旅途中的牛腩 · Songbird: Ultimate ...· 1 周前 · |
怕老婆的跑步机 · kotlin/java生成xml-阿里云开发者社区· 4 天前 · |
爱看书的火锅 · 【33】t-SNE原理介绍与对手写数字MNI ...· 1 月前 · |
爱跑步的香瓜 · java下使用openssl生成公私钥-腾讯 ...· 1 月前 · |
追风的花生 · Android使用EditText+List ...· 6 月前 · |
细心的泡面 · abap 如何得到内表中不重复的记录。· 12 月前 · |
淡定的跑步鞋
11 月前 |
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
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
2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China
2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China
2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China
2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China
2 Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, Jilin Province, People’s Republic of China
1 School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an Shaanxi, 710061, China
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.
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.
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 |
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.
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.
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.
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.
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.
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 .
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.
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.
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.
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.
The author(s) received no specific funding for this work.
All relevant data are within the manuscript and its Supporting Information files.
PONE-D-21-36962Epidemiological clustered characteristics of COVID-19 in three phases transmission in Jilin Province, ChinaPLOS ONE
Dear Dr. Yan,
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. Please revise.
Please submit your revised manuscript by Aug 14 2022 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 gro.solp@enosolp . 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.
If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .
We look forward to receiving your revised manuscript.
Kind regards,
Robert Jeenchen Chen, MD, MPH
Academic Editor
PLOS ONE
Journal requirements:
When submitting your revision, we need you to address these additional requirements.
1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at
https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and
2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions .
In your revised cover letter, please address the following prompts:
a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.
b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories .
We will update your Data Availability statement on your behalf to reflect the information you provide.
3. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ .
4. We note that [Figure 4] in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted
maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright .
We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:
a. You may seek permission from the original copyright holder of Figure 4 to publish the content specifically under the CC BY 4.0 license.
We recommend that you contact the original copyright holder with the Content Permission Form ( http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf ) and the following text:
“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 ( http://creativecommons.org/licenses/by/4.0/ ). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”
Please upload the completed Content Permission Form or other proof of granted permissions as an ""Other"" file with your submission.
In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”
b. If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.
The following resources for replacing copyrighted map figures may be helpful:
USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/
The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/
Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html
NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/
Landsat: http://landsat.visibleearth.nasa.gov/
USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#
Natural Earth (public domain): http://www.naturalearthdata.com/
[Note: HTML markup is below. Please do not edit.]
Reviewers' comments:
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,
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 gro.solp@gnillibrohtua .
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 gro.solp@sserpeno .
Kind regards,
Robert Jeenchen Chen, MD, MPH
Academic Editor
PLOS ONE
Additional Editor Comments (optional):
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.
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 gro.solp@sserpeno .
If we can help with anything else, please email us at gro.solp@enosolp .
Thank you for submitting your work to PLOS ONE and supporting open access.
Kind regards,
PLOS ONE Editorial Office Staff
on behalf of
Dr. Robert Jeenchen Chen
Academic Editor
PLOS ONE