Evidence map›Paper›PMID 32886309›Full record

ArticleEnvironmental science and pollution research international2021

Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence.

Marco Mele, Cosimo Magazzino

Abstract read
In one paragraph

Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Air pollution and respiratory infections: the past, present, and future.Toxicological sciences : an official journal of the Society of Toxicology · 2023
    Review
  5. Article
  6. Article
  7. Determination of the human impact on the drop in NOEnvironmental pollution (Barking, Essex : 1987) · 2022
    Article
  8. Review
  9. Asian dust storms result in a higher risk of the silicosis hospital admissions.Journal of environmental health science & engineering · 2022
    Article
  10. Article
  11. Article
  12. The impact of lockdown on nitrogen dioxide (NOEnvironmental science and pollution research international · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

2 authors.

Marco MeleDepartment of Political Sciences, University of Teramo, Teramo, Italy. mmele@unite.it.ORCID http://orcid.org/0000-0002-1477-1071
Cosimo MagazzinoDepartment of Political Sciences, University of Roma Tre, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution. Then, a D2C algorithm on proportion-based causality is applied, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis is that a predetermined pollution concentration, caused by economic growth, could foster COVID-19 by making the respiratory system more susceptible to infection. We use data (from January 29 to May 18, 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verify a ML causal link between PM

Indexed as

Air PollutantsAir PollutionCOVID-19CitiesEconomic DevelopmentHumansIndiaMachine LearningParticulate MatterSARS-CoV-2Air PollutantsParticulate MatterCOVID-19Economic growthIndiaMachine learningPollutionTime series

Identifiers

PMID32886309
PMCPMC7472938

What OpenQuestion holds

Textmetadata
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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.