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Hi All,

https://urldefense.proofpoint.com/v2/url?u=https-3A__www.nature.com_articles_s41598-2D019-2D41719-2D8&d=DwIDaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=dwP3sWlWCNfFvqKdtfcReMlNpLGD8h1a19enp-UQTSQ&s=394UbaYLNIYpyjitzB_dmCeU0K_RLUwLagzU0S_0VNE&e= 

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.


All best,


Chung-Yuan Huang

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