where the host of the disease is located may play a fundamental role in the propagation dynamics. Here we consider a generalization of the RGG to account for the variation of the shape of the plots or fields where the hosts of a disease are allocated. We consider a disease propagation taking place on the nodes of a random rectangular graph and we consider a lower bound for the epidemic threshold of a susceptible-infected-susceptible model or a susceptible-infected-recovered model on these networks. Using extensive numerical simulations and based on our analytical results we conclude that (ceteris paribus) the elongation of the plot or field in which the nodes are distributed makes the network more resilient to the propagation of a disease due to the fact that the epidemic threshold increases with the elongation of the rectangle. These results agree with accumulated empirical evidence and simulation results about the propagation of diseases on plants in plots or fields of the same area and different shapes. 000058456 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P$$9info:eu-repo/grantAgreement/EC/FP7/317532/EU/Foundational Research on MULTIlevel comPLEX networks and systems/MULTIPLEX 000058456 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000058456 590__ $$a2.366$$b2016 000058456 591__ $$aPHYSICS, MATHEMATICAL$$b6 / 55 = 0.109$$c2016$$dQ1$$eT1 000058456 591__ $$aPHYSICS, FLUIDS & PLASMAS$$b10 / 31 = 0.323$$c2016$$dQ2$$eT1 000058456 592__ $$a1.27$$b2016 000058456 593__ $$aCondensed Matter Physics$$c2016$$dQ1 000058456 593__ $$aStatistics and Probability$$c2016$$dQ1 000058456 593__ $$aStatistical and Nonlinear Physics$$c2016$$dQ1 000058456 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000058456 700__ $$aMeloni, S. 000058456 700__ $$aSheerin, M. 000058456 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Y.$$uUniversidad de Zaragoza 000058456 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica 000058456 773__ $$g94, 5 (2016), 052316 [9 pp.]$$pPhys. rev., E$$tPhysical Review E$$x2470-0045 000058456 8564_ $$s684656$$uhttps://zaguan.unizar.es/record/58456/files/texto_completo.pdf$$yVersión publicada 000058456 8564_ $$s133338$$uhttps://zaguan.unizar.es/record/58456/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000058456 909CO $$ooai:zaguan.unizar.es:58456$$particulos$$pdriver 000058456 951__ $$a2020-02-21-13:39:39 000058456 980__ $$aARTICLE