Laboratory of Data Discovery for Health

6.3k citations
383 papers ·

Impact in

Papers in

Laboratory of Data Discovery for Health

301 papers receiving 4.7k citations

Peers

Laboratory of Data Discovery for Health
Comparison fields: 5 of 165
  • Infectious Diseases 2.5k
  • Modeling and Simulation 620
  • Health 438
  • Neurology 411
  • Epidemiology 816
Replace Guangzhou Chest Hospital with:
Guangzhou Chest Hospital China
Tseung Kwan O Hospital China
Thai Binh University of Medicine and Pharmacy Vietnam
Vietnamese - German Center of Excellence in Medical Research Vietnam
Shenzhen International Travel Health Care Center China
Hainan Center for Disease Control & Prevention China
Center for Tuberculosis Control of Guangdong Province China
Wuhan City Chinese Medicine Hospital China
Wuhan No. 7 Hospital China
Khanh Hoa General Hospital Vietnam
Laboratory of Data Discovery for Health relative to Guangzhou Chest Hospital China Guangzhou Chest Hospital's profile →
Citations per field
00.5×2×4×6×8.8×
Guangzhou Chest Hospital · 1×
Citations per year

Countries citing scholars working at Laboratory of Data Discovery for Health

Since Specialization
Citations

This map shows the geographic impact of research produced by authors working at Laboratory of Data Discovery for Health. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers produced at Laboratory of Data Discovery for Health with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laboratory of Data Discovery for Health more than expected).

Fields of papers published by authors at Laboratory of Data Discovery for Health

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers affiliated with Laboratory of Data Discovery for Health at the time of their publication. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers affiliated with Laboratory of Data Discovery for Health at the time of their publication.

About Laboratory of Data Discovery for Health

In recent decades, authors affiliated with Laboratory of Data Discovery for Health have published 383 papers, which have received a total of 6.3k indexed citations . Scholars at this organization have produced 63 papers in Modeling and Simulation, 134 papers in Infectious Diseases, 29 papers in Health, 6 papers in Applied Microbiology and Biotechnology and 73 papers in Epidemiology on the topics of SARS-CoV-2 and COVID-19 Research (99 papers), COVID-19 epidemiological studies (63 papers), Influenza Virus Research Studies (41 papers), COVID-19 Clinical Research Studies (37 papers), Vaccine Coverage and Hesitancy (27 papers), Respiratory viral infections research (25 papers), SARS-CoV-2 detection and testing (13 papers) and COVID-19 and Mental Health (11 papers). Their work is cited by papers focused on Infectious Diseases (2.5k citations), Modeling and Simulation (620 citations), Health (438 citations), Neurology (411 citations) and Epidemiology (816 citations). Authors at Laboratory of Data Discovery for Health collaborate with scholars in Hong Kong, China and United Kingdom and have published in prestigious journals including The Journal of Infectious Diseases, Nature Communications, Clinical Infectious Diseases, The Lancet Regional Health - Western Pacific and BMC Medicine. Some of Laboratory of Data Discovery for Health's most productive authors include Benjamin John Cowling, Eric Ho Yin Lau, Gabriel Leung, Carlos King Ho Wong, Ian Chi Kei Wong, Esther Wai Yin Chan, Peng Wu, Celine Sze Ling Chui, Eric Yuk Fai Wan and Ivan Chi Ho Au.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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