Liling Chaw

1k citations
31 papers · 790 · h-index 14

Impact in

Papers in

Liling Chaw

29 papers receiving 771 citations

Peers

Liling Chaw
Comparison fields: 5 of 80
  • Modeling and Simulation 348
  • Infectious Diseases 291
  • Applied Microbiology and Biotechnology 31
  • General Dentistry 2
  • Epidemiology 82
Replace Toon Braeye with:
Toon Braeye Belgium
José Oñate Colombia
Nicholas P B Thomas United Kingdom
Xiaopeng Qi China
Debjit Chakraborty India
Madeleine Oliver United Kingdom
Zachary D. Schneider United States
Kelly Walblay United States
Helen S. Bond Hong Kong
Audrey Giraud‐Gatineau France
Liling Chaw relative to Toon Braeye Belgium Toon Braeye's profile →
Citations per field
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Toon Braeye · 1×
Citations per year

Countries citing papers authored by Liling Chaw

Since Specialization
Citations

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

Fields of papers citing papers by Liling Chaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liling Chaw. 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 Liling Chaw. The network helps show where Liling Chaw may publish in the future.

Co-authors

The 25 scholars most cited alongside Liling Chaw, 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 Liling Chaw Line = papers co-authored together Liling Chaw links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020169
2 2021156
3 202074
4 202040
5 201637
6 202036
7 201634
8 202034
9 202028
10 202025
11 202024
12 202219
13 202118
14 202214
15 202213
16 202012
17 202210
18 20198
19 20207
20 20216

About Liling Chaw

Liling Chaw is a scholar working on Modeling and Simulation, Infectious Diseases, Public Health, Environmental and Occupational Health, Pharmacology and Epidemiology, having authored 31 papers that have together received 790 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (8 papers), Tuberculosis Research and Epidemiology (5 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Cervical Cancer and HPV Research (2 papers), Respiratory viral infections research (2 papers), Influenza Virus Research Studies (2 papers), Mosquito-borne diseases and control (2 papers) and Pharmacy and Medical Practices (2 papers). The work is most often cited by research in Modeling and Simulation (348 citations), Infectious Diseases (291 citations), Applied Microbiology and Biotechnology (31 citations), General Dentistry (2 citations) and Epidemiology (82 citations). Liling Chaw has collaborated with scholars based in Brunei, United Kingdom and Malaysia. Frequent co-authors include Justin Wong, Lin Naing, Mohammad Fathi Alikhan, Wee Chian Koh, Matthew Griffith, Roberta Pastore, Long Chiau Ming, Zahid Hussain, F. Merlin Franco and Taro Kamigaki. Their work appears in journals such as PLoS ONE, International Journal of Environmental Research and Public Health, Influenza and Other Respiratory Viruses, International Journal of Infectious Diseases and Clinical Infectious Diseases.

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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