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PLoS One. 2023; 18(1): e0279879.
Published online 2023 Jan 19. doi: 10.1371/journal.pone.0279879
PMCID: PMC9851530

Epidemiological clustered characteristics of coronavirus disease 2019 (COVID-19) in three phases of transmission in Jilin Province, China

Qinglong Zhao , Writing – original draft , # Yang Zhang , Supervision , # Meina Li , Writing – original draft , # Rui Tian , Methodology , Yifei Zhao , Formal analysis , Bonan Cao , Software , Laishun Yao , Writing – original draft , Xi Sheng , Visualization , and Yan Yu , Writing – review & editing corresponding author , *

Qinglong Zhao

1 School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an Shaanxi, 710061, China

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China

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Yang Zhang

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China

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Meina Li

3 The First Hospital of Jilin University, Changchun, 130021, Jilin Province, People’s Republic of China

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Rui Tian

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China

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Yifei Zhao

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China

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Bonan Cao

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China

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Laishun Yao

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China

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Xi Sheng

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China

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Yan Yu

1 School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an Shaanxi, 710061, China

2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China
3 The First Hospital of Jilin University, Changchun, 130021, Jilin Province, People’s Republic of China
Stanford University School of Medicine, UNITED STATES Gender Male5520195χ 2 = 4.7020.094Female4226240 0-013F = 12.764< 0.055-12910-201115-10920-621225-1641630-1062735-522240-1173045-1382350-924155-733860-513665-334970-104675-434280-121285-209 Occupation Staff and Officers1695χ 2 = 148.660< 0.05Workers619Domestic and non-working2013175Teachers013Retirees144107Farmers10810Diaspora children034Business Services8513Students6128Medical staff4113Other9015Not available4053 Incubation period (median) 1085F = 29.550< 0.05

In the first phase of the outbreak, Changchun had the highest number of cases (22 patients), followed by Siping (10 patients). The second stage of the outbreak occurred mainly in Jilin and included 45 cases. The third phase of the outbreak occurred in Tonghua with 320 cases. The clustered events occurred in six regions of the Jilin Province, as shown in Fig 4 . The two incidents with the highest number of cases involved in the aggregated events were located in Jilin (spreading to Changchun) and Tonghua (spreading to Changchun and Songyuan), with six aggregated events reported in Siping and five aggregated events in Changchun.

Regional distribution of three phases of COVID-19 outbreaks in Jilin Province.

The map depicted in this figure was taken from Datamap ( http://datamaps.github.io/ ).

As the contact time of disseminated cases exposed to the associated cases was difficult to identify, only the incubation period of cases involved in aggregated outbreaks was calculated in this study. Based on the information obtained from the flow survey, the contact time and onset interval of renewed cases along with the source of infection were used to calculate the incubation period. The median incubation periods for the first, second, and third phases of the outbreak were 10 days, 8 days, and 5 days, respectively. There are four types of aggregated events in Jilin Province. Each type of event can involve multiple modes of aggregation, all of which include household contact, staying in public places, attending gatherings, and work; the various types of aggregated events are shown in Table 2 . All the clustered events in Jilin Province involved household contact and five compound contact aggregated events.

Table 2

Types of cluster events.
Cluster types Event Cases Composition ratio (%) Total case composition ratio (%)
Family 14 51 73.69 9.19
Family & Public place 1 3 5.26 0.54
Family & Public place & Gathering 1 6 5.26 1.08
Family & Public place & Gathering & Work 3 495 15.79 89.19
Total 19 555 100.00 100.00

