Evidence map›Paper›PMID 33344871›Full record

ArticleGeoHealth2020

Impact of Extreme Hot Climate on COVID-19 Outbreak in India.

Keerthi Sasikumar, Debashis Nath, Reshmita Nath, Wen Chen

Abstract read
In one paragraph

Article in GeoHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  6. Article
  7. Article
  8. Article
  9. Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly.International journal of environmental research and public health · 2022
    Review
  10. Interactions between climate and COVID-19.The Lancet. Planetary health · 2022
    Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. 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

4 authors.

Keerthi SasikumarCenter for Monsoon System Research, Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China.
Debashis NathSchool of Atmospheric Sciences Sun Yat-sen University Zhuhai China.ORCID https://orcid.org/0000-0001-9299-2729
Reshmita NathSchool of Atmospheric Sciences Sun Yat-sen University Zhuhai China.
Wen ChenCenter for Monsoon System Research, Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China.ORCID https://orcid.org/0000-0001-9327-9079

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus Disease 2019 (COVID-19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID-19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID-19 epicenters. Daily COVID-19 count has strong covariability with local temperature, which accounts approximately 65-85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID-19 cases are clustered at temperature and humidity ranging within 27-32°C and 25-45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID-19 growth at the initial phase. The results are highly significant for predicting future COVID-19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO

Indexed as

climate change and extremesCOVID‐19Indiapopulation densitytemperature and humidity

Identifiers

PMID33344871
PMCPMC7742201

What OpenQuestion holds

Textmetadata
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.