Contact network analysis of Covid-19 in tourist areas--Based on 333 confirmed cases in China.

The spread of infectious diseases is highly related to the structure of human networks. Analyzing the contact network of patients can help clarify the path of virus transmission. Based on confirmed cases of COVID-19 in two major tourist provinces in southern China (Hainan and Yunnan), this study analyzed the epidemiological characteristics and dynamic contact network structure of patients in these two places. Results show that: (1) There are more female patients than males in these two districts and most are imported cases, with an average age of 45 years. Medical measures were given in less than 3 days after symptoms appeared. (2) The whole contact network of the two areas is disconnected. There are a small number of transmission chains in the network. The average values of degree centrality, betweenness centrality, and PageRank index are small. Few patients have a relatively high contact number. There is no superspreader in the network.

1 Introduction

Coronavirus Disease 2019 (Covid-19) has broken out in many countries around the world and drawn widespread global attention since 2019 [1, 2]. On January 30, 2020, the World Health Organization declared the epidemic a Public Health Emergency of International Concern. This virus spreads via respiratory droplets, close proximity interactions (CPIs), and airborne transmission [3, 4], with an incubation period of 1–14 days. According to the joint investigation report of COVID-19 released by China and World Health Organization in February 2020, there is little or no pre-existing immunity in most populations [5]. Its main clinical manifestations include fever, dry cough, and fatigue. The outbreak of the epidemic has caused a major impact on society and economy, especially in areas where the tourism industry is more developed.

Existing research on COVID-19 is mostly based on epidemiology, virology, and medicine, involving patients’ case analysis, infection model construction, gene sequencing, and clinical diagnosis, etc. [6-12]. However, there is a lack of analysis on virus transmission from the perspective of a complex network. In particular, existing models lack the description of particular diffusion paths from actor to actor, so the interventions based on these models lack pertinence [13]. The spread of the virus is closely related to human social network [14]. It is the connection, gathering, and movement of people, that makes the virus spread and form a transmission network [14-19]. Existing studies have analyzed the relationship between population movement among cities and infection ratio during the epidemic based on mobile phone data on the macro-level [20]. But there is still less analysis from the micro-individual level based on the contact network of COVID-19 patients. The topology of the contact network plays a critical role in virus spread [21]. Human social networks are heterogeneous, and the number of people an individual contact with every day is different. Therefore, the probability that each individual may spread the virus is not the same. Intervention policies should also be targeted and heterogeneous [22]. Prior research showed that the immunization strategy based on the actual contact network is better than the uniform random immunization strategy [19].

This study collects detailed information about patients diagnosed with COVID-19 that are reported by the Health Commission of Hainan and Yunnan Provinces of China in Janauary and February, 2020. These two regions are major tourism provinces in China, so the epidemic has a greater impact on the local economy and society. We extract patients’ demographics and characteristics by text mining method, and construct the contact network based on patient movement tracks and contact records.

2 Data and method

All samples in this study are officially announced cases by the Health Commission of Hainan and Yunnan Provinces of China. By February 16, 2020, there are a total of 333 cases. The study received approval from the Ethics Committee of Xi’an Jiaotong University Health Science Center (No. 2020–1217). All the data are anonymous. The specific steps of text mining and coding are as follows.

First, based on the detailed information of confirmed cases, we summarized these patients’ demographic and epidemiological characteristics, including gender, age, residence, place of onset, place of infection, time of arrival, symptom outbreak time, quarantine time, and diagnosis time, etc.

Second, the Health Commissions of Hainan and Yunnan listed whether the patient is a family member, friend, or close contact of the previously confirmed patients. We construct the patient contact network in chronological order, calculate relevant network indicators, and draw a dynamic visual network of virus transmission between patients to show how the epidemic develops day by day. We use three network indicators to measure the position of patients in the network.

The first statistic we consider is the degree centrality. It measures the number of direct contacts of one patient [16]. The specific formula is as following, where a is whether case i contacts case j.

Our next statistic is betweenness centrality. It measures whether one patient is in the bridge position of a transmission path in the network [16]. The higher the betweenness centrality of a patient, the higher the risk of him or her spreading the virus. The specific formula is as followin. The betweenness centrality of case i is gjk(i) divided by gjk, which means the ratio of the geodesic passing through case i and connecting case j and k to the total number of geodesic between case j and k.

Then we consider PageRank index. It measures the centrality of a patient in the whole network [23, 24]. PageRank measures how likely one is to arrive at a given node by moving randomly around a network, and is calculated iteratively. In the calculating process, we not only consider the number of direct contacts of patients but also the number of indirect contacts. The PageRank value of all nodes in the network converge to limiting values as the number of iterations goes to infinity. More details of the calculation can be found in [24]. Cases with a higher PageRank value are at the center of the whole contact network. The specific formula is as follows. PRj is the PageRank value that the case j directly contacted with the case i, nj is case j’s ego network size, which means the number of cases connected with case j, and Bi is the set containing all nodes linking to the focal node j.

Finally, we visualize the network by decomposing it into different communities based on components separated layout. A component means that, within in it, all members can be connected by a well-defined path. Besides, no connection exists between members from two different components. Through visualization, we can intuitively observe changes in the epidemic contact network over time.

3 Results and discussion

3.1 Epidemiological characteristics of patients with COVID-19

Based on the movement trajectories of confirmed cases reported by the Health Commission of Hainan and Yunnan Provinces, basic information of the patients’ gender, place of infection, and route of infection were counted (see Table 1). Specifically, there are slightly more female cases, accounting for more than half. Over 80% cases were infected out of the province, which means that most cases are imported. Also, about 48% cases’ relatives were infected.

Table 1

Epidemiological information of cases with COVID-19 in Hainan and Yunnan Province of China (N = 333)*.

Gender	Frequency	Percentage (%)	Infected place	Frequency	Percentage (%)
Female	142	50.35	Inside Province	55	18.71
Male	140	49.65	Outside Province	239	81.29
Total	282	100	Total	294	100
Whether any relatives are infected?	Frequency	Percentage (%)	Age	Mean	S.D.
Yes	106	47.75		45.33	18.06
No	116	52.25
Total	222	100

* In Yunnan Province, 51 cases did not report gender. In the two provinces, 39 cases did not report places of infection, 111 cases did not report contacts, and 52 cases did not report age.

Average age of all cases in these two areas is 45 years old, with the youngest 3 months old, and the oldest 79 years old. The frequency distribution of cases’ age is left-skewed (see Fig 1). The most highly infected groups are the 25–40 years old and the 50–70 years old, among which the 65-year-old has the largest number, with 11 people infected.

Fig 1

Age distribution of Covid-19 cases in Hainan Province and Yunnan Province, China.

(N = 281).

As showed in Figs 2 and 3, cases’ average age in the two provinces varies with time. In the data of Hainan Province, except for January 27, January 28, and February 9, the average age is stable at 40–60 years old. In the data of Yunnan Province, except February 4, February 8, and February 12, the average age is stable at 30–50 years. Compared with Hainan Province (48.44 years), the average age of cases in Yunnan Province (41.09 years) is lower. One possible reason for the age gap might be that the tourism industry is the pillar industry in Hainan Province, mild and warm all-year-round climate attracts more elderly tourists coming to spend the winter. Tourism in Yunnan Province is typically less concentrated towards older age groups. In Yunnan Province, 21.01% of the cases are aged 60 years and above, which exceeds the proportion of the local population aged 60 years and above in the total population (14.91%, China the seventh National Census). In Hainan Province, 32.72% of the cases are aged 60 years and above, which is greater than the proportion of this age group in the total population (14.65%, China the seventh National Census). Compared with young people, older people may have a weaker immune system, which will make them more susceptible to the virus.

Fig 2

Mean Age of Covid-19 cases in Hainan Province, China.

Fig 3

Mean Age of Covid-19 cases in Yunnan Province, China (cases before Jan.22 did not report patient’s age).

