Evidence map›Paper›PMID 32288416›Full record

ArticleJournal of Shanghai Jiaotong University (science)2020

CIRD-F: Spread and Influence of COVID-19 in China.

Lingyun Zhou, Kaiwei Wu, Hanzhi Liu, Yuanning Gao, Xiaofeng Gao

Abstract read
In one paragraph

Article in Journal of Shanghai Jiaotong University (science), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

5 authors.

Lingyun Zhou *1Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, 200240 China.
Kaiwei Wu *2SJTU-ParisTech Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai, 200240 China.
Hanzhi Liu *3School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
Yuanning Gao3School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
Xiaofeng Gao3School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has been spreading rapidly in China and the Chinese government took a series of policies to control the epidemic. Therefore, it will be helpful to predict the tendency of the epidemic and analyze the influence of official policies. Existing models for prediction, such as cabin models and individual-based models, are either oversimplified or too meticulous, and the influence of the epidemic was studied much more than that of official policies. To predict the epidemic tendency, we consider four groups of people, and establish a propagation dynamics model. We also create a negative feedback to quantify the public vigilance to the epidemic. We evaluate the tendency of epidemic in Hubei and China except Hubei separately to predict the situation of the whole country. Experiments show that the epidemic will terminate around 17 March 2020 and the final number of cumulative infections will be about 78 191 (prediction interval, 74 872 to 82 474). By changing the parameters of the model accordingly, we demonstrate the control effect of the policies of the government on the epidemic situation, which can reduce about 68% possible infections. At the same time, we use the capital asset pricing model with dummy variable to evaluate the effects of the epidemic and official policies on the revenue of multiple industries.

Indexed as

capital asset pricing modelcoronavirus disease 2019 (COVID-19)dummy variableepidemic prediction modelnegative feedback

Identifiers

PMID32288416
PMCPMC7137851

What OpenQuestion holds

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Registered trials

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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.