Locating Infectious Sources Using Bluetooth System of Smart Devices
Round 1
Reviewer 1 Report
This paper reports an approach for locating the source of infection based on big data. The presented approach/architecture wants to combine Bluetooth and GPS positioning to track the action trajectory of each individual.
Paper has several typos. Authors must make a rigorous proofreading. Figures must also be improved, text out of the limits of boxes. EX: "._By turning", "phone The in", etc.
In the abstract is stated that " ..We can also generate reports..", But there is no evidences of what reports.
In the keywords is "smart phones" but maybe the keyword is smartphones.
A deeper state of the art must be present in paper to demonstrate the novelty of its contribution. For example, making a simple search in google I found this one DOI : 10.17577/IJERTCONV9IS04010 that also used GPS and Bluetooth. It would improve the paper if the authors present a deeper review of the state of the art to make clear the advantages of the system presented in this paper.
In 4.1 is stated that "We have developed a smartphone application to demonstrate our proposed system". But no app is presented in the paper, neither results of its use.
No results are presented in the paper, about the use and performance of the new system or about the data processing (BigData). This seams to me mandatory to demonstrate the usefulness or novelty of the proposed system.
Conclusions are too much scarce and vague. For a journal a good conclusions is mandatory. Must be clear the novelty and achievements and results of the described contribution.
Can be improved to remove some typos.
Author Response
Reviewer#1, Concern # 1: Paper has several typos. Authors must make a rigorous proofreading. Figures must also be improved, text out of the limits of boxes. EX: "._By turning", "phone The in", etc. In the keywords is "smart phones" but maybe the keyword is smartphones.
Author response: Thank you very much for your valuable comments. We have read through the entire paper and corrected errors. Thank you so much for your careful check.
Author action:
Keywords: Bluetooth; Bluetooth low energy; COVID-19; Contact tracing; Weight-based matching method; smartphones
Reviewer#1, Concern # 2: In the abstract is stated that " ..We can also generate reports..", But there is no evidences of what reports.
Author response: Thank you very much for your valuable comments. The Bluetooth interaction in the crowd is completed when the contact person's characteristic information is also stored locally, and when the target person is diagnosed it is uploaded to the server, where the contact person report is generated after comparison. Because the information about people and devices is maintained on the server side, the risk level is also managed in a unified manner, and an epidemic status report can also be generated, such as how many new high-risk areas are currently being added and how many areas are changing to low-risk areas.
Author action: The abstract of this article has been modified as follows:
Abstract: Infectious diseases, such as COVID-19, have a significant impact on human society. One effective method to prevent and control the spread of such diseases is to quickly locate the source of infection and reduce the population's exposure to the virus. This paper presents a method, system, and storage medium for locating the source of infection based on Bluetooth technology. We combine Bluetooth and GPS positioning to track the movement trajectory of each individual, store the close contact and location information locally, and update it on the cloud platform for an appropriate period. The method and system can provide personal and regional risk levels through related algorithms and provide an alarm function when entering a high-risk area or if the personal risk level is greater than the set threshold within the Bluetooth interconnection range. The system can quickly locate the source of infection and close contacts, providing a reference for quick and highly efficient epidemic prevention and control.
Reviewer#1, Concern # 3: A deeper state of the art must be present in paper to demonstrate the novelty of its contribution. For example, making a simple search in google I found this one DOI : 10.17577/IJERTCONV9IS04010 that also used GPS and Bluetooth. It would improve the paper if the authors present a deeper review of the state of the art to make clear the advantages of the system presented in this paper.
Author response: We appreciate for your valuable comment. According to your comments, we have conducted an in-depth research on the current status of this field. The Introduction section was rewritten to review the different software positioning methods used in several countries and also to give examples of several types of research work done by several researchers on Bluetooth systems for digital tracking of epidemics. And the introduction shows that the advantage of our proposed approach is the dynamism and timeliness of risk level updates.
Author action: The new part of introduction is as follow:
In the information age, digital tracking technology and Geo-Information system have played a very important role in the fight against COVID-19[10-12], providing a reference for solving travel restrictions. The analysis and mining of big data can help us control and track the development of the epidemic, and play a role in prediction and prevention. Close contact tracking applications have been adopted in many countries[13,14]. Different countries utilize different technologies such as Bluetooth, Global Positioning System (GPS), Global System for Mobile Communication (GSM) technology, mobile tracking technology, and card transaction data.
