The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B4-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 55–61, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-55-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 55–61, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-55-2021

  30 Jun 2021

30 Jun 2021

SOCIAL NETWORK ANALYSIS OF SPATIAL HUMAN MOBILITY BEHAVIOUR IN INFECTIOUS DISEASE INTERACTION: AN EXPLORATORY EVIDENCE OF TUBERCULOSIS IN MALAYSIA

I. Abdul Jalil and A. R. Abdul Rasam I. Abdul Jalil and A. R. Abdul Rasam
  • Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture, Planning and Surveying, University Teknologi MARA, Malaysia

Keywords: Social Network Analysis, Geospatial, Human Mobility, Infectious Disease, Tuberculosis

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.