Yin Mo
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- Antibiotic Use and Resistance 12
- Molecular Medicine top 5%
- Antibiotic Resistance in Bacteria 11
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies 7
- Infectious Diseases top 10%
- SARS-CoV-2 and COVID-19 Research 4
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- Infection Control and Ventilation 7
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- COVID-19 and Mental Health 6
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- COVID-19 and healthcare impacts 5
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- Pneumonia and Respiratory Infections 4
- Co-authors
- Dale FisherBen S. CooperPaul Anantharajah TambyahCherry LimQin Xiang NgWee Song YeoNicholas J. WhiteJames A Watson
- Journals
- Journal of Hospital Infection (3 papers)PLoS Medicine (2 papers)Epidemiology and Infection (2 papers)
- Partner nations
- SingaporeUnited KingdomThailand
In The Last Decade
Yin Mo
33 papers receiving 531 citations
Peers
Comparison fields: 5 of 105
- Applied Microbiology and Biotechnology 78
- Molecular Medicine 101
- Modeling and Simulation 58
- Infectious Diseases 206
- Critical Care and Intensive Care Medicine 34
Countries citing papers authored by Yin Mo
This map shows the geographic impact of Yin Mo'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 Yin Mo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yin Mo more than expected).
Fields of papers citing papers by Yin Mo
This network shows the impact of papers produced by Yin Mo. 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 Yin Mo. The network helps show where Yin Mo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yin Mo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 4 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 12 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 4 | |
| 8 | 2023 | 26 | |
| 9 | 2022 | 2 | |
| 10 | 2021 | 8 | |
| 11 | 2021 | 8 | |
| 12 | 2021 | 11 | |
| 13 | 2020 | 15 | |
| 14 | 2019 | 0 | |
| 15 | 2019 | 11 | |
| 16 | 2019 | 15 | |
| 17 | 2018 | 8 | |
| 18 | 2018 | 26 | |
| 19 | 2017 | 42 | |
| 20 | 2012 | 20 |
About Yin Mo
Yin Mo is a scholar working on Applied Microbiology and Biotechnology, Molecular Medicine and Modeling and Simulation, having authored 40 papers that have together received 546 indexed citations. Recurring topics across this work include Antibiotic Use and Resistance (12 papers), Antibiotic Resistance in Bacteria (11 papers), COVID-19 epidemiological studies (7 papers), Infection Control and Ventilation (7 papers), COVID-19 and Mental Health (6 papers), COVID-19 and healthcare impacts (5 papers), SARS-CoV-2 and COVID-19 Research (4 papers) and Pneumonia and Respiratory Infections (4 papers). The work is most often cited by research in Applied Microbiology and Biotechnology (78 citations), Molecular Medicine (101 citations) and Modeling and Simulation (58 citations). Yin Mo has collaborated with scholars based in Singapore, United Kingdom and Thailand. Frequent co-authors include Dale Fisher, Ben S. Cooper, Paul Anantharajah Tambyah, Cherry Lim, Qin Xiang Ng, Wee Song Yeo, Nicholas J. White, James A Watson, Michelle Lee Zhi Qing De Deyn and Nandini Venkatanarayanan. Their work appears in journals such as Journal of Hospital Infection, PLoS Medicine, Epidemiology and Infection, BMC Infectious Diseases and BMJ Open.
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.