For example, Health Code (China) and Corona100m (Korea) are applications that identify and store the location of users through GPS or GSM tracking. Location data is usually collected centrally by the government and contact tracking programs are mandatory in both countries. The movement of the holder is restricted by the color of the Health Code[15,16], which achieves the effect of isolation to some degree. However, it cannot be communicated between different provinces.
In addition to GPS positioning, many contact tracking applications use Bluetooth to estimate the distance between smartphones to protect privacy. For example, COVIDSafe in Australia and TraceTogether in Singapore[17]. Australia's COVIDSafe uses Bluetooth to record the information of people who stayed for a set time within a set distance, and it will be automatically cleared after 21 days[18].This reduces the time to find and locate close contacts to a certain extent. However, there are strict restrictions on the duration of stay.
Besides, Apple and Google jointly developed an API for exposure notification, which can be used across borders. It is mainly based on the DP3T protocol[19]. The DP3T protocol is a decentralized protocol. Exposure notification APIs have been applied by applications in many countries, such as COVID Tracker in Ireland and My Trace in Malaysia[13]. The use and implementation largely depend on the corresponding public health department, and the authorities need to follow development standards such as privacy and security.
In addition to the national application development, many research institutions and scholars have also done a lot of related research. There are several types of related researches nowadays on the use of Bluetooth for digital tracking.
First, many studies have used RSSI-based distance estimation. In [20] the authors improved the accuracy of distance estimation based on RSSI. The authors of [21] maintain social distancing by RSSI.
Second, some studies have optimized the framework design for privacy security. The authors of [22] outline three common tracking application architectures and discusses privacy security issues. And In [23] the authors developed a privacy-preserving mobile and fog computing framework to track infections and suspected cases through fog nodes without revealing the identity of the infected person..
Third, some studies have assessed the risk level. In [24] the authors use the combination of Bluetooth and GPS. The article provides a self-assessment of the severity of the new coronavirus. The authors of [25] uses different features to calculate the matching scores of two users to complete contact tracking. But the above level assessment method given in those papers does not have real-time dynamics.
So the problem is that existing technology can not accurately locate the source of infection and the epidemic prevention and control measures are inadequate. Taking all these into account, this article provides a method and system for locating the source of infection based on Bluetooth to solve the predicament.
This system provides precise location of the source of infection based on GPS and Bluetooth. The system provides real-time recording and close contact tracking. And the system provides a real-time display of multi-user risk levels and location information of the source of infection. The technical background will be introduced in the second section. The third section will introduce the related algorithms adopted by this method. The fourth section will briefly introduce the system architecture and functional design. The advantage of the method proposed in this paper lies in the dynamic and real-time update of the risk level, which dynamically updates the risk level of people at regular intervals during the process of crowd contact. And warnings are issued as the risk level changes to help users isolate and self-isolate.The system does not rely strongly on infectious disease monitoring. When there is no infectious disease monitoring, the method can still calculate and update the regional risk level of each location and the risk level of each person at each time based on the big data of the flow of people. The method of locating the source of infection can be applied to suppress the transmission of infectious diseases and thus control or prevent epidemics. The method can also be used to intelligently generate epidemic investigation reports to guide epidemic prevention and control.
Reviewer#1, Concern # 4: In 4.1 is stated that "We have developed a smartphone application to demonstrate our proposed system". But no app is presented in the paper, neither results of its use.
No results are presented in the paper, about the use and performance of the new system or about the data processing (BigData). This seams to me mandatory to demonstrate the usefulness or novelty of the proposed system.
Author response: Big data processing is a technical issue in development, and practical applications will be influenced by policies. This paper proposes points that can be optimized in future applications, and the system mentioned in the article is for small-scale testing. The innovation and advantage of this system lies in the real-time update of risk levels.
Reviewer#1, Concern # 5: Conclusions are too much scarce and vague. For a journal a good conclusions is mandatory. Must be clear the novelty and achievements and results of the described contribution.
Author response: Thank you very much for your valuable comments. We have rewritten the conclusion section to summarize the main work of this paper, introduce the application scenarios and implications of this work.
Author action: The new version of conclusion is as follow:
The purpose of this paper is to propose a method for infectious source localization and tracing to solve the problem of insufficient epidemic prevention and control methods and provide a reference for epidemic prevention and control. It can be applied to regular and highly efficient epidemic prevention and control to help people isolate and self-isolate through risk level assessment. The system has broad application prospects and practical significance.
