Uncovering Spatial Invasion on Metapopulation Networks with SIR Epidemics

Understanding how infectious diseases spatially diffuse is critical to predict and control the epidemic prevalence. However, uncovering epidemic spatial invasion is challenging due to stochastic travel of hosts and insufficient data availability. In this study, we develop a methodology for inferring...

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Bibliographic Details
Published in:IEEE transactions on network science and engineering Vol. 6; no. 4; pp. 788 - 800
Main Authors: Wang, Jian-Bo, Li, Xiang
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computational modeling
Computer simulation
Disease control
Epidemic maximum diffusion
Epidemics
Graph theory
Heuristic algorithms
Infectious diseases
Inference algorithm
Inference algorithms
Local optimization
Metapopulation network
Networks
SIR model
Sociology
Spatial invasion pathways
Statistics
Surveillance
ISSN:2327-4697, 2334-329X
Online Access:Get full text
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Summary:Understanding how infectious diseases spatially diffuse is critical to predict and control the epidemic prevalence. However, uncovering epidemic spatial invasion is challenging due to stochastic travel of hosts and insufficient data availability. In this study, we develop a methodology for inferring global invasion pathways on metapopulation networks with the susceptible-infected-recovered (SIR) epidemics. To solve the inference problem, we infer the invasion pathways of each invasion case as a subgraph containing a part of global invasion pathways at each epidemic arrival time. We first develop a reduction approach to decrease the sizes of the dominant invasion cases, and propose a local optimization method aiming at the SIR epidemics based on epidemic maximum diffusion (EMD) to infer spatial invasion pathways of the reduced invasion cases, and reconstruct the global invasion pathways. Compared with the previous work, we reduce the computational complexity of invasion cases, and improve the calculation of epidemic diffusion likelihood and effectiveness of the algorithm for epidemic recovery. Simulations on real and synthetic metapopulation networks verify the validity of our algorithm. Finally, an empirical example of the 2009 A (H1N1) in the USA is presented to uncover the spatial invasion pathways and identify the superinvaders.
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ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2018.2873609