title: Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic.
abstract: Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.
authors.affiliation: Dpto de Ecología Funcional y Evolutiva, Estación Experimental de Zonas Aridas (EEZA-CSIC), Ctra. de Sacramento s/n, La Cañada de San Urbano, 04120, Almería, Spain.|Dpto de Ecología, University of Granada, Granada, Spain.
authors.forename: José M
authors.initials: JM
authors.lastname: Gómez
authors.affiliation: Centro de Investigaciones sobre Desertificación (CSIC-UV-GV), Valencia, Spain.
authors.forename: Miguel
authors.initials: M
authors.lastname: Verdú
mesh_terms: D000352:Africa, Northern / Q000453:epidemiology; D001208:Asia / Q000453:epidemiology; D002947:Cities / Q000453:epidemiology* / Q000266:history; D003132:Commerce / Q000266:history / Q000706:statistics & numerical data; D019058:Community Networks / Q000266:history / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005060:Europe / Q000453:epidemiology; D049691:History, Medieval; D006801:Humans; D015233:Models, Statistical; D058873:Pandemics; D010930:Plague / Q000453:epidemiology* / Q000401:mortality / Q000635:transmission*; D016019:Survival Analysis; D014195:Travel / Q000266:history / Q000706:statistics & numerical data; D015010:Yersinia pestis / Q000472:pathogenicity*