This paper presents a method for locating the source of infection based on a Bluetooth system for smart devices, which combines Bluetooth and GPS positioning. The system uses GPS to record the user's activity trajectory. The distance between users is estimated by the Bluetooth RSSI, which enables the exchange of Bluetooth packets from smart devices within a set distance and early warning. The risk level real-time update algorithm is also proposed. The advantage of the method proposed in this paper lies in the dynamic and timely update of the risk level. With the change of risk level, the system sends warnings, which helps in early user isolation and self-isolation.
An infectious source tracking system was designed based on the proposed algorithm, and the implementation of the main functions was completed. However, the functionality of the system is still poor and is a direction that can be continued in subsequent research.
Reviewer 2 Report
The paper describes in past years the hot topic of locating the source of the infection based on using BLE technology. The topic is relevant even today, but the general public interest is now vanishing. The paper structure is appropriate but I am missing:
a figure with the general concept of the system
some results of the system in the operation or at least the simulation of the system in operation and
the conclusion is poor, I would expect the more comprehensive conclusion
The description of processes is too general, basically copy-paste from the known literature, for example, the Kalman filter with equations (2) and (3) and how data is encoded in BLE message.
I have hardly got the concept of the risk level algorithm. The expression such as at the previous time at the first current time, etc. are completely confusing. A figure with the relation of time scale would be beneficial. Furthermore, the naming of time instances must be reconsidered.
There are several mistakes in the flow charts presented in Figures 4 and 5. For example, why put the question "Is it frequent in the user list within 14 days?" and the answer is yes or no, the Gama is set to zero? There are several such errors. Thus I hardly believe the concept is tested in real or at least in simulations.
There exist several similar studies using BLE which are not mentioned in the paper.
In general, English is readable. There are some minor mistakes in spelling. Some information is repeated in successive paragraphs, which leads to a loss of the readers' concentration.
Author Response
Reviewer#2, Concern # 1: The paper structure is appropriate but I am missing a figure with the general concept of the system
Author response: Thank you very much for your valuable comments. the diagram of the general concept of the system is shown in Figure 1. The system contains three subjects, the smartphone, the server and the communication network. The software installed on the smartphone exchanges personal risk levels in the Bluetooth risk circle through the Bluetooth system, and updates personal risk levels locally on the phone. And at the server side the regional risk level is managed uniformly. And the devices, user information, and other information are managed uniformly. Data communication and data synchronization between the smartphone and the server is carried out through the mobile network.
Author action: The general concept of the system is as follow:
Figure 1. A schematic diagram of system data collection and transmission.
Reviewer#2, Concern # 2: Some results of the system in the operation or at least the simulation of the system in operation and
Author response: Same as the fourth question from Reviewer 1. Big data processing is a technical issue in development, and practical applications will be influenced by policies. This paper proposes points that can be optimized in future applications, and the system mentioned in the article is for small-scale testing. The innovation and advantage of this system lies in the real-time update of risk levels.
Reviewer#2, Concern # 3:The conclusion is poor, I would expect the more comprehensive conclusion
Author response: Thank you very much for your valuable comments. We have rewritten the conclusion section to summarize the main work of this paper and introduce the application scenarios and implications of this work.
Author action: The new version of conclusion is as follow:
The purpose of this paper is to propose a method for infectious source localization and tracing to solve the problem of insufficient epidemic prevention and control methods and provide a reference for epidemic prevention and control. It can be applied to regular and highly efficient epidemic prevention and control to help people isolate and self-isolate through risk level assessment. The system has broad application prospects and practical significance.
This paper presents a method for locating the source of infection based on a Bluetooth system for smart devices, which combines Bluetooth and GPS positioning. The system uses GPS to record the user's activity trajectory. The distance between users is estimated by the Bluetooth RSSI, which enables the exchange of Bluetooth packets from smart devices within a set distance and early warning. The risk level real-time update algorithm is also proposed. The advantage of the method proposed in this paper lies in the dynamic and timely update of the risk level. With the change of risk level, the system sends warnings, which helps in early user isolation and self-isolation.
An infectious source tracking system was designed based on the proposed algorithm, and the implementation of the main functions was completed. However, the functionality of the system is still poor and is a direction that can be continued in subsequent research.
Reviewer#2, Concern # 3:The description of processes is too general, basically copy-paste from the known literature, for example, the Kalman filter with equations (2) and (3) and how data is encoded in BLE message.
Author response: Thank you very much for your valuable comments. Additional textual explanations about the application of Kalman filtering have been provided. A corresponding schematic has been added for the encoding of Bluetooth packets.
Author action: The new description is as follow:
The Kalman filter model includes the following equations.
