Evidence map›Paper›PMID 39035783›Full record

ArticleInfectious Disease Modelling2024

Confounding amplifies the effect of environmental factors on COVID-19.

Zihan Hao, Shujuan Hu, Jianping Huang, Jiaxuan Hu, Zhen Zhang, Han Li, Wei Yan

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Zihan HaoCollege of Atmospheric Sciences, Lanzhou University, Lanzhoum, 730000, China.
Shujuan HuCollege of Atmospheric Sciences, Lanzhou University, Lanzhoum, 730000, China.
Jianping HuangCollaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China.
Jiaxuan HuCollege of Atmospheric Sciences, Lanzhou University, Lanzhoum, 730000, China.
Zhen ZhangCollege of Atmospheric Sciences, Lanzhou University, Lanzhoum, 730000, China.
Han LiCollaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China.
Wei YanCollaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

Indexed as

Causal analysisCOVID-19Double machine learningEnvironmental factors

Identifiers

PMID39035783
PMCPMC11260012

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.