Evidence map›Paper›PMID 33209406›Full record

ArticleJournal of thoracic disease2020

The global COVID-19 pandemic at a crossroads: relevant countermeasures and ways ahead.

Yimin Zhou, Jun Li, Zuguo Chen, Qingsong Luo, Xiangdong Wu, Lingjian Ye, Haiyang Ni, Chunnan Fei

Open access · diamondAbstract read
In one paragraph

Article in Journal of thoracic disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 43% of its field
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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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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

8 authors at 4 institutions in 2 countries.

Yimin ZhouShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Jun LiSchool of International Relations, Sun Yat-sen University, Guangzhou, China.
Zuguo ChenShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Qingsong LuoShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Xiangdong WuShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Lingjian YeShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Haiyang NiTianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin, China.
Chunnan FeiTianjin Center for Disease Control and Prevention, Tianjin, China.
Shenzhen Institutes of Advanced Technology · CNSun Yat-sen University · CNTianjin Centers for Disease Control and Prevention · CNTianjin University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the outbreak of novel coronavirus disease (COVID-19) in Wuhan, China at the beginning of December 2019, there have been over 11,200,000 confirmed cases in the world as of the 3

methodsWe developed a new hybrid model of infectious disease transmission based on Cellular Automata (CA)-configured SEIR to analyse the COVID-19 outbreak and estimate its transmission pattern. A probabilistic contamination network is embedded in the pandemic transmission model to capture the randomness feature of person-to-person spread of the novel virus. We used the improved SEIR model to quantify the population contact state with isolation measures under different continuous time series contact probability via CA. We adjusted the modelling parameters to verify the model performance in accordance to the data from the reports published by the Chinese Center for Disease Control and Prevention. We simulated several scenarios by varying such key parameters as number of isolation rate, average contact times of the population, number of infected people before taking prevention and control measures, medical level and number of imported cases.

resultsIn the baseline model, we identified that the isolation control as the most influencing factor that had the largest impact on decreasing the speed of the reproductive number, accelerating the arrival of the "inflection point" of pandemic prevention and control, and the death rate reduction. We estimated that the probability of people contacts and the number of the onset infected cases before prevention measures also had significant effect on the infection rate reduction with appropriate prevention measures adoption, which partly reflects the impact of timely measure on the severity of the outbreak. We found that imported cases will risk the domestic prevention.

conclusionsOur modelling results clearly indicate that early-stage preventive measures are the most effective way to contain the pandemic spread and a strong interventionist approach needs to be adopted by policymakers vis-à-vis of the highly contagious nature of the COVID-19. Human resources, intensified isolation and confinement as well as special hospital buildings should be prioritised in countries with large number of infections to constrain the global transmission of the virulent infection. To do so, internationally coordinated actions require to be taken to replicate good practices to less infected countries and regions immediately.

Indexed as

COVID-19global spreadnetwork transmissionprevention and control

Identifiers

PMID33209406
PMCPMC7656443
OpenAlexW3088996363

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.