Kalman filter time update equations are
|
, . |
(2) |
Measurement update equations are
|
, , . |
(3) |
in equation (2) is the a prior state estimate and is the covariance between the true and predicted values. in equation (3) is the Kalman gain. is the optimal estimate and is the covariance between the true value and the optimal estimate.
As shown in equation(4), the Kalman gain actually characterizes the proportion of model predicted error to measurement error in the process of state-optimal estimation.
|
Kalman Gain=Predicted Error/(Predicted Error+Measurement Error) |
(4) |
As shown in Figure 4, we extract the information of Bluetooth scan packets as RSSI array. The Kalman gain is calculated from equation (4). Then and are calculated from equations (3). The corrected RSSI is . The time update equations are updated according to the values of the measurement update equations. And this goes to the next correction loop.
Figure 4. RSSI correction flow chart.
...and each AD data segment must consist of the length and data. Broadcast packets contain information including personal risk levels and other necessary discriminatory information. It is sent as AD data after encryption.
Figure 3. Advertising packet encryption.
Figure 3 shows an example, the personal risk level and protection measures are translated into a stream of bytes, which are encrypted and packaged into advertising packet. And then, it spreads in the crowd.
Reviewer#2, Concern # 4:I have hardly got the concept of the risk level algorithm. The expression such as at the previous time at the first current time, etc. are completely confusing. A figure with the relation of time scale would be beneficial. Furthermore, the naming of time instances must be reconsidered.
Author response: Thank you very much for your valuable comments. We have changed the naming of the time in such a way that the previous moment and the current moment are easy to understand.
Author action: The time description is shown in the article as follows.
Taking into account the transmission characteristics of COVID-19[35], the risk level of each target person at each moment is related to the following factors:
- The personal risk level at the previous time.
- The personal risk level correction parameter that determined based on the detection information at the current moment.
- The infection transmission probability at the previous time.
- The regional risk level of the previous moment.
- The infection probability of the environment which the target person is exposed to.
Wherein, the current moment and the previous moment are separated by a period. The initial personal risk level of each target may be preset according to the initial information which includes the initial position and initial protective measures. Supposed that the current moment is recorded as t, the period is recorded as , and the previous moment is recorded as . And then the calculation and update methods of the personal risk level of each target person at each moment may be given by
...
The regional risk level of each target location at each moment is related to the following factors:
- The regional risk level of the target location at the previous moment.
- The disinfection coefficient at the previous time .
- The personal risk level at the previous moment.
- The transmission probability of the infected person to the environment and the infection source dissipation coefficient.
Wherein, the current moment and the previous moment are separated by a period, and the initial regional risk level of each target location is preset;
Reviewer#2, Concern # 5:There are several mistakes in the flow charts presented in Figures 4 and 5. For example, why put the question "Is it frequent in the user list within 14 days?" and the answer is yes or no, the Gama is set to zero? There are several such errors. Thus I hardly believe the concept is tested in real or at least in simulations.
Author response: Thank you very much for your valuable comments. We apologize for not showing the initial diagram clearly. Here is to remove the influence of people who are frequently exposed in life in the calculation of risk level. When the contact person is a frequent contact person within 14 days, it is no longer overlapping calculation, and double calculation is avoided by setting gamma to 0.
Author action: The modified diagram is as follow:
Figure 5. Personal risk level update flow chart.
The people in the risk circle have an effect on the risk level of the target person. People with different risk levels set different infection transmission probabilities according to the relative position and protective measures of the people. The longer the distance, the lower the probability of propagation. So the risk level of people within the set range is added according to the weight and has an impact on the risk level of the target person at the current moment. In addition, it need to calls the database to check whether people in the current list are frequent contact before adding. And if it is frequent contact in 14 days, the impact will not be superimposed.
Figure 6. Regional risk level update flow chart.
Through the above formula, the personal risk level can be obtained iteratively. The iterative process is shown in Figure 6.
The regional risk level is related to the disinfection measures and the person staying in the environment. If the disinfection is better, the regional risk level is lower, and the area is safer. If there is a long-term gathering of infected persons in this area, the longer the time, the higher the regional risk level will be under the influence of the second term of the equation(6). If a person in the range is not an infected person, the infection transmission rate gamma is 0, which does not affect the risk level of the region. The second term on the right side of the equation(6) calls the database to check whether people in the current list are infected within 14 days before adding. And if it is no, the impact will not be superimposed.
Reviewer#2, Concern # 6:There exist several similar studies using BLE which are not mentioned in the paper.