For imported cases, we also summarized the number of days between arrival in Hainan or Yunnan and emergence of symptoms (such as cough, fever, etc.), the number of days between arrival and relevant medical measures(such as seeing a doctor, quarantine, etc.), and the number of days between emergence of symptoms and taking medical measures. As showed in Table 2, statistics show that in Hainan, the average time between arrival and emergence of symptoms is 9.96 days, with the earliest case emerge symptoms 1 day before arrival and the latest 66 days after arrival. The average time between arrival and seeing a doctor or being quarantined is 10 days, with the earliest case taken medical treatment 11 days before arrival. The average time between the emergence of symptoms and seeing a doctor or taking medical treatment is 3 days. The earliest case took medical measures 4 days before onset of symptoms, and the latest one took medical measures 12 days after onset of symptoms. While in Yunnan Province, the data is a bit different. The average time between arrival and emergence of symptoms is 4.5 days in Yunnan, with the earliest case emerge symptoms 19 days before arrival and the latest one 22 days after arrival. The average time between arrival and seeing a doctor or being quarantined is 8 days. The earliest case went to see a doctor once arriving, whereas the latest one did not take any medical treatment until 23 days after arriving in Yunnan. The average time of taking relevant medical treatment after symptoms emerge is about 2 days.

Table 2

Statistics related to the onset time of Covid-19 cases in Hainan and Yunnan Province, China.

Variables	Number of cases	Mean	S.D.	Minimum value	Maximum value
Symptom onset date minus arriving in Hainan date (days)	23	9.96	15.73	-1	66
Diagnosis/quarantined date minus arriving in Hainan date (days)	118	9.92	17.18	-11	118
Diagnosis/quarantined date minus Symptom onset date (days) in Hainan	31	2.58	3.40	-4	12
Symptom onset date minus arriving in Yunnan date (days)	6	4.50	13.58	-19	22
Diagnosis/quarantined date minus arriving in Yunnan date (days)	40	8.05	5.17	1	23
Diagnosis/quarantined date minus Symptom onset date (days) in Yunnan	31	2.55	5.49	-2	23

It is worth noting that there are lots of imported cases in Hainan and Yunnan. Many of them are tourists, and they have a large range of activities. For example, cases No. 73 and No. 87 in Hainan Province crossed districts and counties about 8 times. Such movement tracks increased the risk of spreading the virus and difficulty for epidemic prevention.

Figs 4 and 5 respectively show the average symptoms onset time and average diagnosis time per day in each province. In both regions, the average diagnosis time and onset time have an increasing trend. This means that with the development of the epidemic, it takes more time to discover potential cases.

Fig 4

Changes in the average onset and diagnosis time of Covid-19 cases in Hainan Province.

Average onset time refers to the time when symptoms appear in imported cases after they arrive in Hainan. Average diagnosis time refers to the time of taking medical measures after symptoms appear.

Fig 5

Changes in the average onset and diagnosis time of Covid-19 cases in Yunnan Province.

Average onset time refers to the time when symptoms appear in imported cases after they arrive in Yunnan. Average diagnosis time refers to the time of taking medical measures after symptoms appear.

3.2 Contact network of patients with COVID-19

We visualized the dynamic contact network of patients with COVID-19 in Hainan and Yunnan provinces by February 16, 2020. As showed in Figs 6 and 7, each figure contains three parts: the network at the early stage of the epidemic, the network at the middle stage of the epidemic, and the network at the late stage of the epidemic. Nodes represent cases, numbers in nodes are diagnosis date. The larger the number is, the later that case was diagnosed. Edges in the network represent contact relation (family members, friends, close contact, or strangers) between cases. Size of node represents the degree centrality of each case. The larger the node is, the more contact relations that case has with others. There are many unconnected nodes in each network, most of which are imported cases. According to existing information, it is hard to trace the source of infection out of province.

Fig 6

Dynamic contact network of cases with Covid-19 in Hainan (a) January 30, 2020; (b) February 8, 2020; (c) February 16, 2020.

Fig 7

Dynamic contact network of cases with Covid-19 in Yunnan (a) January 30; (b) February 8; (c) February 16.

Fig 6 shows the contact networks in Hainan Province. The early network by January 30 is still relatively sparse, most cases no relation with others. Cases No.38, No. 44, and No. 45 are from one family and formed the largest cluster (component). They are imported cases from Wuhan, Hubei Province. In the mid-term network by February 8, several major infection clusters are formed, most of which are cluster infections caused by imported cases (tourists from Hubei). The size of these clusters is normally 3–6, and would still be expanding in the late stage. In the late stage network by February 16, the newly emerged cluster is composed of 7 cases including case No. 121, which is a local family infection cluster. The largest cluster consists of 9 cases including case No. 130. There are many clusters of full connection in the network, which are composed of family members or close friends. According to the contact data, we found that there are more parallel infection networks caused by strong connections (cases in the network are diagnosed almost at the same time) than serial infection networks caused by strangers or weak connections (cases in the network have a clear order of diagnosis).

It can be seen from Fig 7 that the early network in Yunnan Province was also relatively sparse. There were only two clusters in the first stage network, both did not expand further. It indicates that these cases were effectively controlled at the early stage and did not cause further spread. Cases in the two clusters were infected in Wuhan and traveled to Yunnan. In the mid-term, cluster infections appear, several major clusters emerged in the network. Cases in these clusters are mostly imported cases that traveled from Wuhan to Yunnan for visiting family members. Some of their close contacts in Yunnan were also infected. However, these clusters did not form larger clusters in the late stage, indicating that most infected people were given medical treatment or quarantined in time. The later network was divided into multiple clusters, most of which were fully connected. Among them, the largest cluster is composed of thirteen cases including case No.139. In this cluster, case No.139 participated in a village-wide gathering activity and contacted with people returned from Hubei, leading to this cluster of infection. In the network, case No.139 is a typical hub of virus spreading and should be the primary target of epidemic prevention. In general, compared with the contact network in Hainan, the contact network in Yunnan has fewer clusters. However, there is a very large cluster, which is mainly caused by case No.139. This shows that the epidemic prevention and control in the tourist area should focus on preventing imported super-infected persons.

Table 3 shows the centrality indicators of the contact network in Hainan Province. Degree centrality can reflect the basic reproduction number of the virus in a certain extent. The average degree centrality of cases with COVID-19 in Hainan Province is 1.51. The minimum centrality is 0, and the maximum is 6. The maximum centrality is case No.121, who connected with other six cases. These cases formed a fully connected cluster. Betweenness centrality indicates whether the patient is in a bridge position of the infection chain. Its average value is 0.0000, indicating that there are fewer infection chains formed in the contact network. PageRank index indicates the centrality of each case’s position in the whole contact network rather than ego network. The average value is only 0.0062, while the maximum value is 0.0189, indicating that the degree of connection between cases is unevenly distributed and the deviation is large.

Table 3

Statistics of the contact network of Covid-19 cases in Hainan Province.

Variables	Cases	Mean	S.D.	Minimum Value	Maximun value
Degree Centrality	162	1.51	1.79	0	6
Betweenness Centrality	162	0.0000	0.0001	0.0000	0.0012
PageRank Index	162	0.0062	0.0043	0.0015	0.0189

Table 4 shows the network indicators of Yunnan Province. The average degree centrality is 1.345. The smallest degree is 0 and the largest is 12. The average value is smaller than that of Hainan Province(1.51), but the standard deviation is much larger. That is because there is one case (Case No.139 in Fig 7) that owned 12 contacts and they formed the largest cluster in the network. The betweenness centrality of the contact network in Yunnan Province is same of Hainan Province, with an average of 0.0000. The main reason is that there are many clusters in the network, and there is no path between these clusters. Therefore, the overall connectivity of the network is at a low level. The average PageRank index of Yunnan Province is 0.0058, which is lower than that of Hainan Province (0.0062), but the standard deviation is larger than that of Hainan Province. The maximum value (0.0221) is also larger than that of Hainan Province (0.0015). This indicates that a small number of cases own more contacts than others, leading to the more uneven distribution than Hainan Province.

