Chon-Fu Lio

685 citations
18 papers · 475 · h-index 10

Impact in

Papers in

Chon-Fu Lio

18 papers receiving 468 citations

Peers

Chon-Fu Lio
Comparison fields: 5 of 95
  • Infectious Diseases 213
  • Modeling and Simulation 26
  • General Dentistry 8
  • Psychiatry and Mental health 56
  • Neurology 48
Replace Antonello Serra with:
Antonello Serra Italy
Selina Greuel Germany
Alicia Ang Singapore
Kathrine Agergård Kaspersen Denmark
Sifat Sharmin Australia
Katharine Harman United Kingdom
Aliaë Mohamed-Hussein Egypt
Luis Enrique Colunga‐Lozano Mexico
Ahmet Nalbant Türkiye
Songklot Aeumjaturapat Thailand
Chon-Fu Lio relative to Antonello Serra Italy Antonello Serra's profile →
Citations per field
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Antonello Serra · 1×
Citations per year

Countries citing papers authored by Chon-Fu Lio

Since Specialization
Citations

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

Fields of papers citing papers by Chon-Fu Lio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chon-Fu Lio. 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 Chon-Fu Lio. The network helps show where Chon-Fu Lio may publish in the future.

Co-authors

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

All Works

18 of 18 papers shown
#Work
1 2020254
2 202144
3 201934
4 201731
5 201920
6 201918
7 201814
8 202111
9 202210
10 202010
11 20209
12 20179
13 20203
14 20202
15 20182
16 20202
17 20161
18
[Permanent environmental disinfection techniques in hospital settings with infectious risk].
20221

About Chon-Fu Lio

Chon-Fu Lio is a scholar working on Infectious Diseases, Psychiatry and Mental health, Epidemiology, Modeling and Simulation and Surgery, having authored 18 papers that have together received 475 indexed citations. Recurring topics across this work include Fibromyalgia and Chronic Fatigue Syndrome Research (3 papers), COVID-19 epidemiological studies (2 papers), Burn Injury Management and Outcomes (2 papers), COVID-19 Clinical Research Studies (2 papers), Pneumothorax, Barotrauma, Emphysema (1 paper), Neurological Complications and Syndromes (1 paper), Streptococcal Infections and Treatments (1 paper) and SARS-CoV-2 detection and testing (1 paper). The work is most often cited by research in Infectious Diseases (213 citations), Modeling and Simulation (26 citations), General Dentistry (8 citations), Psychiatry and Mental health (56 citations) and Neurology (48 citations). Chon-Fu Lio has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Hou Hon Cheong, Chin Ion Lei, Iek Long Lo, Yakun Tian, Shin‐Yi Tsai, Chien-Feng Kuo, Tse-Yen Yang, Lan Yao, Chi Chen and Weicheng Yao. Their work appears in journals such as PeerJ, Journal of Translational Medicine, International Journal of Biological Sciences, Medicine and BMC Public Health.

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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