Author response: Thank you very much for your valuable comments. We have conducted an in-depth research on the current status of this field. The Introduction section was rewritten to review the different software positioning methods used in several countries and also to give examples of several types of research work done by several researchers on Bluetooth systems for digital tracking of epidemics. And the introduction shows that the advantage of our proposed approach is the dynamism and timeliness of risk level updates.
Author action: The new introduction is as follow:
In the information age, digital tracking technology and Geo-Information system have played a very important role in the fight against COVID-19[10-12], providing a reference for solving travel restrictions. The analysis and mining of big data can help us control and track the development of the epidemic, and play a role in prediction and prevention. Close contact tracking applications have been adopted in many countries[13,14]. Different countries utilize different technologies such as Bluetooth, Global Positioning System (GPS), Global System for Mobile Communication (GSM) technology, mobile tracking technology, and card transaction data.
For example, Health Code (China) and Corona100m (Korea) are applications that identify and store the location of users through GPS or GSM tracking. Location data is usually collected centrally by the government and contact tracking programs are mandatory in both countries. The movement of the holder is restricted by the color of the Health Code[15,16], which achieves the effect of isolation to some degree. However, it cannot be communicated between different provinces.
In addition to GPS positioning, many contact tracking applications use Bluetooth to estimate the distance between smartphones to protect privacy. For example, COVIDSafe in Australia and TraceTogether in Singapore[17]. Australia's COVIDSafe uses Bluetooth to record the information of people who stayed for a set time within a set distance, and it will be automatically cleared after 21 days[18].This reduces the time to find and locate close contacts to a certain extent. However, there are strict restrictions on the duration of stay.
Besides, Apple and Google jointly developed an API for exposure notification, which can be used across borders. It is mainly based on the DP3T protocol[19]. The DP3T protocol is a decentralized protocol. Exposure notification APIs have been applied by applications in many countries, such as COVID Tracker in Ireland and My Trace in Malaysia[13]. The use and implementation largely depend on the corresponding public health department, and the authorities need to follow development standards such as privacy and security.
In addition to the national application development, many research institutions and scholars have also done a lot of related research. There are several types of related researches nowadays on the use of Bluetooth for digital tracking.
First, many studies have used RSSI-based distance estimation. In [20] the authors improved the accuracy of distance estimation based on RSSI. The authors of [21] maintain social distancing by RSSI.
Second, some studies have optimized the framework design for privacy security. The authors of [22] outline three common tracking application architectures and discusses privacy security issues. And In [23] the authors developed a privacy-preserving mobile and fog computing framework to track infections and suspected cases through fog nodes without revealing the identity of the infected person..
Third, some studies have assessed the risk level. In [24] the authors use the combination of Bluetooth and GPS. The article provides a self-assessment of the severity of the new coronavirus. The authors of [25] uses different features to calculate the matching scores of two users to complete contact tracking. But the above level assessment method given in those papers does not have real-time dynamics.
So the problem is that existing technology can not accurately locate the source of infection and the epidemic prevention and control measures are inadequate. Taking all these into account, this article provides a method and system for locating the source of infection based on Bluetooth to solve the predicament.
This system provides precise location of the source of infection based on GPS and Bluetooth. The system provides real-time recording and close contact tracking. And the system provides a real-time display of multi-user risk levels and location information of the source of infection. The technical background will be introduced in the second section. The third section will introduce the related algorithms adopted by this method. The fourth section will briefly introduce the system architecture and functional design. The advantage of the method proposed in this paper lies in the dynamic and real-time update of the risk level, which dynamically updates the risk level of people at regular intervals during the process of crowd contact. And warnings are issued as the risk level changes to help users isolate and self-isolate.The system does not rely strongly on infectious disease monitoring. When there is no infectious disease monitoring, the method can still calculate and update the regional risk level of each location and the risk level of each person at each time based on the big data of the flow of people. The method of locating the source of infection can be applied to suppress the transmission of infectious diseases and thus control or prevent epidemics. The method can also be used to intelligently generate epidemic investigation reports to guide epidemic prevention and control.
Round 2
Reviewer 1 Report
In section 4.1, in my opinion, still confusion if you are describing a system or a smartphone application to demonstrate the proposed system. I recommend to check the text of the section.
Author Response
Dear Reviewer,
I would like to express my sincere gratitude for the time and effort you have dedicated to reviewing our manuscript. Your insightful comments and constructive feedback have greatly improved the quality of our work. Your expertise and attention to detail are truly appreciated, and we are fortunate to have you as a reviewer for our paper.