Table 4

Statistics of the contact network of Covid-19 cases in Yunnan Province.

Variables	Cases	Mean	S.D.	Minimum Value	Maximum value
Degree Centrality	171	1.345	3.187	0	12
Betweenness Centrality	171	0.0000	0.0000	0.000	0.0030
PageRank index	171	0.0058	0.0055	0.0020	0.0221

Figs 8 and 9 show the trend of network indicators over time, including the number of nodes, the number of edges, the average degree, the density, and the size of the largest component. In both regions, the number of nodes, the number of edges, and the average degree show an obvious increasing trend. This means that with the development of the epidemic, the number of cases and the connection between them are increasing. But the density and the size of the largest component did not change a lot. It indicates that the density of the contact network in these two areas is both at a low level, and the size of the components is mostly on a small scale.

Fig 8

Trend of network indicators of Hainan Province (Nodes, edges, and the size of the largest component refer to the y-axis coordinates on the left.

Average degree and density refer to the y-axis coordinates on the right).

Fig 9

Trend of network indicators of Yunnan Province (Nodes, edges, and the size of the largest component refer to the y-axis coordinates on the left.

Average degree and density refer to the y-axis coordinates on the right).

4 Conclusion

By analyzing two major tourist regions of Hainan and Yunnan in China, the study found that: (1) The susceptible population is mostly middle-aged men. There were more imported cases in the early stage of the epidemic and more local cluster infections in the late stage. Cases in the two regions received medical treatment in less than three days on average after they developed symptoms. In the early stage of the epidemic in the two provinces, cases are mostly young and middle-aged people who have a wide range of activities and strong transmission ability. At the late stage, cases are mostly elderly people. Our results have some reference value for future infectious disease control policies. However, specific epidemic prevention policies need to consider multiple data sources and be justified by rigorous interventions. (2) The contact network in two areas is consisted of multiple clusters. The whole network is disconnected, and the degree distribution is skewed. The highest contact number is 6 in Hainan and 12 in Yunnan, which shows that the network connectivity in these two areas is relatively low.

We did not include any intervention in this study and epidemic control policies in Hainan and Yunnan are essentially the same, so we cannot compare the effects of different political interventions. However, our study still illustrates that if we face with a similar epidemic situation in the future, abundant information can be mined from case report text for epidemic control. When this information is quickly collected and analyzed by epidemic prevention department, they can, after anonymization, provide the public with quick and clear understanding of virus transmission information such as age, gender, and onset time distribution of each case. The epidemiology department can also map the transmission network of virus based on merging multiple cases, allowing the public to understand the spread of the virus. This data analyzation and visualization are totally based on case reports, which can be easily implemented without much cost.

In general, most of the research on the COVID-19 epidemic has focused on the epidemiological characteristics of confirmed patients. This study helps to better understand the social network of COVID-19 cases by visualizing dynamic contact networks and analyzing network centrality. Due to the difficulty of data collection and lack of patient movement information provided by the Health Commission, we only analyzed confirmed cases. We don’t know much about those imported cases before their arrival. Information such as movement trajectory and infection route is still unclear. Therefore, it is hard to present a cross-regional case contact network. Furthermore, Covid-19 can also be spread via airborne transmission [3, 4], especially in more confined spaces [25]. Under such conditions, transmission may occur between two people that are in the same space while did not meet face to face, so network analysis cannot fully capture the virus transmission pathway. Because of limited data, we underestimated the corresponding network metrics and connectivity. Therefore, a more complete analysis of virus transmission paths requires the integration of network analysis, geography information system and mobile trajectory analysis. The above needs to be further studied based on more detailed case information.

18 Jun 2021

PONE-D-21-13537

Contact Network Analysis of Covid-19 in Tourist Areas——Based on 333 Confirmed Cases in China

PLOS ONE

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Reviewer #1: The authors have carried out an analysis of epidemiological data gathered in two Chinese provinces during the early stages of the COVID-19 pandemic, including an analysis of the age and gender profile of cases as well as of an inferred underlying transmission network. While the analysis itself has been conducted to a good standard, I have a few comments about the presentation of the work, and I am particularly concerned that the authors have overstated the policy implications of their analysis.

Main comments:

As stated in the short summary of my review, the authors have repeatedly made statements on policy which are not justified by the scientific content of the paper. The authors have studied the epidemiological characteristics of a relatively small number of cases and analysed the underlying contact network, and have not carried out any mechanistic modelling of control measures or interventions. I am not suggesting that the authors should be carrying out mechanistic modelling, but they need to remove, or at least qualify, some of their statements on policy. In the abstract, the authors say “The study suggests that the tourism industry should adopt a strategy of opening up different scenic spots during the epidemic and strengthen the detecting and tracking of tourists”. In general, when making policy recommendations it is much better to state results in the form “If policy X is implemented, then our study suggests outcome Y will occur”, rather than just “Policy X should be implemented.” The “If X then Y” phrasing is more information-rich and states a scientific finding, whereas the “X should be implemented” phrasing contains less information and states a political, or even moral, imperative. The “If X then Y” phrasing is thus better suited to the abstract of a scientific paper. However, since the authors have not performed any modelling of interventions or drawn explicit connections between the differences in policy in the two provinces and the differences in epidemic spread, I do not feel their work justifies any definite “If X then Y” statements; while their analysis may point to places where a certain policy might be useful, it certainly is not capable of rigorously predicting the impact of a given policy. In particular, I see no way the authors can predict the impact of opening up certain scenic spots based on the work presented here. I would also like to point out that this is a very vague policy recommendation, and based on what is stated in the abstract and in the conclusions it could refer to any control measures other than the closure of all scenic spots.

In their conclusion section on page 21, the authors say “It shows that prevention and control measures such as quarantine are effective”. First of all, it is not entirely clear what the “It” here is – I assume they are referring to their complete analysis, but it would be good to make this clear. More importantly, the only detailed reference to quarantine measures comes in their analysis of the Yunan transmission network on page 16, where they say “these clusters did not form larger clusters in the late stage, indicating that the infected people were taken medical treatment or quarantined in time”. There appears to be some circular reasoning at work here: the authors infer that small cluster size has been caused by quarantining without explaining why they have made this inference, and then later argue that their analysis supports the use of quarantine. However, based on the evidence provided one could (very facetiously) propose any mechanism one wanted to for the small cluster size, and then later argue that the analysis supports policies involving this mechanism. The authors need to either remove the comment about quarantine on page 21, or provide more concrete reasoning for why they think their analysis supports the use of quarantine.

On similar grounds, I recommend that the authors remove the entire paragraph on page 22 beginning “Based on the research results, we should strictly control the number of tourists…” and ending “…registration of information”. No indication is given of what the impact of the measures discussed in this paragraph is likely to be, or how this potential impact relates to the results of the network analysis. The authors could replace this paragraph with a more qualified discussion of the implications of their work for control policy.

Overall I recommend that the authors completely rewrite all of the sections of their paper which talk about policy interventions. While I do not necessarily disagree with any of these statements, I do not believe that the analysis conducted in the paper is at all sufficient to support the very confident recommendations which the authors make. A justified policy recommendation would need to state the likely impact of the policy, and the work presented here is not capable of predicting such impacts. A more tentative discussion of policy, outlining the ways the authors’ analysis could support a policy response, would be very welcome, but the sweeping recommendations made in the current manuscript would require either a mechanistic modelling study or a direct comparison of data from regions implementing different policies to be justified.

In their introduction, the authors state “This virus spreads via respiratory droplets and close proximity interactions (CPIs), with an incubation period of 1-14 days.“ While there has been some controversy on this point I believe it is now well-established that COVID-19 can be spread through airborne transmission (I have provided a reference for this at the end of this review, but I recommend the authors seek out some more references to get a wider view of the evidence on this topic). This transmission pathway is obviously less determined by human social networks than the CPI and droplet components. While a network analysis is still extremely worthwhile, it would be good to point out the limitations implied by airborne transmission; for instance, infection can potentially spread across large rooms or between neighbouring rooms connected by a ventilation system, meaning the index and secondary case may not necessarily have directly interacted and so will not necessarily belong to a shared social contact network. While the authors are already careful not to imply that all transmissions are “on-network” transmissions, statements like “The analysis of the virus contact network is the key to understand and clarify the spread of disease” may be overstating the importance of the transmission network in light of “non-network” airborne transmissions.

