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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Tracking COVID-19 using taste and smell loss Google searches is not a reliable strategy
Kim Asseo, Fabrizio Fierro, Yuli Slavutsky, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 23

Showing 23 citing articles:

Validating deep learning inference during chest X-ray classification for COVID-19 screening
Robbie Sadre, Baskaran Sundaram, Sharmila Majumdar, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 42
Search Engine Use for Health-Related Purposes: Behavioral Data on Online Health Information-Seeking in Germany
Marko Bachl, Elena Link, Frank Mangold, et al.
Health Communication (2024) Vol. 39, Iss. 8, pp. 1651-1664
Open Access | Times Cited: 6
The rise of infodemiology and infoveillance during COVID-19 crisis
S Springer, Michael Zieger, Artur Strzelecki
One Health (2021) Vol. 13, pp. 100288-100288
Open Access | Times Cited: 34
Evaluation of Twitter data for an emerging crisis: an application to the first wave of COVID-19 in the UK
Iris Cheng, Johannes Heyl, N. N. Lad, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 28
Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review
Tobias Saegner, Donatas Austys
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 19, pp. 12394-12394
Open Access | Times Cited: 20
Google Trends as a predictive tool in the era of COVID-19: a scoping review
Berlanty A. Zayed, Ahmed M. Talaia, Mohamed A Gaaboobah, et al.
Postgraduate Medical Journal (2023) Vol. 99, Iss. 1175, pp. 962-975
Closed Access | Times Cited: 9
Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic
Jean-Paul R. Soucy, Shelby L. Sturrock, Isha Berry, et al.
PeerJ (2024) Vol. 12, pp. e17455-e17455
Open Access | Times Cited: 2
Using Google Health Trends to investigate COVID-19 incidence in Africa
Alexander Fulk, Daniel Romero-Álvarez, Qays Abu-Saymeh, et al.
PLoS ONE (2022) Vol. 17, Iss. 6, pp. e0269573-e0269573
Open Access | Times Cited: 10
The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
Halit Çınarka, Mehmet Atilla Uysal, Atilla Çifter, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 13
COVID-19 pandemic in Saint Petersburg, Russia: Combining population-based serological study and surveillance data
Антон Барчук, Dmitriy Skougarevskiy, Alexei Kouprianov, et al.
PLoS ONE (2022) Vol. 17, Iss. 6, pp. e0266945-e0266945
Open Access | Times Cited: 9
Harnessing Food Product Reviews for Personalizing Sweetness Levels
Kim Asseo, Masha Y. Niv
Foods (2022) Vol. 11, Iss. 13, pp. 1872-1872
Open Access | Times Cited: 6
COVID-19 pandemic in Saint Petersburg, Russia: combining surveillance and population-based serological study data in May, 2020–April, 2021
Антон Барчук, Dmitriy Skougarevskiy, Alexei Kouprianov, et al.
medRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 6
Search Engine Use for Health-Related Purposes: Behavioral Data on Online Health Information-Seeking in Germany
Marko Bachl, Elena Link, Frank Mangold, et al.
(2023)
Open Access | Times Cited: 2
State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data
Alex Turvy
JMIR Formative Research (2022) Vol. 6, Iss. 12, pp. e40825-e40825
Open Access | Times Cited: 3
Predicting mobility using limited data during early stages of a pandemic
Michael T. Lash, S. Sajeesh, Özgür M. Araz
Journal of Business Research (2023) Vol. 157, pp. 113413-113413
Open Access | Times Cited: 1
Searching for the Peak Google Trends and the COVID-19 Outbreak in Italy
Paolo Brunori, Giuliano Resce, Laura Serlenga
Research Square (Research Square) (2021)
Open Access | Times Cited: 3
Analyzing Citizens’ and Health Care Professionals’ Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs
Milla Mukka, Samuli Pesälä, C Hammer, et al.
JMIR Public Health and Surveillance (2021) Vol. 7, Iss. 12, pp. e31961-e31961
Open Access | Times Cited: 3
Using Google Health Trends to investigate COVID-19 incidence in Africa
Alexander Fulk, Daniel Romero-Álvarez, Qays Abu-Saymeh, et al.
medRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 2
THE SEQUELAE OF COVID-19
Janailton Carlos L Pinheiro Muniz
Health and Society (2023) Vol. 3, Iss. 01, pp. 262-275
Open Access
COVID-19 Symptom-Related Google Searches and State-Level COVID-19 Case Incidence: Investigating Political Beliefs as a Predictor of Variation in the Temporal Symptom and Case Relationship (Preprint)
Alex Turvy
(2022)
Closed Access
Searching for the peak: Google Trends and the first COVID-19 wave in Italy
Laura Serlenga, Giuliano Resce, Paolo Brunori
International Journal of Computational Economics and Econometrics (2022) Vol. 12, Iss. 4, pp. 1-1
Open Access
Searching for the peak: Google Trends and the first COVID-19 wave in Italy
Paolo Brunori, Giuliano Resce, Laura Serlenga
International Journal of Computational Economics and Econometrics (2022) Vol. 12, Iss. 4, pp. 445-445
Open Access
Analyzing Citizens’ and Health Care Professionals’ Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs (Preprint)
Milla Mukka, Samuli Pesälä, C Hammer, et al.
(2021)
Open Access
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