Thank you again for your invaluable contribution to our research.
Best regards,
Yunan Han
Reviewer 2 Report
After amendments the paper is improved but it remains very conceptual. I still missing some results of the proposed concept, showing that the concept or system can operate. I have the impression that only some minor part of the system is implemented. The authors use the phrases "may be", "can be" and "will be" where I expect they will say it is implemented, "this set of parameters results into this performance", etc.
Furthermore, there should be space between the reference and word before referencing: transmission [2,3] in line 27. This mistake is repeated throughout the paper.
line 129 typo also repeated from the initial transmission: trajcectory._By ...
line 133: wirelessly received signal ..., I propose to delete wirelessly
lines 211 - 219: missing reference for values of proposed values, or explanation why the particular environment is proposed
In conclusion, if the policy of the journal is to accept the concept papers this minor revisions are sufficient for accepting the paper, otherwise the paper has to go under major revision.
Author Response
Response to reviewers
Thank you for allowing a resubmission of our manuscript, with an opportunity to address the reviewers’ comments. We also take this opportunity to thank the reviewers for their timely and thoughtful comments.
Reviewer#1 Concern # 1: After amendments the paper is improved but it remains very conceptual. I still missing some results of the proposed concept, showing that the concept or system can operate. I have the impression that only some minor part of the system is implemented. The authors use the phrases "may be", "can be" and "will be" where I expect they will say it is implemented, "this set of parameters results into this performance", etc.
Author response: Thank you for your valuable comments. The issues you raised are of utmost importance to us. We have successfully implemented several functions of the system, such as Bluetooth scanning and broadcasting, local updating of personal risk levels, GPS positioning and tracking, and alarm functions for high-risk individuals, as mentioned in the system design section of our paper. Additionally, we have implemented server-side users and device management, as well as report generation.
However, it is important to note that our system is still in the testing phase. The information obtained from a few smartphones during testing may not be entirely accurate, and as a result, our preset parameters may not be optimal. Therefore, we require more users and further testing to obtain relatively accurate parameters. Furthermore, the implementation of our system on a large scale is subject to policy and requires cooperation with government departments. As a result, it may be challenging to obtain risk level data in the actual population dissemination.
We are aware of these challenges and are working to address them in our future research. Our team plans to expand the testing scale and optimize parameter aspects to improve the accuracy of our system. We will keep you updated on our progress and look forward to sharing our findings with you in the future.
Reviewer#1 Concern # 2: Furthermore, there should be space between the reference and word before referencing: transmission [2,3] in line 27. This mistake is repeated throughout the paper. line 129 typo also repeated from the initial transmission: trajcectory._By
Author response: Thank you for your thorough review. We have carefully gone through the entire article and made the necessary corrections to address the errors.
Author action: We updated the manuscript as follows.
The current outbreak of the novel coronavirus SARS-CoV-2 and its variants has spread to many countries [1]. The virus has infected a wide range of humans and animals, with human-to-human transmission and animal-to-human transmission [2,3]. The number of infected people is still increasing, and many people show mild symptoms or no symptoms at all [4,5]. Asymptomatic infections are still at risk of transmission, which increases the difficulty of epidemic prevention, control, and monitoring. In the era of globalization, the rapid flow of the population has played a big role in the spread of the virus [6].
...
Specifically, the software installed on the target person’s mobile device may obtain Bluetooth permission and GPS permission to track the movement trajectory. By turning on the Bluetooth function, the target person can exchange individual identity information, personal risk level, and Bluetooth signal strength with other people in the Bluetooth critical transmission circle.
Reviewer#1 Concern # 3: line 133: wirelessly received signal ..., I propose to delete wirelessly
Author response: Thank you for your valuable comments. We have taken it into consideration and removed the word "wirelessly" from the paper.
Author action: We updated the manuscript as follows.
The information transmitted by smartphones includes received signal strength information, target personal risk level, and personal protection information.
Reviewer#1 Concern # 4: lines 211 - 219: missing reference for values of proposed values, or explanation why the particular environment is proposed
Author response: Thank you for your valuable comments. The values for A_1 and A_tt are based on empirical data since the exact location of surrounding Bluetooth devices is unknown. The range of these values is also discussed in the article referenced by doi: 10.1109/ICISE.2010.5691135.
Author action: We have added references as follows.
The above distance Dist is the estimated distance between two smart mobile terminals. Under normal circumstances, according to the surrounding environment, the value of can be given as 59, the value of is 2, and the value of is 0.2 [33].
