Abstract
Abstract. The movement of individuals between specific locations and the different group contacts of people is essential to predict the future movement and interaction pattern of infectious diseases. Previous studies have shown major factor of infectious disease spread comes from human mobility because a complex and dynamic network of spatial interactions between locations such as the mobility formed by the daily activity of people from place to place. To better understand the such human mobility behaviour, innovative methods are required to depict and analyse their structures by using social network analysis (SNA). This paper aims to investigate the social network structure of selected tuberculosis (TB) case in Klang, Selangor as actors (nodes), and then human mobility (home-work place) data as edge generally used to investigate social network mobility structures and analyse relation among the nodes and study their edges in term of their network centrality. The main finding has revealed that the higher the centrality (degree and betweenness) of a node in the network structure, the higher the chance the node influencing the TB spread in the whole network, after comparing the network graph result with the geographic information system (GIS) mapping approach. Most of the result share the similar result where most of high infection of TB are located in urban and crowded areas. The SNA is a practical knowledge of the social system and contact structure of a community that can therefore provide crucial information to predict outbreaks of infectious diseases in a dynamic spatial phenomena.
Highlights
The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals
We ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts
By comparing degree and betweenness centrality by using social network analysis (SNA) method with geographic information system (GIS), most localities proves the hypothesis that the higher the degree and betweenness centrality of a node in the network structure, the higher the chance the node influencing the TB spread in the whole network
Summary
1.1 BackgroundThe epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. We ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. According to an individual-based model of airborne pathogen transmission parametrized with mobility data, systematic variation in mobility patterns is sufficient to trigger significant differences in infectious disease dynamics among cities, even among cities of similar size (Dalziel, Pourbohloul, & Ellner, 2013). Human mobility patterns generate the proximity between individuals prerequisite for the transmission of many infectious diseases This suggests that cities with different mobility patterns may differ in the rate at which their inhabitants have infectious contact, leading to variation among cities in the risk of an epidemic (Merler & Ajelli, 2010)
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