Statistical tests for clustered and non-clustered cases

The viral strains detected in the different phases of the epidemic and the prevention and control policies at that time vary considerably; therefore, the comparison of different types of cases in the same period is meaningful. As all cases in the second and third phases of the epidemic were aggregated epidemic-involved cases, all patients in the first phase were selected for this study and divided into aggregated and non-aggregated epidemic cases for comparison. Statistical methods were used to compare the age, occupation, urban and rural distribution, severity, mode of infection, and method of detection between the 74 patients involved in the 17 aggregated outbreaks and those involved in the non-aggregated outbreaks in the first phase of the outbreak. Results ( Table 3 ) showed that the differences in detection methods between the clustered and non-clustered cases were statistically significant. Active screening was performed in 44 of the 74 cases and two of the 23 non-clustered cases; thus, the aggregated cases were more likely to be detected by active screening than by evaluation in outpatient clinics; this finding indicates that the aggregated cases in the first phase of the outbreak in Jilin Province were more likely to be detected by active screening compared with the non-clustered cases. In addition, the difference in the number of days from diagnosis to discharge between aggregated and non-aggregated cases was statistically significant. The median number of days from diagnosis to discharge was 5.5 days longer in the aggregated cases compared with that in the non-aggregated cases; this result indicated that the number of days from diagnosis to discharge was longer in the aggregated cases in the first phase of the outbreak in Jilin Province compared with that in the non-aggregated cases. The factors other than the test method and time from diagnosis to discharge did not differ significantly between the aggregated and non-aggregated cases.

Table 3

Characteristics analysis of the first phase of the epidemic in Jilin Province.
Characteristic Non-Cluster cases
(n = 23)
Cluster cases (n = 74) Test statistics P value
Gender
Male (n = 55) 14 41 χ 2 = 0.213 0.810
Female (n = 42) 9 33
Occupation
Food and beverage industry (n = 1) 0 1 - -
Officers (n = 16) 2 14
Workers (n = 6) 3 3
Public places attendant (n = 2) 0 2
House-workers and unemployed (n = 20) 7 13
Retirees (n = 14) 3 11
workforce (n = 1) 0 1
Farmers (n = 9) 0 9
Others (n = 6) 0 6
Business services (n = 8) 3 5
Students (n = 6) 3 3
Medical staff (n = 4) 1 3
Unknown (n = 4) 1 3
Urban (n = 83) 21 62 - 0.508 *
Rural (n = 14) 2 12
severity
Asymptomatic cases (n = 4) 1 3 Z = -0.202 0.840
Mild Cases (n = 39) 10 29
Normal Cases (n = 48) 10 38
Severe Cases (n = 5) 2 3
Critical Cases (n = 1) 0 1
Age (n)
0–9 (1) 0 1 - -
10–19 (3) 1 2
20–29 (22) 8 14
30–39 (15) 5 10
40–49 (24) 2 22
50–59 (16) 5 11
60–69 (8) 1 7
70–79 (5) 1 4
80- (3) 0 3
Median (Range) 33 (11,70) 44.5 (7,87)
Infection ways
Imported cases (n = 45) 19 26 - -
Close contact with local cases (n = 8) 0 8
Close contact with provincial cases (n = 43) 4 39
Unknown (n = 1) 0 1
The detected method
Outpatient found (n = 51) 21 30 χ 2 = 18.135 0.000
Active screening (n = 46) 2 44
Case classification
Confirmed cases (n = 93) 22 71 - 1.000 *
Asymptomatic cases (n = 4) 1 3
Days from illness onset to diagnosis
Median (Range) 7 (2,14) 5 (0,13) Z = 1.762 0.78
Days from diagnosis to discharged from hospital time
Median (Range) 11.5 (5,27) 17 (8,28) Z = -1.973 0.048
Days from onset time to discharged from hospital time
Median (Range) 19.5 (13,31) 22 (11,38) t = 0.792 0.431

* = Fisher’s exact probability test.

