Paper Info
Title
Modeling and Mining Spatiotemporal Social Contact of Metapopulation from Heterogeneous Data
Abstract
During an epidemic, the spatial, temporal and demographical patterns of disease transmission are determined by multiple factors. Besides the physiological properties of pathogenes and hosts, the social contacts of host population, which characterize individuals' reciprocal exposures of infection in view of demographical structures and various social activities, are also pivotal to understand and further predict the prevalence of infectious diseases. The means of measuring social contacts will dominate the extent how precisely we can forecast the dynamics of infections in the real world. Most current works focus their efforts on modeling the spatial patterns of static social contacts. In this work, we address the problem on how to characterize and measure dynamical social contacts during an epidemic from a novel perspective. We propose an epidemic-model-based tensor deconvolution framework to address this issue, in which the spatiotemporal patterns of social contacts are represented by the factors of tensors, which can be discovered by a tensor deconvolution procedure with an integration of epidemic models from rich types of data, mainly including heterogeneous outbreak surveillance, social-demographic census and physiological data from medical reports. Taking SIR model as a case study, the efficacy of the proposed method is theoretically analyzed and empirically validated through a set of rigorous experiments on both synthetic and real-world data.
Year
DOI
Venue
2014
10.1109/ICDM.2014.11
ICDM
Keywords
Field
DocType
data models,tensile stress,sociology,statistics
Data science,Data modeling,Data mining,Population,Metapopulation,Epidemic model,Computer science,Data type,Spatial ecology
Conference
ISSN
Citations 
PageRank 
1550-4786
0
0.34
References 
Authors
5
5
Name
Order
Citations
PageRank
Bo Yang182264.08
Hongbin Pei2164.25
Hechang Chen3189.53
Jiming Liu43241312.47
Shang Xia511.37