On page 6, the authors introduce some network statistics, stating “These are the three specific network indicators that we used to depict the postion of patients in the network”. I am not sure that “depict” is the right word here. These statistics really act to quantify the patients’ positions, and I would like to see this sentence adjusted accordingly. As an aside, I note here that “position” is misspelt.

The definition of betweenness centrality on page 6 is incomplete. In their verbal explanation the authors say “The betweenness centrality of the patient i is the ratio of the geodesic passing through patient i and connecting patients j and k to the total number of geodesic between patients j and k.” This clearly isn’t a correct description; based on the description given on Wikipedia, I believe this should be the sum of all such ratios over all distinct pairs of j and k. In their algebraic formula they do not define the term b_jk(i). If this term is supposed to be the ratio, the authors should state this. If it is just meant to be the number of geodesics passing through I, then they also need to add an appropriate denominator to their formula. Similarly, the authors do not provide any indication of how to calculate Pagerank index. The authors need to either state how it can be calculated, or provide an appropriate reference. The Brin and Page 1998 paper does not actually include an explicit formula for Pagerank and so is not sufficient here. The authors also mention “patient j’s ego network size” in this section, but do not explain what an ego network is. For publication, a definition of the ego network needs to be provided.

The description of the network component on page 7 is a bit confusing: “Network component means that within it, each patient has a network path reach with others, it may be a direct tie or an indirect tie”. I think what the authors mean is that all members of a network component can be connected by a well-defined path. It would be good to reword this sentence to make its meaning clearer.

In Figure 1 on page, no indication is given of what the solid line corresponds to. I assume this is a distribution which has been fitted to the data. The authors do not actually appear to use this fitted distribution anywhere in the paper, and no a priori reasoning is given for why the unimodal distribution they have attempted to fit should give a good approximation of the age distribution of cases. In practice the age distribution of cases will depend on a range of factors including the age distribution of the underlying population, the contact patterns within that population, and age-stratified heterogeneities in behaviour and physiology. This complex combination of factors means that a simple parametric distribution is unlikely to give a good explanation of the data, and so I do not think there is anything to be gained from fitting to such a distribution. I therefore recommend that the authors either remove this line from the plot, or provide a description of their fit and a compelling explanation as to why they have carried it out. The choice of bins and axis ticks in Figure 1 also needs to be changed. In the body text they refer to five-year intervals, their bars appear to be defined according to four-year intervals, and the axis ticks are at five year intervals starting at 3. The figure needs to be redrawn with five-year bins starting at zero, and matching axis ticks. In the body text describing Figure 1 the authors say “The frequency distribution of patients’ age is the left-skewed distribution”. I believe they mean that the frequency distribution is left-skewed, since “the left-skewed distribution” is not any specific probability distribution.

Typos and minor comments:

Abstract - “The spread of virus is highly related to the stucture of human network.” should be “The spread of virus is highly related to the structure of human networks.”

“There are more male patients than the females in these two districts, most are imported cases, with an average age of 45 years” should be “There are more male patients than the females in these two districts and most are imported cases, with an average age of 45 years”

“There is no superspreader in the network” – since outbreaks can have more than one superspreader, this should really say “There are no superspreaders in the network”.

Page 2: “Coronavirus Disease 2019 (Covid-19) has outbroken in many countries around the world and drew widespread global attention” should be “Coronavirus Disease 2019 (Covid-19) has broken out in many countries around the world and drawn widespread global attention”

Page 3: “Population is generally susceptible.” This statement is a bit vague. Something like “There is little or no pre-existing immunity in most populations”, with an appropriate citation, would be more specific.

Page 5: “Pior research showed that the immunization strategy based on the actual contact network is better than the random immunization strategy ( Eubank et al., 2004).” While I am not familiar with the Eubank et al. paper, a network-based immunisation strategy can still be random in the sense that each individual has a certain probability of being vaccinated, and I expect the authors mean uniform random immunisation (i.e. each individual has the same probability of being immunised) rather than just random immunisation. The authors have also misspelt “prior” at the start of this sentence.

Pages 6-7: The list of network statistics (degree centrality, betweenness centrality, Pagerank index) is currently presented as three paragraphs, with the first sentence of each being the name of the statistic. Depending on what the journal’s formatting requirements allow, I would rather see this presented in either a more explicit list form (i.e. with bullet points or at least bold item headings) or as “normal” text (i.e. “The first statistic we consider is…. Our next statistic is…. Finally, we consider….”).

Page 7: “Over 80% of patients were infected out of the province, which means there are more imported cases.” While the meaning here is clear, this is slightly poor wording, and should be amended to something like “Over 80% of patients were infected out of the province, which means that most cases are imported.”

Table starting on page 7 spills over to the next page and should be reformatted.

Page 9: “Hainan Province, where is a major pure tourism area in China” should be “Hainan Province, which is a major pure tourism area in China”

Table 2 on page 11: the column labelled “Case number” should be labelled “Number of cases”.

Tables 3 and 4 have final column labelled “Maximus value”, which should be “Maximum value”.

Page 22: “In general, most of the researches on the COVID-19 epidemic…” should be “In general, most of the research on the COVID-19 epidemic…”

Data availability – while the authors have provided a link to a repository containing their data and code, I was unable to access it. Under the “Files” tab on the repo website I got a message saying “Loading files” which did not resolve itself after >10 minutes of waiting. Because I can not see the code and the authors have also not provided a complete methodology for their calculation of betweenness centrality and Pagerank index, I can not confirm that their analysis has been carried out correctly.

Reference for airborne transmission:

Lidia Morawska, Julian W. Tang, William Bahnfleth, Philomena M. Bluyssen, Atze Boerstra, Giorgio Buonanno, Junji Cao, Stephanie Dancer, Andres Floto, Francesco Franchimon, Charles Haworth, Jaap Hogeling, Christina Isaxon, Jose L. Jimenez, Jarek Kurnitski, Yuguo Li, Marcel Loomans, Guy Marks, Linsey C. Marr, Livio Mazzarella, Arsen Krikor Melikov, Shelly Miller, Donald K. Milton, William Nazaroff, Peter V. Nielsen, Catherine Noakes, Jordan Peccia, Xavier Querol, Chandra Sekhar, Olli Seppänen, Shin-ichi Tanabe, Raymond Tellier, Kwok Wai Tham, Pawel Wargocki, Aneta Wierzbicka, Maosheng Yao,

How can airborne transmission of COVID-19 indoors be minimised?,

Environment International,

Volume 142,

2020,

105832,

ISSN 0160-4120,

https://doi.org/10.1016/j.envint.2020.105832.

**********

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Reviewer #1: Yes: Joe Hilton

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11 Aug 2021

Authors’ Response

Dear Editor and Review,

Thank you very much for the helpful comments that you and the reviewer provided on our manuscript. We are very pleased with the detailed modification suggestions and grateful to have the opportunity to refine our paper on these valuable suggestions. As you will see, we have addressed all the issues raised, which are highlighted in yellow in the manuscript and detailed below point by point. We look forward to your rechecking the paper and making other valuable suggestions for us.

Thank you once more for your constructive feedback and tireless patience. We hope that the refined manuscript meets your expectations.

Yours sincerely,

Authors of PONE-D-21-13537

Main comments: Please ensure that you refer to Figure 9 in your text as, if accepted, production will need this reference to link the reader to the figure.

Our response: Thank you very much for your attentiveness. We have checked the text and corrected this typo to Figure 8.

Main comments: As stated in the short summary of my review, the authors have repeatedly made statements on policy which are not justified by the scientific content of the paper. The authors have studied the epidemiological characteristics of a relatively small number of cases and analysed the underlying contact network, and have not carried out any mechanistic modelling of control measures or interventions. I am not suggesting that the authors should be carrying out mechanistic modelling, but they need to remove, or at least qualify, some of their statements on policy.