Statistical tests for differences between source of infection and sequel cases in clustered cases

The local health authorities immediately implemented measures such as city lockdown and travel restrictions at the beginning of the second and third phases of the outbreak; all cases in these two phases were local secondary cases, except one, which was an imported case. Therefore, all 17 cases from the first phase of the epidemic were selected for this study, and the differences between the infectious and sequelae cases in the same time period were compared. Statistical tests were conducted to examine the differences in the distribution of infectious and sequelae cases by sex, occupation, urban and rural distribution, severity, mode of infection, method of detection, time of onset to time of diagnosis, time of diagnosis to time of discharge, and time of onset to time of discharge. Based on the results of the tests, significant differences were observed in the in the mode of infection and method of detection between infectious and sequela cases in the first phase of clustered events ( Table 4 ). Imported cases accounted for 24 of the 30 source cases and two of the 44 sequel cases; this finding indicates that there were more imported cases among the sources of infection than among the sequel cases. Cases detected in outpatient clinics accounted for 19 of the 30 sources of infection and four of the 44 sequel cases, indicating that the source of infection was more likely to be detected in outpatient clinics than the sequel cases. The differences in the other factors between the two populations were not significant.

Table 4

Characteristics of cluster cases.
Characteristic Source of infection(n = 30) Secondary cases (n = 44) Test statistics P value
Gender
Male (n = 41) 20 21 χ 2 = 2.59 0.153
Female (n = 33) 10 23
Occupation
Food and beverage industry (n = 1) 1 0 - -
Officers (n = 14) 8 6
Workers (n = 3) 1 2
Public places attendant (n = 2) 0 2
Houseworkers and unemployed (n = 13) 3 10
Retirees (n = 11) 5 6
workforce (n = 1) 1 0
Farmers (n = 9) 1 8
Others (n = 6) 4 2
Business services (n = 5) 3 2
Students (n = 3) 1 2
Medical staff (n = 3) 1 2
Unknown (n = 3) 1 2
Urban (n = 62) 27 35 - 0.339 *
Rural (n = 12) 3 9
severity
Asymptomatic cases (n = 3) 0 3 - 4.92 *
Mild Cases (n = 29) 13 16
Normal Cases (n = 38) 16 22
Severe Cases (n = 3) 0 3
Critical Cases (n = 1) 1 0
Cases type
Imported cases (n = 45) 24 2 χ 2 = 44.562 0
Non-imported cases (n = 45) 6 42
The detected method
Outpatient found (n = 23) 19 4 χ 2 = 24.501 0
Active screening (n = 51) 11 40
Age (n)
Median (Range) 44.5 (10,77) 44 (7,89) t = 0.045 0.965
Days from illness onset to diagnosis
Median (Range) 5 (0,11) 6 (0,13) Z = -0.181 0.856
Days from diagnosis to discharged from hospital time
Median (Range) 17 (8,28) 18 (8,25) Z = -0.48 0.962
Days from onset time to discharged from hospital time
Median (Range) 21 (11,38) 22.5 (11,32) Z = -0.137 0.891

* = Fisher’s exact probability test.

Statistical tests for the differences between the three phases of the epidemic

To investigate the differences in case information between the different phases of the epidemic, all cases were divided into three phases according to the period in which the cases were collected, and the differences in the baseline information (gender, age, and occupation) and incubation period between the three phases of the epidemic were examined. A significant difference was observed in age, occupation, and incubation period, except for sex, between the three phases of the epidemic ( Table 1 ). A two-by-two comparison of the three phases of the epidemic in terms of age and incubation period was conducted. A significant difference was observed in the proportion of different age groups between the third and first two phases of the epidemic, while no difference was observed in the age groups between the first and second phases. The third phase of the epidemic had the highest proportion of older people aged 50–90 years (62.76%); this result suggests that the third phase of the epidemic had a higher proportion of middle-aged and older people than the first two phases. Significant differences were found in the percentage of incubation periods between the third and first two phases, while no differences were found in the incubation periods between the first and second phases. The mean incubation periods in all three phases were 10 days, 8 days, and 5 days, respectively, indicating that the incubation period of the third phase was shorter than those of the first two phases.

