Evidence map›Paper›PMID 36267598›Full record

ArticleInternational journal of biomedical and clinical analysis2022

Indirect Impacts of COVID-19 on the Environment: A Global Review.

Anita K Patlolla, Zavier Smith, Paul Tchounwou

Abstract read
In one paragraph

Article in International journal of biomedical and clinical analysis, 2022. 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

3 authors.

Anita K PatlollaRCMI-Center for Environmental Health, CSET, Jackson State University, Jackson, MS, USA.
Zavier SmithEnvironmental Science Ph.D., Program, Jackson State University, Jackson, MS, USA.
Paul TchounwouRCMI-Center for Environmental Health, CSET, Jackson State University, Jackson, MS, USA.

Funding

Strengthening the Biomedical Data Science Training Program at Jackson State UniversityU54MD015929 · NIMHD · JACKSON STATE UNIVERSITY · PI PAYTON, MARINELLE · 2020 to 2024
$12.1M
NIMHD NIH HHS U54 MD015929
6 · The paper itself

Abstract

Coronavirus (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus, which has plagued the Earth for the past two years and brought much controversy along with it. This report aims to analyze how the Covid-19 pandemic has had indirect effects on the environment. The onset of the pandemic has not only caused havoc disrupting routine average and businesses, but also claimed at least five million lives worldwide. This prompted the governments and the World Health Organization (WHO) to formulate measures to contain the transmission and the impact of the disease on the populations. Quarantine measures, movement restrictions, lockdowns and curfews, and travel bans are some of the most effective response methods that have helped the world contain the pandemic's spread. The adopted measures have had an indirect impact on the environment, opening the global community to numerous opportunities and threats. This report provides a critical analysis of how the Covid-19 pandemic has had indirect effects on the environment, examining how the response and containment measures have affected the environment. It focuses on air quality, water demand and quality, climate change, afforestation and deforestation, wildlife resurgence, littering, traffic congestion, noise reduction and changed human activities. It explores how the Covid-19 containment measures have had an environmental impact with a keen interest in the earlier areas.

Indexed as

Covid-19EnvironmentLockdownPandemicQuarantine

Identifiers

PMID36267598
PMCPMC9580545

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.