Our response: Thanks for your suggestions. We have removed all statements on policy because we can’t do any intervention.

Main comments: In their conclusion section on page 21, the authors say “It shows that prevention and control measure such as quarantine are effective”. First of all, it is not entirely clear what the “It” here is – I assume they are referring to their complete analysis, but it would be good to make this clear. More importantly, the only detailed reference to quarantine measures comes in their analysis of the Yunan transmission network on page 16, where they say “these clusters did not form larger clusters in the late stage, indicating that the infected people were taken medical treatment or quarantined in time”. There appears to be some circular reasoning at work here: the authors infer that small cluster size has been caused by quarantining without explaining why they have made this inference, and then later argue that their analysis supports the use of quarantine. However, based on the evidence provided one could (very facetiously) propose any mechanism one wanted to for the small cluster size, and then later argue that the analysis supports policies involving this mechanism.

Our response: According to your suggestions, we removed related discussion on policy measures. We note that our findings can be used as a reference in the policy process, but it is clear that specific policies require a combination of multiple data sources.

Main comments: Overall I recommend that the authors completely rewrite all of the sections of their paper which talk about policy interventions. While I do not necessarily disagree with any of these statements, I do not believe that the analysis conducted in the paper is at all sufficient to support the very confident recommendations which the authors make. A justified policy recommendation would need to state the likely impact of the policy, and the work presented here is not capable of predicting such impacts. A more tentative discussion of policy, outlining the ways the authors’ analysis could support a policy response, would be very welcome, but the sweeping recommendations made in the current manuscript would require either a mechanistic modelling study or a direct comparison of data from region simplementing different policies to be justified.

Our response: We have completely rewritten the section on policy. We did not revisit the effectiveness of the policy because this was not possible in the absence of interventions and regional comparisons. We mainly discussed the fact that epidemic prevention departments can quickly analyze case texts, integrate case data, map networks, and make them available to the public after anonymization in the case of similar disease outbreaks. This can give the public a clear picture of development of the outbreak.

Main comments: In their introduction, the authors state “This virus spreads via respiratory droplets and close proximity interactions (CPIs), with an incubation period of 1-14 days.“ While there has been some controversy on this point I believe it is now well-established that COVID-19 can be spread through airborne transmission (I have provided a reference for this at the end of this review, but I recommend the authors seek out some more references to get a wider view of the evidence on this topic). This transmission pathway is obviously less determined by human social networks than the CPI and droplet components. While a network analysis is still extremely worthwhile, it would be good to point out the limitations implied by airborne transmission; for instance, infection can potentially spread across large rooms or between neighbouring rooms connected by a ventilation system, meaning the index and secondary case may not necessarily have directly interacted and so will not necessarily belong to as hared social contact network.

Our response: In addition to the literature given by the reviewer, we also refer to some other literature related to airborne transmission. In the introduction section, we added airborne as a main transmission pathway. In the conclusion section, we added a discussion about the mode of transmission. Airborne transmission makes network analysis cannot depict the full transmission as you pointed out since virus can transmit between two strangers in different space, and the virus transmission should be discussed in the context of geographical location and space. We also put it into the limitation of this study.

Main comments: On page 6, the authors introduce some network statistics, stating “These are the three specific network indicators that we used to depict the postion of patients in the network”. I am not sure that “depict” is the right word here. These statistics really act to quantify the patients’ positions, and I would like to see this sentence adjusted accordingly. As an aside, I note here that “position” is misspelt.

Our response: Thank you very much for your suggestions for corrections. We have adjusted “depict” to “measure” and modified the misspelt of “position”. New sentence is as: “These are the three specific network indicators that we use to measure the position of patients in the network.”

Main comments: The definition of betweenness centrality on page 6 is incomplete. In their verbal explanation the authors say “The betweenness centrality of the patient i is the ratio of the geodesic passing through patient i and connecting patients j and k to the total number of geodesic between patients j and k.” This clearly isn’t acorrect description; based on the description given on Wikipedia, I believe this should be the sum of all such ratios over all distinct pairs of j and k. In their algebraic formula they do not define the term b_jk(i). If this term is supposed to be the ratio, the authors should state this. If it is just meant to be the number of geodesics passing through I, then they also need to add an appropriate denominator to their formula.

Our response: Thank you very much for your suggestions. We have revised the calculation formula of the betweenness centrality by adding an appropriate ratio to our formula to make it clearer and more explicit.

Main comments: Similarly, the authors do not provide any indication of how to calculate Pagerank index. The authors need to either state how it can be calculated, or provide an appropriate reference. The Brin and Page 1998 paper does not actually include an explicit formula for Pagerank and so is not sufficient here. The authors also mention “patient j’s ego network size” in this section, but do not explain what an ego network is. For publication, a definition of the ego network needs to be provided.

Our response: Thank you very much for your corrections. We have stated how Pagerank index can be calculated and provide an appropriate reference. And we also have provided a definition of the ego network. In our study, the patient j’s ego network size means the number of patients connecting with patient j.

Main comments: The description of the network component on page 7 is a bit confusing: “Network component means that within it, each patient has a network path reach with others, it may be a direct tie or an indirect tie”. I think what the authors mean is that all members of a network component can be connected by a well-defined path. It would be good to reword this sentence to make its meaning clearer.

Our response: Thank you very much for your suggestions. We have corrected the description of the network component on page 7 to make its meaning clearer. Network component means that within it, each patient has a network path reach with others, it may be a direct tie or an indirect tie, all members of a component can be connected by a well-defined path.

Main comments: In Figure 1 on page, no indication is given of what the solid line corresponds to. I assume this is a distribution which has been fitted to the data. The authors do not actually appear to use this fitted distribution anywhere in the paper, and no a priori reasoning is given for why the unimodal distribution they have attempted to fit should give a good approximation of the age distribution of cases. In practice the age distribution of cases will depend on a range of factors including the age distribution of the underlying population, the contact patterns within that population, and age-stratified heterogeneities in behaviour and physiology. This complex combination of factors means that a simple parametric distribution is unlikely to give a good explanation of the data, and so I do not think there is anything to be gained from fitting to such a distribution. I therefore recommend that the authors either remove this line from the plot, or provide a description of their fit and a compelling explanation as to why they have carried it out. The choice of bins and axis ticks in Figure 1 also needs to be changed. In the body text they refer to five-year intervals, their bars appear to be defined according to four-year intervals, and the axis ticks are at five year intervals starting at 3. The figure needs to be redrawn with five-year bins starting at zero, and matching axis ticks. In the body text describing Figure 1 the authors say “The frequency distribution of patients’ age is the left-skewed distribution”. I believe they mean that the frequency distribution is left-skewed, since “the left-skewed distribution” is not any specific probability distribution.

Our response: Thank you very much for your correction. We have redrawn Figure 1 by removing the solid line and changing axis ticks into five-year intervals starting at zero. We also revised the “left-skewed distribution” sentence: “The frequency distribution of patients’ age is left-skewed (see Figure 1).”

Typos and minor comments: Abstract - “The spread of virus is highly related to the stucture of human network.” should be “The spread of virus is highly related to the structure of human networks.”

Our response: Thank you very much for your corrections. We have changed this sentence in the abstract as: “The spread of virus is highly related to the structure of human networks”.

Typos and minor comments: “There are more male patients than the females in these two districts, most are imported cases, with an average age of 45 years” should be “There are more male patients than the females in these two districts and most are imported cases, with an average age of 45 years”.

Our response: Thank you very much for your corrections. We have modified the sentence into “There are more male patients than the females in these two districts and most are imported cases, with an average age of 45 years”.

Typos and minor comments: “There is no super spreader in the network” – since outbreaks can have more than one super spreader, this should really say “There are no super spreaders in the network”.

Our response: Thank you very much for your corrections. We have revised the sentence into “There are no super spreaders in the network”.