Spatiotemporal analysis

Moran’s I coefficients did not differ significantly between the two epidemic phases ( P >0.05), except for the second phase of epidemics in Jilin ( P < 0.05). The second phase of the epidemic showed a significant spatial variability (Moran’s I < 0, P < 0.05) in Jilin City ( Table 5 ). For global spatial autocorrelation testing, we performed a local spatial autocorrelation analysis of each district and city in Jilin in the second phase of the epidemic and observed an uneven regional distribution of COVID-19 incidence in Jilin City. The high-low cluster area is concentrated in Shulan City. The results are shown in Fig 5 .

Local spatial autocorrelation analysis of the second phase of COVID-19 incidence in Jilin Province.

The map depicted in this figure was taken from Datamap ( http://datamaps.github.io/ ).

Table 5

Global autocorrelation of the incidence of three phases of COVID-19 outbreaks in Jilin Province.
The three phases of the epidemic in Jilin Province Moron’s I Z value P value
The first phase 0.049 0.754 0.221
The second phase -0.301 -1.31 0.045
The third phase -0.119 0.020 0.402

Discussion

COVID-19 has posed great challenges to all health professionals worldwide. China has experienced a large outbreak, and its economy is gradually stabilizing; however, the cumulative number of overseas imported infections is slowly increasing. Except in Wuhan, China, the initial cases were mainly imported cases, and the number of infections eventually increased due to the occurrence of cluster events. During the Spring Festival, China adopted measures such as sealing off the city, prohibiting the entry of foreign populations, closing public places, and extending holidays to reduce the incidence of imported and cluster cases, which achieved good results [ 19 ]. In Beijing, Shanghai, and other provinces and cities in China, the number of cluster cases accounted for the number of confirmed cases (50%–80%) [ 20 , 21 ]. The aggregated cases in Jilin Province suggested that most of the aggregated outbreaks involved family aggregation.

The COVID-19 outbreak in Jilin Province can be divided into three phases according to temporal distribution. The first phase of the outbreak involved 17 aggregated outbreaks, the second phase involved one aggregated outbreak, and the third phase involved one aggregated outbreak. The median incubation periods of the cases in all three phases of the outbreak in Jilin Province were 10 days, 8 days, and 5 days, respectively, with the longest of all cases being 19 days and the shortest being 0 days; that is, the symptoms appeared on the day of exposure. Based on previous studies, the longest incubation period for COVID-19 is 19 days [ 22 ]. As most of the first cases were imported, data on their exposure time were difficult to obtain; therefore, the incubation period of the renewed cases is more representative of the actual situation of COVID-19 in Jilin Province. Previous studies have indicated that the average incubation period of COVID-19 is 5 days [ 7 ]. The differences between the present study and previous studies may be due to the different strains of the transmitted viruses across regions. Results of statistical tests showed that the incubation period of the third stage of the epidemic differs from those of the first two stages, presumably due to the differences in the source of virus, thus leading to the variations in the incubation period of the infected cases [ 23 ].

The third stage of the outbreak was discovered through a flow survey, in which a lecturer who had already been infected with COVID-19 conducted a health promotion class and caused mass transmission of COVID-19 to the audience. Since the outbreak occurred during the winter, the lecture room was not properly ventilated as it was extremely cold outside; hence, most of the people who attended the lecture were infected. In addition, because the topic of the lecture was health related, most of the older adults were interested to listen as they were overly concerned about their health and had sufficient time to attend the event; hence, most of the attendees were unemployed or retired, which explains the difference in age and occupation between the third and first two stages of the epidemic.

