ArticleIEEE transactions on cybernetics2016
Identifying Spatial Invasion of Pandemics on Metapopulation Networks Via Anatomizing Arrival History.
Article in IEEE transactions on cybernetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part II: Emerging Technologies and Open Issues.IEEE access : practical innovations, open solutions · 2020Article
- Network reconstruction from infection cascades.Journal of the Royal Society, Interface · 2019Article
- Article
- Optimizing HIV Interventions for Multiplex Social Networks via Partition-Based Random Search.IEEE transactions on cybernetics · 2018Article
- Exploring Voluntary Vaccinating Behaviors using Evolutionary N-person Threshold Games.Scientific reports · 2017Article
- Voluntary Vaccination through Self-organizing Behaviors on Locally-mixed Social Networks.Scientific reports · 2017Article
- Pattern transitions in spatial epidemics: Mechanisms and emergent properties.Physics of life reviews · 2016Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: 1) anatomizing the whole spread process among all subpopulations into disjoint componential patches; 2) inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; and 3) recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.
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Registered trials
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