Typos and minor comments: Page 2: “Coronavirus Disease 2019 (Covid-19) has outbroken in many countries around the world and drew widespread global attention” should be “Coronavirus Disease 2019 (Covid-19) has broken out in many countries around the world and drawn widespread global attention”

Our response: Thank you very much for your corrections. We have amended the sentence into “Coronavirus Disease 2019 (Covid-19) has broken out in many countries around the world and drawn widespread global attention”.

Typos and minor comments: Page 3: “Population is generally susceptible.” This statement is a bit vague. Something like “There is little or no pre-existing immunity in most populations”, with an appropriate citation, would be more specific.

Our response: Thank you very much for your corrections. We have revised the sentence into “According to the joint investigation report of COVID-19 released by China and World Health Organization in February 2020, there is little or no pre-existing immunity in most populations”.

Typos and minor comments: Page 5: “Pior research showed that the immunization strategy based on the actual contact network is better than the random immunization strategy ( Eubank et al., 2004).” While I am not familiar with the Eubank et al. paper, a network-based immunisation strategy can still be random in the sense that each individual has a certain probability of being vaccinated, and I expect the authors mean uniform random immunisation (i.e. each individual has the same probability of being immunised) rather than just random immunisation. The authors have also misspelt “prior” at the start of this sentence.

Our response: Thank you very much for your corrections. We do focus on uniform random immunization rather than just random immunization, so we clarified the expression and modified misspelt word as following: “Prior research showed that the immunization strategy based on the actual contact network is better than the uniform random immunization strategy.”

Typos and minor comments: Pages 6-7: The list of network statistics (degree centrality, betweenness centrality, Pagerank index) is currently presented as three paragraphs, with the first sentence of each being the name of the statistic. Depending on what the journal’s formatting requirements allow, I would rather see this presented in either a more explicit list form (i.e. with bullet points or at least bold item headings) or as “normal” text (i.e. “The first statistic we consider is…. Our next statistic is…. Finally, we consider….”).

Our response: Thank you very much for your corrections. We have revised the first sentence at the beginning of the paragraph on Pages 6-7 into unified and standardized format: “The first statistic we consider is degree centrality.” “Our next statistic is betweenness centrality.” “Then we consider Pagerank index.”

Typos and minor comments: Page 7: “Over 80% of patients were infected out of the province, which means there are more imported cases.” While the meaning here is clear, this is slightly poor wording, and should be amended to something like “Over 80% of patients were infected out of the province, which means that most cases are imported.”

Table starting on page 7 spills over to the next page and should be reformatted.

Our response: Thank you very much for your corrections. We have amended the sentence on Page 7 into “Over 80% of patients were infected out of the province, which means that most cases are imported”. We also reformatted Table 1 for the spilling over problem.

Typos and minor comments: Page 9: “Hainan Province, where is a major pure tourism area in China” should be “Hainan Province, which is a major pure tourism area in China”

Table 2 on page 11: the column labelled “Case number” should be labelled “Number of cases”.

Our response: Thank you very much for your corrections. We have revised the sentence into “Hainan Province, which is a major pure tourism area in China” on Page 9. And we also have changed the column of Table 2 on page 11 into “Number of cases”.

Typos and minor comments: Tables 3 and 4 have final column labelled “Maximus value”, which should be “Maximum value”.

Our response: Thank you very much for your corrections. We have modified the word “Maximus” into “Maximum” in Tables 3 and 4.

Typos and minor comments: Page 22: “In general, most of the researches on the COVID-19 epidemic…” should be “In general, most of the research on the COVID-19 epidemic…”.

Our response: Thank you very much for your corrections. We have modified the sentence into “In general, most of the research on the COVID-19 epidemic…”.

Data availability – while the authors have provided a link to a repository containing their data and code, I was unable to access it. Under the “Files” tab on the repo website I got a message saying “Loading files” which did not resolve itself after >10 minutes of waiting. Because I can not see the code and the authors have also not provided a complete methodology for their calculation of betweenness centrality and Pagerank index, I can not confirm that their analysis has been carried out correctly.

Our response: We are sorry about the improper link. We re-uploaded our research data on a new data repository ICPSR. Please access the deposit workspace at: https://www.openicpsr.org/openicpsr/project/145901/version/V2/view. The data and code are in a Stata format and the network file is in a Gephi file, you may need relevant software to open them. Apologize again for the inconvenience we have brought to your work.

Our response: Moreover, we asked a copy editor to help improve our language. Thank you again for your very detailed and extremely constructive comments, they help improve this paper a lot. We would not have got a high-quality paper without these sincere suggestions. We look forward to hear more about your feedback.

Submitted filename: 20210729-Response Letter.docx

Click here for additional data file.

13 Sep 2021

PONE-D-21-13537R1Contact Network Analysis of Covid-19 in Tourist Areas——Based on 333 Confirmed Cases in ChinaPLOS ONE

Dear Dr. Wang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

In particular, you should carefully address the comments about the calculation of the PageRank index, as well as fixing the typographical and grammatical mistakes highlighted.

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Reviewer #1: (No Response)

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: In this revised version of the manuscript, the authors have addressed almost all of the points which I raised in my initial review. The link to their repository, which was not working in the original submission, has been replaced with a working link to a repository containing their code and data. The authors have also removed the references to policy which I identified as not being supported by the results of their study.

The only aspect of the paper which I feel is still insufficient Is the section on Pagerank index on pages 6 and 7. While this section is more detailed than it was previously, it still isn’t totally clear how Pagerank is calculated based on the details the authors provide. In particular, the authors do not define B_i, the set which they sum over in the formula at the top of page 7. However, given the authors have now provided a reference (Easley and Kleinberg 2010) which includes a detailed definition of Pagerank, I think it will be sufficient to give a qualitative description and point the reader to the reference for full detail. This description just needs to cover the basic idea that Pagerank measures how likely one is to arrive at a given node by moving randomly around a network, and that it is calculated iteratively.

While Figures 6 and 7 are useful for visualising the epidemic network, they are currently a bit small. Depending on the journal’s formatting requirements, it would be good to make these figures larger, possibly by rearranging each one into a 3x1 column of plots rather than the current 1x3 rows.

Minor grammar/typo notes:

• Abstract: “The spread of virus is highly related to the structure of human networks” should be “The spread of viruses is highly related to the structure of human networks”, or, since this holds for other types of pathogen, “The spread of infectious diseases is highly related to the structure of human networks”

• Abstract: “There are more male patients than the females in these two districts…” should be “There are more male patients than females in these two districts…”

• Top of page 5: “This study collects detailed information of patients diagnosed with COVID-19…” should be “This study collects detailed information about patients diagnosed with COVID-19…”

• Bottom of page 6: “By many times of iterations, the Pagerank value of each node in the network converge to a limiting value.”

• Top of page 10: “We also summarized the number of days that symptom had emerged”. I think the authors here mean “We also summarized the number of days between arrival in Hainan or Yunan and emergence of symptoms”, although the authors should confirm that this is indeed the intended meaning.

• Page 15: “However, these clusters did not form larger clusters in the late stage, indicating that most infected people were taken medical treatment or quarantined in time” should be “However, these clusters did not form larger clusters in the late stage, indicating that most infected people were given medical treatment or quarantined in time”

• Page 20: “When these information be quickly collected…” should be “When these information are quickly collected…”

• Page 20: “In general, most of the research on the COVID-19 epidemic focus on the epidemiological characteristics of confirmed patients” should be “In general, most of the research on the COVID-19 epidemic has focused on the epidemiological characteristics of confirmed patients”

• Page 21: “Because of limited date” should be “Because of limited data”.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Joe Hilton

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

5 Oct 2021

Authors’ Response

Dear Editor and Reviewer,

Thank you very much for the targeted and constructive comments that you and the reviewer provided on our manuscript. We are very pleased with the detailed modification suggestions and grateful to have another opportunity to refine our paper. As you will see, we have addressed all the issues raised again, which are highlighted in yellow in the manuscript and detailed below point by point. We look forward to your rechecking the paper and making other valuable suggestions for us.

