Suok Kai Chew

4.5k citations
36 papers · 3.3k indexed · 1 hit paper · h-index 27

Suok Kai Chew

36 papers receiving 3.1k citations

Hit Papers

Transmission Dynamics and Control of Severe Acute Respira...1.0k20032026201020182505007501000

Peers

Suok Kai Chew
Comparison fields: 5 of 147
  • Modeling and Simulation 864
  • Infectious Diseases 945
  • Endocrinology, Diabetes and Metabolism 509
  • Epidemiology 1.0k
  • Public Health, Environmental and Occupational Health 725
Replace Edith Lau with:
Edith Lau Hong Kong
Andrew Roddam United Kingdom
Ross Harris United Kingdom
Gary K. Dowse Australia
Susan P. Fisher‐Hoch United States
Susan S. Chiu Hong Kong
Marco Massari Italy
Thomas Hanslik France
Su‐Vui Lo Hong Kong
Richard A. Forshee United States
Suok Kai Chew relative to Edith Lau Hong Kong Edith Lau's profile →
Citations per field
00.5×1.5×2.1×
Edith Lau · 1×
Citations per year

Countries citing papers authored by Suok Kai Chew

Since Specialization
Citations

This map shows the geographic impact of Suok Kai Chew's research. 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 Suok Kai Chew with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suok Kai Chew more than expected).

Fields of papers citing papers by Suok Kai Chew

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Suok Kai Chew. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Suok Kai Chew. The network helps show where Suok Kai Chew may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Suok Kai Chew, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Suok Kai Chew Line = papers co-authored together Suok Kai Chew links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201212
2 200964
3 200826
4 200875
5 200728
6 200790
7 200633
8 2006144
9
Online Health Education on SARS to University Students during the SARS Outbreak.
20056
10 200554
11 200512
12 200536
13 200535
14 200438
15 2004100
16 200354
17 2003122
18 2001136
19 200128
20 1999352

About Suok Kai Chew

Suok Kai Chew is a scholar working on Modeling and Simulation, Endocrinology, Diabetes and Metabolism and Infectious Diseases, having authored 36 papers that have together received 3.3k indexed citations. Recurring topics across this work include Diabetes, Cardiovascular Risks, and Lipoproteins (7 papers), Genetic Associations and Epidemiology (5 papers), COVID-19 epidemiological studies (4 papers), Liver Disease Diagnosis and Treatment (4 papers), Adipose Tissue and Metabolism (4 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Research in Cotton Cultivation (2 papers) and Influenza Virus Research Studies (2 papers). The work is most often cited by research in Modeling and Simulation (864 citations), Infectious Diseases (945 citations) and Endocrinology, Diabetes and Metabolism (509 citations). Suok Kai Chew has collaborated with scholars based in Singapore, United States and Spain. Frequent co-authors include Stefan Ma, E Shyong Tai, Lyn James, James M. Robins, David N. Fisman, Ted Cohen, Megan Murray, Chorh Chuan Tan, Marc Lipsitch and Matthew H. Samore.

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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