EpiRank: Modeling Bidirectional Disease Spread in Asymmetric Commuting Networks View Full Text


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Article Info

DATE

2019-04-01

AUTHORS

Chung-Yuan Huang, Wei-Chien-Benny Chin, Tzai-Hung Wen, Yu-Hsiang Fu, Yu-Shiuan Tsai

ABSTRACT

Commuting network flows are generally asymmetrical, with commuting behaviors bi-directionally balanced between home and work locations, and with weekday commutes providing many opportunities for the spread of infectious diseases via direct and indirect physical contact. The authors use a Markov chain model and PageRank-like algorithm to construct a novel algorithm called EpiRank to measure infection risk in a spatially confined commuting network on Taiwan island. Data from the country's 2000 census were used to map epidemic risk distribution as a commuting network function. A daytime parameter was used to integrate forward and backward movement in order to analyze daily commuting patterns. EpiRank algorithm results were tested by comparing calculations with actual disease distributions for the 2009 H1N1 influenza outbreak and enterovirus cases between 2000 and 2008. Results suggest that the bidirectional movement model outperformed models that considered forward or backward direction only in terms of capturing spatial epidemic risk distribution. EpiRank also outperformed models based on network indexes such as PageRank and HITS. According to a sensitivity analysis of the daytime parameter, the backward movement effect is more important than the forward movement effect for understanding a commuting network's disease diffusion structure. Our evidence supports the use of EpiRank as an alternative network measure for analyzing disease diffusion in a commuting network. More... »

PAGES

5415

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  • Identifiers

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    37 schema:description Commuting network flows are generally asymmetrical, with commuting behaviors bi-directionally balanced between home and work locations, and with weekday commutes providing many opportunities for the spread of infectious diseases via direct and indirect physical contact. The authors use a Markov chain model and PageRank-like algorithm to construct a novel algorithm called EpiRank to measure infection risk in a spatially confined commuting network on Taiwan island. Data from the country's 2000 census were used to map epidemic risk distribution as a commuting network function. A daytime parameter was used to integrate forward and backward movement in order to analyze daily commuting patterns. EpiRank algorithm results were tested by comparing calculations with actual disease distributions for the 2009 H1N1 influenza outbreak and enterovirus cases between 2000 and 2008. Results suggest that the bidirectional movement model outperformed models that considered forward or backward direction only in terms of capturing spatial epidemic risk distribution. EpiRank also outperformed models based on network indexes such as PageRank and HITS. According to a sensitivity analysis of the daytime parameter, the backward movement effect is more important than the forward movement effect for understanding a commuting network's disease diffusion structure. Our evidence supports the use of EpiRank as an alternative network measure for analyzing disease diffusion in a commuting network.
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    45 Asymmetric Commuting Networks
    46 Bidirectional Disease
    47 Commuting Networks
    48 EpiRank
    49 EpiRank algorithm results
    50 H1N1 influenza outbreak
    51 HITS
    52 Markov chain model
    53 Modeling Bidirectional Disease
    54 PageRank
    55 PageRank-like algorithm
    56 Taiwan Island
    57 actual disease distributions
    58 algorithm
    59 algorithm results
    60 alternative network measure
    61 analysis
    62 authors
    63 backward direction
    64 backward movement
    65 backward movement effect
    66 behavior
    67 bidirectional movement model
    68 calculations
    69 cases
    70 census
    71 chain model
    72 commute
    73 contact
    74 country's 2000 census
    75 data
    76 daytime parameters
    77 diffusion
    78 diffusion structure
    79 direction
    80 disease
    81 disease diffusion
    82 disease distribution
    83 distribution
    84 effect
    85 enterovirus cases
    86 epidemic risk distribution
    87 evidence
    88 flow
    89 forward movement effect
    90 function
    91 home
    92 index
    93 indirect physical contact
    94 infection risk
    95 infectious diseases
    96 influenza outbreaks
    97 islands
    98 location
    99 measures
    100 model
    101 movement
    102 movement effects
    103 movement model
    104 network
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    106 network functions
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    111 opportunities
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