Evidence map›Paper›PMID 40477158›Full record

ArticleFundamental research2021

Estimating the time interval between transmission generations and the presymptomatic period by contact tracing surveillance data from 31 provinces in the mainland of China.

Zhongxing Ding, Kai Wang, Mingwang Shen, Kai Wang, Shi Zhao, Wenyu Song, Rui Li, Zhongjie Li, Liping Wang, Ganzhu Feng and 13 more

Abstract read
In one paragraph

Article in Fundamental research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Zhongxing DingDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Kai WangJiangsu Provincial Key Laboratory of Geriatrics, Department of Geriatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing 211166, China.
Mingwang ShenSchool of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
Kai WangDepartment of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China.
Shi ZhaoJC School of Public Health and Primary Care, Chinese University of Hong Kong, 999077, Hong Kong, China.
Wenyu SongDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Rui LiSchool of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
Zhongjie LiDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Liping WangDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Ganzhu FengThe Second Affiliated Hospital of Nanjing Medical University, Nanjing 211166, China.
Zhiliang HuThe Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing 210003, China.
Hongxia WeiThe Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing 210003, China.
Yanni XiaoSchool of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
Changjun BaoInstitute for Prevention and Control of Acute Infectious Diseases, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China.
Jianli HuInstitute for Prevention and Control of Acute Infectious Diseases, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China.
Liguo ZhuInstitute for Prevention and Control of Acute Infectious Diseases, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China.
Yong LiDepartment of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xufeng ChenDepartment of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yi YinDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Weiming WangSchool of Mathematics Science, Huaiyin Normal University, Huaian 223300, China.
Yongli CaiSchool of Mathematics Science, Huaiyin Normal University, Huaian 223300, China.
Zhihang PengDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Hongbing ShenDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global pandemic of 2019 coronavirus disease (COVID-19) is a great assault to public health. Presymptomatic transmission cannot be controlled with measures designed for symptomatic persons, such as isolation. This study aimed to estimate the interval of the transmission generation (TG) and the presymptomatic period of COVID-19, and compare the fitting effects of TG and serial interval (SI) based on the SEIHR model incorporating the surveillance data of 3453 cases in 31 provinces. These data were allocated into three distributions and the value of AIC presented that the Weibull distribution fitted well. The mean of TG was 5.2 days (95% CI: 4.6-5.8). The mean of the presymptomatic period was 2.4 days (95% CI: 1.5-3.2). The dynamic model using TG as the generation time performed well. Eight provinces exhibited a basic reproduction number from 2.16 to 3.14. Measures should be taken to control presymptomatic transmission in the COVID-19 pandemic.

Indexed as

COVID-19IntervalPresymptomatic transmissionReproduction numberTransmission generation

Identifiers

PMID40477158
PMCPMC7985842

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.