This study found that most cases had the ability to transmit to the next generation of cases 2–7 days prior to the onset of symptoms; that is, they were infectious during the incubation period, which was similar to the findings of other studies related to aggregated outbreaks [ 10 , 24 ]. The epidemiological survey data of patients showed that most people during the aggregated outbreak had not been exposed to the infected area and had no contact with symptomatic patients, yet they still had the possibility of being infected; this finding confirmed the hypothesis that some asymptomatic patients also had the ability to spread the infection. Thus, the neglect of the detection and management of asymptomatic infected persons in the early stages of the COVID-19 outbreak resulted in the widespread transmission. At the first sign of the epidemic, the close contacts of the infected population should be identified, and the management and isolation of the closely connected and sub-closely connected populations should be strengthened to effectively limit the spread of the epidemic. The detection methods used between the first and subsequent cases differed. The predominance of active screening as a detection method for sequel cases suggests that active screening can detect patients who may still be in the incubation period and is an effective method of controlling the spread of the disease. Aggregate cases had a higher rate of active screening than non-aggregate cases. Among the aggregated cases, the proportion of active screening was higher among renewed cases than among first cases, indicating that the local health authorities monitored and managed the close contacts in a timely manner. Furthermore, most of the renewed cases were detected through active testing, effectively controlling latent cases and avoiding the spread of the virus in mostly unknown situations. At the same time, significant differences were observed in the length of hospital stay between the aggregated cases and non-aggregated cases. All patients were discharged with a negative nucleic acid test over a period of time, and the aggregated cases were hospitalized for a longer period of time, which indicates that the aggregated outbreaks involved earlier admissions. This laterally proves that the local health department isolated their close contacts in a timely manner, allowing the source of infection to be detected early through active screening. The largest proportion of imported cases was detected in the first phase of the cluster outbreak, and the population size per cluster outbreak was smaller than that in the last two phases of the outbreak. This is due to the fact that during the first phase of the COVID-19 outbreak in Jilin Province, there was an inflow of a large number of cases from outside the province flowed into Jilin Province due to uncertainty and inexperience in the mode of transmission of the disease, and the failure to adopt contact avoidance methods such as city closures and travel restrictions in the first hours of the epidemic. However, when the disease was confirmed to be contagious, the relevant health authorities immediately implemented mandatory quarantine and city closure measures; hence, each cluster of outbreaks only involved a relatively small number of people. The cases in the latter two phases of the outbreak were all related to each other and were found in close and sub-close contacts of the same imported case, suggesting that sealing the city was effective in avoiding the inflow of cases from outside the province. The outbreaks in both phases were found to be caused by gatherings in public places and therefore involved a large number of people; although the outbreaks were effectively controlled at this stage, the focus of prevention and control should shift to the restriction of large gatherings in public places. For imported cases where various conditions permit (e.g., time and money), if you live with your relatives, home isolation is not recommended, or the family members need to take protective measures when they are at home. Infections during gatherings and common exposures occur mainly in places with a high population density and within a short period of time. Therefore, effective measures, such as controlling the flow of people, should be implemented in densely populated areas. Therefore, it is necessary to abolish any type of in-person gatherings [ 25 ]. For example, the conduct of online classes at home significantly reduced the spread of COVID-19. However, as the mobility of people returning to work and classes increases, this also increases the possibility of spreading the infection. The second and third phases of the outbreak in Jilin Province were caused by gatherings in public places under strict control of imported cases; therefore, it is even more important to avoid gatherings in the current epidemic situation to avoid the possibility of outbreaks caused by mass gatherings.

No spatial correlation was found between the differences in COVID-19 incidence rates across urban areas for the other two phases of the epidemic, except for the second phase; this result implies that during the first and third phases of the COVID-19 outbreak, cases showed a random distribution across municipalities and were not regionally clustered, which may be due to the fact that the epidemic had been spreading across municipalities in Jilin Province for several days by the time cases were detected. By contrast, the second phase of the epidemic in Jilin City, Jilin Province, was detected early at the beginning, and prevention and control measures were implemented quickly; thus, few cases spilled over from Shulan City, showing high-low aggregation in Shulan City and implying the risk of spreading the epidemic from Shulan City to the surrounding municipalities. The results of this spatiotemporal analysis are consistent with the epidemiological distribution of this study.

