Evidence map›Paper›PMID 36567860›Full record

ArticleSustainable cities and society2021

Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts.

Xuan Tang, Zexuan Li, Xian Hu, Zefeng Xu, Linxi Peng

Abstract read
In one paragraph

Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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.

Xuan TangSchool of Management, Guangzhou University Guangzhou 510006, China.
Zexuan LiSchool of Electronics and Communication Engineering, Guangzhou University Guangzhou 510006, China.
Xian HuSchool of Mechanical and Electrical Engineering, Guangzhou University Guangzhou 510006, China.
Zefeng XuSchool of Mechanical and Electrical Engineering, Guangzhou University Guangzhou 510006, China.
Linxi PengData Recovery Key Laboratory of Sichuan Province, Neijiang Normal University, Sichuan, 641100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to improve the prediction accuracy of COVID-19 and strengthen the economic management and control, a self-correcting intelligent pandemic prediction model is proposed. The research shows that: (1) The pandemic, as a major social factor, has a great impact on the consumption expenditure level of various industries, and directly affects the public consumption expenditure level in different periods for example the spend_all in California decreased by 37.7%; (2) The economic losses caused by the increasingly serious pandemic period far less than the economic losses caused by the panic in the early stage of the pandemic, and the reason is the government's strong guarantee policies stimulate economic recovery. For example, the spend_all in California has increased from -37.7% to about -18%; (3) The proposed model improves the prediction accuracy of economic trend, and the government can make prediction according to the early warning economic prediction, which provides reference for the economic management control at the micro level of enterprises and the macro level of the nation; (4) The dual strategies of self correcting prediction and pandemic control realize the overall design of real-time control and performance optimization of economic process, and provide reference for the overall recovery of the economy.

Indexed as

COVID-19Economic controlsPrediction

Identifiers

PMID36567860
PMCPMC9760181

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.