Spatio-Temporal Modelling of Frequent Human Mobility Pattern to Analyse the Dynamics of Epidemic Disease

Abstract

Abstract:Spatial data mining is a rapidly growing field for analysing the data related to space and time. Nowadays most of the applications are based on these factors, so numerous data mining algorithms are developed for spatial characterization and to analyse the spatial trends. The spatial trend analysis determines the change in pattern of some non-spatial attributes on neighbourhood objects. In this paper, we identify spatio-temporal mobility pattern on the dynamics of Epidemic disease (H1N1) that plays a significant role in analysing the outbreak of an infectious disease. Modelling the transmission among the human population with respect to time and space leads to improved understanding of transmission mechanisms. A compartmental model is designed to characterize the disease dynamics of a random variable extracted from binomial and multinomial distribution. ArcGIS tool is used to visualize the mobility distribution of the infected host spatially and yields an output of frequent mobility locations with respect to different time slices. The results thus obtained would help the district administrative authorities to take strategic decisions and prevent the spread of the disease.

Can the Pandemic Be a Catalyst of Spatial Changes Leading Towards the Smart City?

“…Relatively recent pandemics (e.g., smallpox, SARS, swine flu) led to the development of mathematical solutions connected with the mobility of people and social networks, allowing the control of infectious diseases. Those analyses contributed to a better understanding of the dynamics, transmission mechanisms, and spatial correlations of diseases that can help administrative authorities in crisis management (Eubank et al, 2004;Keeling, 1999;Parimala & Lopez, 2016).…”