The proportion of imported cases in the first phase of the cluster epidemic was relatively high, while the size of the population per cluster epidemic was smaller than that in the last two phases of the epidemic, which was due to the fact that during the first phase of the COVID-19 epidemic in Jilin Province, there was an inflow of a large number of cases from outside the province into the Jilin Province due to uncertainty and inexperience with the mode of transmission of the disease and the failure to adopt contact avoidance methods such as city closures and travel restrictions in the first hours of the epidemic. However, after confirming that the disease was contagious, the relevant health authorities immediately implemented mandatory quarantine and city closure measures; hence, each cluster of outbreaks only involved a relatively small number of people. The cases in the second and third phases of the outbreak were linked to each other, with cases found in close and sub-close contacts of the same imported case, indicating that the closure of the city was effective in avoiding the inflow of cases from outside the province. The outbreak of the second and third phases of the epidemic was caused by gatherings in public places, thus involving a large number of people; although the outbreak was effectively controlled at this stage, the focus of prevention and control should shift to the avoidance of large gatherings in public places.

Infections and general exposure at gatherings occur mainly in areas with a high population density. Therefore, effective measures, such as crowd control, should still be implemented in densely populated areas. Gatherings must be eliminated [ 25 ]. For example, the conduct of online class at home significantly reduced the spread of COVID-19. However, with the increased movement to and from home and tourist travel during holidays, this made it possible for the outbreak to spread. The second and third phases of the epidemic in Jilin Province were caused by gatherings in public places under the strict control of imported cases. Therefore, in the current epidemic situation, gatherings should be restricted to avoid the occurrence of an epidemic caused by mass gatherings.

Limitation

This study had some limitations. First, the total number of cases involved in the three phases of the outbreak varied considerably and therefore may be biased when performing statistical analyses. Some of the aggregated events involved fewer cases, such as intra-household transmission that only involved two or three people, and therefore are not as convincing compared with the larger aggregated events. Due to the large number of people involved in the third phase of the epidemic, no clear intergenerational relationships (e.g., intergenerational spacing and renewal rates) were presented in the flow survey data, making it difficult to analyze. The number of close contacts involved in this study has been located to the greatest extent possible, but there is a possibility of omission. Second, future studies should measure the spatial stratified heterogeneity (SSH) to further investigate the interregional transmission patterns of these three phases of the epidemic [ 26 , 27 ]. In this study, the lack of detailed case locations due to data quality limitations can lead to problems of spatial applicability when assessing the epidemiological spatial distribution. Second, this study aimed to investigate the methodology of aggregated outbreaks to inform the prevention and control of aggregated outbreaks, which is why SSH was not included. In future studies, we will attempt to measure the SSH and further elucidate the pattern of transmission of COVID-19 among different regions.

Conclusion

Cluster cases comprised the highest component of the total number of cases. Surveillance of outbreaks is of utmost importance. In addition, family gatherings and high traffic areas should be avoided. Simultaneously, as the number of people returning to work and school increases, precautions should be taken to avoid the possibility of secondary outbreaks.

Acknowledgments

The authors would like to express their sincere gratitude to the following people, without whom the study would not have been possible: (1) study participants for providing data and (2) field investigators for collecting the data.

Funding Statement

The author(s) received no specific funding for this work.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

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PONE-D-21-36962Epidemiological clustered characteristics of COVID-19 in three phases transmission in Jilin Province, ChinaPLOS ONE

Dear Dr. Yan,

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Reviewer's Responses to Questions

Comments to the Author 4 Nov 2022 19 Dec 2022

Epidemiological Clustered characteristics of coronavirus disease 2019 (COVID-19) in three phases of transmission in Jilin Province, China

PONE-D-21-36962R1

Dear Dr. Yu,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author 9 Jan 2023

PONE-D-21-36962R1

Epidemiological clustered characteristics of coronavirus disease 2019 (COVID-19) in three phases of transmission in Jilin Province, China

Dear Dr. Yu:

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.

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on behalf of

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