Network Analysis to Identify the Risk of Epidemic Spreading
, Sunyong Yoo 3,*,†, Sangyeon Lee 1,4, Doheon Lee 1,4,* and Kwang-Hyung Lee 1,5,*
Round 1
Reviewer 1 Report
The authors have provided a new perspective on the risk of pandemic to the global population by combining the use of network analysis, a mathematical technique that is becoming increasingly popular in medical, biomedical and epidemiological research, with the classic SIR (susceptible-infectious-recovered) model of understanding the dynamics of pandemics to simulate and evaluate the effect of social connectivity (network edges/links) on the outcome of diseases of global consequence. The scale-free network has been shown to better simulate the true interaction of natural units, be it social, economic, informational, or biological and provides a realistic representation for understanding how diseases of global magnitude could spread.
This combination may indeed be another weapon in the arsenal of public health practitioners in understanding the possibility, risk and dynamics of future pandemic while providing a relatively more accurate model for decision making regarding prevention and management. Overall, this is a well-presented study. However, there is room for improvement.
Recommended Adjustments
- Figure 6. Provides the simulated case scenarios of various diseases in a varied network degree (i.e. k = 2, 5,7 and 10): The color for the legends should be manipulated to improve clarity. I suggest using colours that are less like the blue background.
- Could the author provide a more detailed reason for choosing pneumonia, cholera, marbug, and Ebola as a model?
- In the context of global public health threat, it is reasonable to assume that various other diseases such as SARS (2002), MERS or COVID-19 or at least one of these will be featured in this study as this will improve the relevance and practical applicability of this study. Indeed, these diseases have been intensely studied and data exist on the varios parameters needed for the SIR and network approach.
- The discussion should be expanded to include various similar studies including those using just SIR techniques for the various diseases presented in figure 6. This will allow readers to understand the context and make comparison.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The authors observed that previous methods did not perform a quantitative assessment of the pandemic risk depending on the network connectivity.
They applied a SIR epidemic model to a scale-free network with Monte-Carlo simulation to identify the quantitative relationship between infectious diseases and human existence and they found that the mean degree of a scale-free network was an essential factor in determining whether epidemics threaten humans.
The work presents some limitations as the authors themselves recognized. To achieve robust results, they need to perform additional experiments for various parameters. They considered only some diseases with well-known reproduction number.
But I think that the results are relevant because, even if it is obvious that the number of contacts increases spread infection, the authors established a procedure to identify a threshold and this can be useful for policymakers.
Minor revisions
It is not clear where the caption of Figure six ends.
In the limitations, the authors can add that they consider only one model of Scale-free networks and it would be important in the future to test the sensitivity of the results using other models.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
This paper shows a severe lack of understanding of basic epidemiology, and of the published literature on epidemic spread. It makes no useful contribution to work on modelling and analysis of epidemic and pandemic diseases.
Author Response
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Author Response File: Author Response.pdf