Thank you once more for your constructive feedback and tireless patience. We hope that the refined manuscript meets your expectations.

Yours sincerely,

Authors of PONE-D-21-13537

Main comments: The only aspect of the paper which I feel is still insufficient Is the section on Pagerank index on pages 6 and 7. While this section is more detailed than it was previously, it still isn’t totally clear how Pagerank is calculated based on the details the authors provide. In particular, the authors do not define B_i, the set which they sum over in the formula at the top of page 7. However, given the authors have now provided a reference (Easley and Kleinberg 2010) which includes a detailed definition of Pagerank, I think it will be sufficient to give a qualitative description and point the reader to the reference for full detail. This description just needs to cover the basic idea that Pagerank measures how likely one is to arrive at a given node by moving randomly around a network, and that it is calculated iteratively.

Our response: Thank you very much for your attentiveness. We gave the define B_i in this section, where B_i is the set containing all nodes linking to the focal node j. We also have added a qualitative explanation of Pagerank that it measures how likely one is to arrive at a given node by moving randomly around a network, and it is calculated iteratively. We remind our readers that they can find the detailed calculation procedure in the book by Easley and Kleinberg.

Main comments: While Figures 6 and 7 are useful for visualising the epidemic network, they are currently a bit small. Depending on the journal’s formatting requirements, it would be good to make these figures larger, possibly by rearranging each one into a 3x1 column of plots rather than the current 1x3 rows.

Our response: Thanks for your suggestions. We have rearranged the plot of the pictures again to show more clear details of the contact network. Please see Figures 6 and 7.

Minor grammar/typo notes

Minor comments: Abstract: “The spread of virus is highly related to the structure of human networks” should be “The spread of viruses is highly related to the structure of human networks”, or, since this holds for other types of pathogen, “The spread of infectious diseases is highly related to the structure of human networks”.

Our response: Thank you very much for your suggestions for corrections. We have revised the sentence as “The spread of infectious diseases is highly related to the structure of human networks”.

Minor comments: Abstract: “There are more male patients than the females in these two districts…” should be “There are more male patients than females in these two districts…”.

Our response: Thank you very much for your suggestions for corrections. We have deleted the word “the” and revised the sentence as “There are more male patients than females in these two districts…”.

Minor comments: Top of page 5: “This study collects detailed information of patients diagnosed with COVID-19…” should be “This study collects detailed information about patients diagnosed with COVID-19…”.

Our response: Thank you very much for your corrections. We have adjusted the sentence as “This study collects detailed information about patients diagnosed with COVID-19…”.

Minor comments: Bottom of page 6: “By many times of iterations, the Pagerank value of each node in the network converge to a limiting value”.

Our response: Thank you. We have corrected the sentence as “The Pagerank value of all nodes in the network converge to limiting values as the number of iterations goes to infinity”.

Minor comments: Top of page 10: “We also summarized the number of days that symptom had emerged”. I think the authors here mean “We also summarized the number of days between arrival in Hainan or Yunan and emergence of symptoms”, although the authors should confirm that this is indeed the intended meaning.

Our response: Thank you very much for your correction. We have clarified the sentence as “We also summarized the number of days between arrival in Hainan or Yunan and emergence of symptoms”.

Minor comments: Page 15: “However, these clusters did not form larger clusters in the late stage, indicating that most infected people were taken medical treatment or quarantined in time” should be “However, these clusters did not form larger clusters in the late stage, indicating that most infected people were given medical treatment or quarantined in time”

Our response: Thank you very much. We have changed this sentence as: “However, these clusters did not form larger clusters in the late stage, indicating that most infected people were given medical treatment or quarantined in time”.

Minor comments: Page 20: “When these information be quickly collected…” should be “When these information are quickly collected…”

Our response: Thank you very much for your corrections. We have modified the sentence into “When these information are quickly collected…”.

Minor comments: Page 20: “In general, most of the research on the COVID-19 epidemic focus on the epidemiological characteristics of confirmed patients” should be “In general, most of the research on the COVID-19 epidemic has focused on the epidemiological characteristics of confirmed patients”

Our response: Thank you. We have revised the sentence into “In general, most of the research on the COVID-19 epidemic has focused on the epidemiological characteristics of confirmed patients”.

Minor comments: Page 21: “Because of limited date” should be “Because of limited data”.

Our response: Thank you. We corrected the mistakes in the sentence.

Our response: Moreover, we checked the full manuscript again and corrected some other grammatical errors. Thank you again for your very detailed and extremely constructive comments, they help improve this paper a lot. We would not have got a high-quality paper without these sincere suggestions. We look forward to hear more about your feedback.

Submitted filename: 20210924-Response Letter.docx

Click here for additional data file.

25 Oct 2021

PONE-D-21-13537R2Contact Network Analysis of Covid-19 in Tourist Areas——Based on 333 Confirmed Cases in ChinaPLOS ONE

Dear Dr. Wang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Overall, you have addressed most of the reviewer's comments. In the revised version, please ensure you address all the remaining issues.

Please submit your revised manuscript by Dec 09 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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We look forward to receiving your revised manuscript.

Kind regards,

Federico Botta

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Main comments

The paper has overall been corrected to a good standard. In particular the section on PageRank, which I identified as lacking in my previous round of comments, has been amended and is now sufficient pending a few comments on grammar which I have noted below.

I have two further notes, which I did not catch on my previous rounds of comments. In the abstract the authors state that there are more male than female patients, whereas Table 1 (pages 7 to 8) and the accompanying text on page 7 show that there are 142 female patients to 140 male patients. The abstract text should be corrected accordingly.

Further, on page 9 the authors say “In the data of Hainan Province, except for January 27, January 28, and February

9, the average age is stable at 40-60 years old, that is, these cases are older people

with a weakened immune system”. This is a fairly sweeping statement. First of all, the authors do not indicate what the mean age of the underlying population is – if the mean population age is in the 40-60 years old range, then this average case age is what one would expect with no underlying age structure to transmission or symptom intensity. If 40-60 is actually older than the baseline population average, then there is still no direct evidence that the high number of cases in older individuals is caused by older individuals having weakened immune systems. This is simply one hypothetical explanation, and the authors should present it as a possibility rather than a certainty. On page 22 the authors then say “At the late stage, cases

are mostly elderly people who have a weak immune system, poor mobility ability, and weak transmission ability”. This should be modified to clarify that these are all risk factors associated with old age, but that not every case will exhibit all of these risk factors (and some may exhibit none of them).

Minor comments

Page 1: “On January 30, 2020, The World Health Organization declared the

epidemic as a Public Health Emergency of International Concern.” – the (newly added) word “as” is not necessary here.

Page 4: “Human social network is heterogeneous” should be “Human social networks are heterogeneous”.

Page 5: “The specific steps of text mining and coding are as following:” should be “The specific steps of text mining and coding are as follows.” Note that since that the sentence is followed by a paragraph break rather than a bulleted list, the authors should replace the semi colon with a full stop.

Page 6: “PageRank measures how likely one is to arrive at a given node by moving randomly around a network, and that it is calculated iteratively” should be “PageRank

measures how likely one is to arrive at a given node by moving randomly around a

network, and is calculated iteratively”.

Page 7: “The specific formula is as following” should be “The specific formula is as follows”.

Page 7: “A component means that, within in it, all

members can be conneted by a well-defined path.” – “connected” is misspelt.

Page 9: “One possible reason for the age gap might be that tourist industry is the pillar industry in Hainan Province” should be “One possible reason for the age gap might be that tourism is the pillar industry in Hainan Province”

Page 9: “As for Yunnan Province, though it is also a tourist attraction, it attracts people at all ages. Besides, Yunnan is one of the biggest labor exporters in China, these returning migrant workers, who are mostly are young, also contributes to the lower the average age of Yunnan.” The phrasing of the first sentence feels slightly informal for a scientific paper. Something like “Tourism in Yunan Province is typically less concentrated towards older age groups” would be a bit more fitting. I do not find the second sentence at all convincing – being a net exporter of migrant labour will make the average age higher, not lower. Returning workers will bring the age profile back up to the pre-migration average but will not increase it past that level. Unless I have seriously misunderstood the reasoning here, this statement should be removed.

