Liling Chaw

28 papers receiving 544 citations

Peers

Liling Chaw
Comparison fields: 5 of 94
  • Modeling and Simulation 143
  • Applied Microbiology and Biotechnology 32
  • Infectious Diseases 160
  • Epidemiology 109
  • Health 20
Replace Oscar Kallay with:
Oscar Kallay Belgium
Prisca Olabisi Adejumo Nigeria
Semeeh Akinwale Omoleke Nigeria
Ambrose Agweyu Kenya
Shirin Aliabadi United Kingdom
Abdullah Algwizani Saudi Arabia
Naser Nasiri Iran
Aishat Jumoke Alaran United Kingdom
Nelson Ashinedu Ukor United Kingdom
Samah Awad Egypt
Liling Chaw relative to Oscar Kallay Belgium Oscar Kallay's profile →
Citations per field
00.5×4.3×
Oscar Kallay · 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 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020143
2 202053
3 202040
4 201637
5 202035
6 201630
7 202029
8 202025
9 202023
10 202019
11 202019
12 202118
13 202215
14 202211
15 202211
16 20229
17 20197
18 20226
19 20205
20 20214

About Liling Chaw

Liling Chaw is a scholar working on Infectious Diseases, Modeling and Simulation, Epidemiology, Clinical Psychology and Oncology, having authored 29 papers that have together received 556 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (7 papers), Tuberculosis Research and Epidemiology (4 papers), COVID-19 and Mental Health (3 papers), Viral Infections and Outbreaks Research (3 papers), Child Nutrition and Water Access (2 papers), Cervical Cancer and HPV Research (2 papers), Global Maternal and Child Health (2 papers) and SARS-CoV-2 and COVID-19 Research (2 papers). The work is most often cited by research in Modeling and Simulation (143 citations), Applied Microbiology and Biotechnology (32 citations), Infectious Diseases (160 citations), Epidemiology (109 citations) and Health (20 citations). Liling Chaw has collaborated with scholars based in Brunei, Malaysia and Philippines. Frequent co-authors include Justin Wong, Lin Naing, Mohammad Fathi Alikhan, Wee Chian Koh, Matthew Griffith, Roberta Pastore, Long Chiau Ming, F. Merlin Franco, Taro Kamigaki and Hitoshi Oshitani. Their work appears in journals such as PLoS ONE, International Journal of Environmental Research and Public Health, Journal of Ethnobiology and Ethnomedicine, BMC Public Health and BMJ Open Respiratory Research.

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.

Explore authors with similar magnitude of impact