Evidence map›Paper›PMID 32088333›Full record

ArticleMicrobes and infection2020

Lessons learned from the 2019-nCoV epidemic on prevention of future infectious diseases.

Xingchen Pan, David M Ojcius, Tianyue Gao, Zhongsheng Li, Chunhua Pan, Chungen Pan

Abstract read
In one paragraph

Article in Microbes and infection, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
–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

46 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Virtual Infection Prevention and Control in Low- and Middle-Income Countries.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Review
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

6 authors.

Xingchen PanDepartment of Human Resources, Shanghai University of Finance and Economics, Shanghai, China.
David M OjciusDepartment of Biomedical Sciences, University of the Pacific, School of Dentistry, San Francisco, USA.
Tianyue GaoEarl Haig Secondary School, North York, Ontario, Canada.
Zhongsheng LiGuangdong Haid Institute of Animal Husbandry & Veterinary, Guangdong Provincial Key Laboratory of Research on the Technology of Pig-breeding and Pig-disease Prevention, Haid Research Institute, Guangdong Haid Group Co., Ltd, Guangzhou, China.
Chunhua PanThe 1st Ward of the Medical Department, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China. Electronic address: chhpan@163.com.
Chungen PanGuangdong Haid Institute of Animal Husbandry & Veterinary, Guangdong Provincial Key Laboratory of Research on the Technology of Pig-breeding and Pig-disease Prevention, Haid Research Institute, Guangdong Haid Group Co., Ltd, Guangzhou, China. Electronic address: chungenp@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Only a month after the outbreak of pneumonia caused by 2019-nCoV, more than forty-thousand people were infected. This put enormous pressure on the Chinese government, medical healthcare provider, and the general public, but also made the international community deeply nervous. On the 25th day after the outbreak, the Chinese government implemented strict traffic restrictions on the area where the 2019-nCoV had originated-Hubei province, whose capital city is Wuhan. Ten days later, the rate of increase of cases in Hubei showed a significant difference (p = 0.0001) compared with the total rate of increase in other provinces of China. These preliminary data suggest the effectiveness of a traffic restriction policy for this pandemic thus far. At the same time, solid financial support and improved research ability, along with network communication technology, also greatly facilitated the application of epidemic prevention measures. These measures were motivated by the need to provide effective treatment of patients, and involved consultation with three major groups in policy formulation-public health experts, the government, and the general public. It was also aided by media and information technology, as well as international cooperation. This experience will provide China and other countries with valuable lessons for quickly coordinating and coping with future public health emergencies.

Indexed as

AnimalsBetacoronavirusChinaChiropteraCommunicationCoronavirus InfectionsCOVID-19EpidemicsGovernmentHumansPneumonia, ViralPublic HealthSARS-CoV-2TravelZoonoses2019-nCoVGovernmentPublic health emergencyTraffic restriction

Identifiers

PMID32088333
PMCPMC7102576

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.