Pages 11-12: “For example, cases No. 73 and No. 87 in Hainan Province crossed districts and counties for about 8 times” should just be “For example, cases No. 73 and No. 87 in Hainan Province crossed districts and counties about 8 times”.

Page 19: “maximum” is misspelt as “maximun”.

Page 24: “In such a condition” should be “Under such conditions”.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Joe Hilton

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

31 Oct 2021

Authors’ Response

Dear Reviewer,

Thank you very much for the targeted and constructive comments that you and the reviewer provided on our manuscript. We are very pleased with the detailed modification suggestions and grateful to have another opportunity to correct errors and refine our work. As you will see, we have addressed all the issues raised again, which are highlighted in yellow in the manuscript and detailed below point by point. We look forward to your rechecking the paper and making other valuable suggestions for us.

Thank you once more for your constructive feedback and tireless patience. We hope that the refined manuscript meets your expectations.

Yours sincerely,

Authors of PONE-D-21-13537

Main comments: In the abstract the authors state that there are more male than female patients, whereas Table 1 (pages 7 to 8) and the accompanying text on page 7 show that there are 142 female patients to 140 male patients. The abstract text should be corrected accordingly.

Our response: Thank you very much for your attentiveness. We have compared the data in Table 1, that is, the number of female patients is more than that of male patients, and revised the expression in the abstract.

Main comments: Further, on page 9 the authors say “In the data of Hainan Province, except for January 27, January 28, and February 9, the average age is stable at 40-60 years old, that is, these cases are older people with a weakened immune system”. This is a fairly sweeping statement. First of all, the authors do not indicate what the mean age of the underlying population is – if the mean population age is in the 40-60 years old range, then this average case age is what one would expect with no underlying age structure to transmission or symptom intensity. If 40-60 is actually older than the baseline population average, then there is still no direct evidence that the high number of cases in older individuals is caused by older individuals having weakened immune systems. This is simply one hypothetical explanation, and the authors should present it as a possibility rather than a certainty.

Our response: Thanks for your suggestions. In this section, we increased the proportions of people aged 60 and above among the cases in Yunnan and Hainan Province, and compared them with the proportions of people of this age in these two provinces shown in the seventh National Census of China. By comparing the difference of the proportion of the elderly in the patients and the census data of the whole province, we find that the proportion of the elderly in the patients is higher, which means that the elderly may have a larger proportion in the confirmed patients due to weak immunity and other reasons. Therefore, we revised the statement in the paper to “Tourism in Yunnan Province is typically less concentrated towards older age groups. In Yunnan Province, 21.01% of the cases are aged 60 years and above, which exceeds the proportion of the local population aged 60 years and above in the total population (14.91%, China the seventh National Census). In Hainan Province, 32.72% of the cases are aged 60 years and above, which is greater than the proportion of this age group in the total population (14.65%, China the seventh National Census).”.

Main comments: On page 22 the authors then say “At the late stage, cases

are mostly elderly people who have a weak immune system, poor mobility ability, and weak transmission ability”. This should be modified to clarify that these are all risk factors associated with old age, but that not every case will exhibit all of these risk factors (and some may exhibit none of them).

Our response: Thanks for your suggestions. We have modified to clarify that higher age is likely to cause patients to who have a weak immune system, poor mobility ability, and weak transmission ability.

Minor grammar/typo notes

Minor comments: Page 1: “On January 30, 2020, The World Health Organization declared the epidemic as a Public Health Emergency of International Concern.” – the (newly added) word “as” is not necessary here.

Our response: Thank you very much for your suggestions for corrections. We have deleted the word “as” in the sentence and revised the sentence as “On January 30, 2020, The World Health Organization declared the epidemic a Public Health Emergency of International Concern”.

Minor comments: Page 4: “Human social network is heterogeneous” should be “Human social networks are heterogeneous”.

Our response: Thank you very much for your suggestions for corrections. We have revised the sentence as “Human social networks are heterogeneous”.

Minor comments: Page 5: “The specific steps of text mining and coding are as following:” should be “The specific steps of text mining and coding are as follows.” Note that since that the sentence is followed by a paragraph break rather than a bulleted list, the authors should replace the semi colon with a full stop.

Our response: Thank you very much for your corrections. We have adjusted the sentence as “The specific steps of text mining and coding are as follows”.

Minor comments: Page 6: “PageRank measures how likely one is to arrive at a given node by moving randomly around a network, and that it is calculated iteratively” should be “PageRank measures how likely one is to arrive at a given node by moving randomly around a network, and is calculated iteratively”.

Our response: Thank you. We have corrected the sentence as “PageRank measures how likely one is to arrive at a given node by moving randomly around a network, and is calculated iteratively”.

Minor comments: Page 7: “The specific formula is as following” should be “The specific formula is as follows”.

Our response: Thank you very much for your correction. We have clarified the sentence as “The specific formula is as follows”.

Minor comments: Page 7: “A component means that, within in it, all members can be conneted by a well-defined path.” – “connected” is misspelt.

Our response: Thank you very much. We corrected the misspelt word in the sentence.

Minor comments: Page 9: “One possible reason for the age gap might be that tourist industry is the pillar industry in Hainan Province” should be “One possible reason for the age gap might be that tourism is the pillar industry in Hainan Province”

Our response: Thank you very much for your corrections. We have modified the sentence into “One possible reason for the age gap might be that tourism is the pillar industry in Hainan Province”.

Minor comments: Page 9: “As for Yunnan Province, though it is also a tourist attraction, it attracts people at all ages. Besides, Yunnan is one of the biggest labor exporters in China, these returning migrant workers, who are mostly are young, also contributes to the lower the average age of Yunnan.” The phrasing of the first sentence feels slightly informal for a scientific paper. Something like “Tourism in Yunan Province is typically less concentrated towards older age groups” would be a bit more fitting. I do not find the second sentence at all convincing – being a net exporter of migrant labour will make the average age higher, not lower. Returning workers will bring the age profile back up to the pre-migration average but will not increase it past that level. Unless I have seriously misunderstood the reasoning here, this statement should be removed.

Our response: Thank you. We have revised the sentence into “Tourism in Yunan Province is typically less concentrated towards older age groups”. We also removed the impertinent statement in the paragraph

Minor comments: Pages 11-12: “For example, cases No. 73 and No. 87 in Hainan Province crossed districts and counties for about 8 times” should just be “For example, cases No. 73 and No. 87 in Hainan Province crossed districts and counties about 8 times”.

Our response: Thank you. We corrected the mistakes and revised the sentence into “For example, cases No. 73 and No. 87 in Hainan Province crossed districts and counties about 8 times”.

Minor comments: Page 19: “maximum” is misspelt as “maximun”.

Our response: Thank you. We corrected the mistake in the sentence and spelled the word “maximun” again.

Minor comments: Page 24: “In such a condition” should be “Under such conditions”.

Our response: Thank you. We corrected the mistake and revised the sentence into “Under such conditions”.

Our response: Moreover, we checked the full manuscript again and corrected some other grammatical errors. Thank you again for your very detailed and extremely constructive comments. They help us to improve this paper a lot. We would not have got a high-quality paper without these sincere suggestions. We look forward to your feedback.

Submitted filename: 20211029-Response Letter-R3.docx

Click here for additional data file.

1 Dec 2021

Contact Network Analysis of Covid-19 in Tourist Areas——Based on 333 Confirmed Cases in China

PONE-D-21-13537R3

Dear Dr. Wang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Federico Botta

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed all of my comments to a good standard, and I feel that the manuscript is now ready for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Joe Hilton

3 Dec 2021

PONE-D-21-13537R3

Contact Network Analysis of Covid-19 in Tourist Areas——Based on 333 Confirmed Cases in China

Dear Dr. Wang:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Federico Botta

Academic Editor

PLOS ONE