Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 339

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

No association of COVID-19 transmission with temperature or UV radiation in Chinese cities

Open access
Yao, Ye, Pan, Jinhua, Liu, Zhixi, Meng, Xia, Wang, Weidong, Kan, Haidong, Wang, Weibing·2020·European Respiratory Journal

Abstract

The coronavirus disease 2019 (COVID-19) epidemic, which was first reported in December 2019 in Wuhan, China, has caused 80 904 confirmed cases as of 9 March 2020, with 28 673 cases being reported outside of China. It has been declared a pandemic by the World Health Organization (WHO), has exhibited human-to-human transmissibility and has spread rapidly across countries [1]. Although the Chinese government has taken various measures to control city-to-city transmission ( e.g. shutting down cities, extending holidays) and many other countries have implemented measures (such as airport screening and testing patients who have reported symptoms), the number of cases is still increasing quickly throughout the world. There is no association of COVID-19 transmission with temperature or UV radiation in Chinese cities

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1183/13993003.00517-2020

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Cited by

See all 339

Metadata

Authors
Yao, Y., Pan, J., Liu, Z., Meng, X., Wang, W., Kan, H., & Wang, W.
Venue
European Respiratory Journal
Published
2020-04-08
Volume
55
Issue
5
Pages
2000517
ISSN
  • 0903-1936 (ISSN-L)
  • 1399-3003
OpenAlex
W3015210835
Cited by
339
References
10
FWCI
12.60
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: No association of COVID-19 transmission with temperature or UV radiation in Chinese cities. https://app.cochat.ai/papers/10.1183/13993003.00517-2020
The paper
Yao, Y., Pan, J., Liu, Z., Meng, X., Wang, W., Kan, H., & Wang, W. (2020). No association of COVID-19 transmission with temperature or UV radiation in Chinese cities. European Respiratory Journal. https://doi.org/10.1183/13993003.00517-2020

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.