An Efficient Geometric Search Algorithm of Pandemic Boundary Detection

Round 1

Reviewer 1 Report

The authors presented an algorithm which can trace the boundary of an infection significantly more efficiently than full-scale contact tracing, even in the presence of some non-cooperative individuals that are unwilling or unable to be tested, with a possibility of researching about “targeted contact tracing".

• Efficiency of the Geometric Search Algorithm needs to be established with other searching algorithms and on other datasets
• Authors need to critically analyse how their approach is novel and different from standard graph-based algorithms used widely in contact tracing applications
• Experimental dataset need more validity and reliability for the results to become comparable
• Scientific comparisons from the literature has not been studied and benchmarking algorithms for boundary search has not been explored in detail.
• Though authors have stated limitations and assumptions to their approach, but it is significant assumption when we consider practical implications of this approach in contact tracing applications, which makes it a limited algorithm in terms of usability
• Improve the references used in this domain to give a sound context to the mathematical problem

Author Response

The authors presented an algorithm which can trace the boundary of an infection significantly more efficiently than full-scale contact tracing, even in the presence of some non-cooperative individuals that are unwilling or unable to be tested, with a possibility of researching about “targeted contact tracing".

Efficiency of the Geometric Search Algorithm needs to be established with other searching algorithms and on other datasets

Experimental dataset need more validity and reliability for the results to become comparable

Scientific comparisons from the literature has not been studied and benchmarking algorithms for boundary search has not been explored in detail.

Response: The concept of pandemic transmission boundary we have brought up is pretty new. Currently, most existing works are focusing on the use of digital contact tracing on a massive scale of population. While they leverage the modern computing power to identify the potentially infected people very well, optimizing the use of test-kit is rarely their goal. Therefore, they would have to test all infected individuals plus many uninfected ones who have been in close contact in order to figure out the pandemic transmission status. On the other hand, our algorithm is able to figure out the transmission status quo by only testing the infected and non-infected individuals on the transmission boundary, while leaving all other people inside of the boundary un-tested. In this way, we are able to save the expenses and time on producing the disease test kits.

Authors need to critically analyse how their approach is novel and different from standard graph-based algorithms used widely in contact tracing applications

Response: The most significant difference between our approach and the standard graph-based algorithms reside in the scope of searching. Standard graph-based algorithms, like BFS or DFS, require searching full scope of the entire population before some stopping criteria is met. On the contrary, our method prioritizes the individuals on the outermost boundary which successfully gets rid of many unnecessary searches.

Though authors have stated limitations and assumptions to their approach, but it is significant assumption when we consider practical implications of this approach in contact tracing applications, which makes it a limited algorithm in terms of usability

Response: Although the assumptions in our paper might not be suitable for air-borne diseases like Covid-19, the model we adopted is still applicable to many slow spreading diseases (such as Ebola) and it is also applicable to the detection of invasion species.

Improve the references used in this domain to give a sound context to the mathematical problem

Response: We derived an asymptotic bound for our algorithm based on some additional referencing research that focuses on diameters of random graphs.

Reviewer 2 Report

In my opinion, the paper should be rejected since it is not deeply worked from either a mathematical or a computational point of view. It does not provide some relevant advance(s)  on the previous related background. The material is more appropriate for presentation at a conference than for its publication in a refereed journal.

Author Response

In my opinion, the paper should be rejected since it is not deeply worked from either a mathematical or a computational point of view. It does not provide some relevant advance(s)  on the previous related background. The material is more appropriate for presentation at a conference than for its publication in a refereed journal.

Response: We have improved our paper by deriving an asymptotic bound on the efficiency of our algorithm.

Round 2

Reviewer 1 Report

The response is satisfactory.

Reviewer 2 Report

The paper has been improved related the previous version by incorporating a study of the asympttoic efficiency of the proposed scheme.