%0 Journal Article
%T Long-Distance Travel Behaviours Accelerate and Aggravate the Large-Scale Spatial Spreading of Infectious Diseases
%A Zhijing Xu
%A Zhenghu Zu
%A Tao Zheng
%A Wendou Zhang
%A Qing Xu
%A Jinjie Liu
%J Computational and Mathematical Methods in Medicine
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/295028
%X The study analyses the role of long-distance travel behaviours on the large-scale spatial spreading of directly transmitted infectious diseases, focusing on two different travel types in terms of the travellers travelling to a specific group or not. For this purpose, we have formulated and analysed a metapopulation model in which the individuals in each subpopulation are organised into a scale-free contact network. The long-distance travellers between the subpopulations will temporarily change the network structure of the destination subpopulation through the ˇ°merging effects (MEs),ˇ± which indicates that the travellers will be regarded as either connected components or isolated nodes in the contact network. The results show that the presence of the MEs has constantly accelerated the transmission of the diseases and aggravated the outbreaks compared to the scenario in which the diversity of the long-distance travel types is arbitrarily discarded. Sensitivity analyses show that these results are relatively constant regarding a wide range variation of several model parameters. Our study has highlighted several important causes which could significantly affect the spatiotemporal disease dynamics neglected by the present studies. 1. Introduction Modelling the spatial disease dynamics has long been a research priority because of its potential in facilitating the development of mitigation strategies responding to the increasing threat of an influenza pandemic [1, 2]. The large-scale spatiotemporal spreading of diseases transmitted person to person via droplets or direct contact in real population is ultimately denominated by the population interaction which could be described by a social contact network and by interplay with the virus itself [3, 4]. Networks have provided a unified way to think about the daily interaction between individuals, are especially helpful when each individual is in direct contact with only a small proportion of the population [5¨C9], and thus have been powerful tools for understanding the transmission of infection in the population due to either social contact or sexual contact. Several types of classical networks have been frequently used in epidemiological literatures, which are random networks [10], lattices [11], small world networks [12], spatial networks [13] and scale-free networks [14]. Recently, researchers have incorporated demogeographic information into the contact network and integrated it with metapopulation model based on human mobility data [15¨C21] to pursue a better understanding of the role of real population
%U http://www.hindawi.com/journals/cmmm/2014/295028/