Evidence map›Paper›PMID 35627661›Full record

ArticleInternational journal of environmental research and public health2022

Modeling and Preliminary Analysis of the Impact of Meteorological Conditions on the COVID-19 Epidemic.

Chenglong Sun, Liya Chao, Haiyan Li, Zengyun Hu, Hehui Zheng, Qingxiang Li

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. 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. 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

6 authors.

Chenglong SunSchool of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China.
Liya ChaoSchool of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China.
Haiyan LiSchool of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China.
Zengyun HuXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.ORCID 0000-0002-4259-9141
Hehui ZhengNational Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
Qingxiang LiSchool of Atmospheric Sciences and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China.ORCID 0000-0002-1424-4108

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the COVID-19 epidemic outbreak at the end of 2019, many studies regarding the impact of meteorological factors on the attack have been carried out, and inconsistent conclusions have been reached, indicating the issue's complexity. To more accurately identify the effects and patterns of meteorological factors on the epidemic, we used a combination of logistic regression (LgR) and partial least squares regression (PLSR) modeling to investigate the possible effects of common meteorological factors, including air temperature, relative humidity, wind speed, and surface pressure, on the transmission of the COVID-19 epidemic. Our analysis shows that: (1) Different countries and regions show spatial heterogeneity in the number of diagnosed patients of the epidemic, but this can be roughly classified into three types: "continuous growth", "staged shock", and "finished"; (2) Air temperature is the most significant meteorological factor influencing the transmission of the COVID-19 epidemic. Except for a few areas, regional air temperature changes and the transmission of the epidemic show a significant positive correlation, i.e., an increase in air temperature is conducive to the spread of the epidemic; (3) In different countries and regions studied, wind speed, relative humidity, and surface pressure show inconsistent correlation (and significance) with the number of diagnosed cases but show some regularity.

Indexed as

COVID-19EpidemicsHumansMeteorological ConceptsMeteorologyWindCOVID-19 epidemicLgR modelmeteorological driversmodelingPLSR model

Identifiers

PMID35627661
PMCPMC9140896

What OpenQuestion holds

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LicenceCC BY
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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.