Evidence map›Paper›PMID 39311105›Full record

ArticleJournal of research in health sciences2024

The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.

Nadia Mohammadi Dashtaki, Alireza Mirahmadizadeh, Mohammad Fararouei, Reza Mohammadi Dashtaki, Mohammad Hoseini, Mohammad Reza Nayeb

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In one paragraph

Article in Journal of research in health sciences, 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

6 authors.

Nadia Mohammadi DashtakiStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.ORCID 0000-0003-1252-5157
Alireza MirahmadizadehNon-communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Mohammad FararoueiAIDS/HIV Research Center, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran.ORCID 0000-0001-9362-328X
Reza Mohammadi DashtakiDepartment of Chemistry, Isfahan University of Technology, Isfahan, Iran.
Mohammad HoseiniDepartment of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Mohammad Reza NayebStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronavirus disease 19 (COVID-19) using machine learning (ML) techniques in Shiraz, Iran.

methodsIn this ecological research, three main ML techniques, including decision trees, random forest, and extreme gradient boosting (XGBoost), have been applied to correlate meteorological parameters and air pollutants with infection transmission, hospitalization, and death due to COVID-19 from 1 October 2020 to 1 March 2022. These parameters and pollutants included particulate matter (PM2), sulfur dioxide (SO

resultsBased on the three ML techniques, NO

conclusionOur findings suggested that epidemics caused by COVID-19 and (possibly) similarly viral transmitted infections, including flu, air pollutants, and meteorological parameters, can be used to predict their burden on the community and health system. In addition, meteorological and air quality data should be included in preventive measures.

Indexed as

Air PollutantsAir PollutionCOVID-19Machine LearningParticulate MatterSARS-CoV-2Carbon MonoxideHospitalizationHumansIranMeteorological ConceptsNitrogen DioxideOzoneSeverity of Illness IndexSulfur DioxideAir PollutantsCarbon MonoxideNitrogen DioxideOzoneParticulate MatterSulfur DioxideAir pollutantsCOVID-19Machine learningMeteorological factorsTime factors

Identifiers

PMID39311105
PMCPMC11380733

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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.