Ming Guo
Impact in
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies
- General Dentistry top 2%
Papers in
-
- Tuberculosis Research and Epidemiology 13
- SARS-CoV-2 and COVID-19 Research 4
- COVID-19 Clinical Research Studies 4
- Epidemiology 11
- Mycobacterium research and diagnosis 8
- Pneumocystis jirovecii pneumonia detection and treatment 3
- Co-authors
- Ke LanYu ChenYingle LiuYuan LiuKin‐Fai HoZhi NingNirmal Kumar GaliLi Sun
- Journals
- Emerging Microbes & Infections (3 papers)Tuberculosis (2 papers)Frontiers in Microbiology (1 paper)Infection and Drug Resistance (1 paper)Vaccine (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Ming Guo
27 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Modeling and Simulation 353
- General Dentistry 129
- Infectious Diseases 1.2k
- Pulmonary and Respiratory Medicine 1.0k
- Neurology 314
Countries citing papers authored by Ming Guo
This map shows the geographic impact of Ming Guo'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 Ming Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Guo more than expected).
Fields of papers citing papers by Ming Guo
This network shows the impact of papers produced by Ming Guo. 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 Ming Guo. The network helps show where Ming Guo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ming Guo, 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 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 11 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 16 | |
| 7 | 2021 | 71 | |
| 8 | Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients Hit paper breakdown → | 2020 | 782 |
| 9 | Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals Hit paper breakdown → | 2020 | 1389 |
| 10 | 2019 | 5 | |
| 11 | 2018 | 4 | |
| 12 | 2017 | 10 | |
| 13 | 2016 | 40 | |
| 14 | 2016 | 1 | |
| 15 | 2016 | 31 | |
| 16 | 2015 | 90 | |
| 17 | 2014 | 13 | |
| 18 | 2012 | 77 | |
| 19 | 2011 | 24 | |
| 20 | 2010 | 17 |
About Ming Guo
Ming Guo is a scholar working on Infectious Diseases, Epidemiology, Animal Science and Zoology, Immunology and Modeling and Simulation, having authored 33 papers that have together received 2.8k indexed citations. Recurring topics across this work include Tuberculosis Research and Epidemiology (13 papers), Mycobacterium research and diagnosis (8 papers), Infectious Diseases and Tuberculosis (5 papers), SARS-CoV-2 and COVID-19 Research (4 papers), COVID-19 Clinical Research Studies (4 papers), Pneumocystis jirovecii pneumonia detection and treatment (3 papers), Animal Virus Infections Studies (3 papers) and Antenna Design and Analysis (2 papers). The work is most often cited by research in Modeling and Simulation (353 citations), General Dentistry (129 citations), Infectious Diseases (1.2k citations), Pulmonary and Respiratory Medicine (1.0k citations) and Neurology (314 citations). Ming Guo has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Ke Lan, Yu Chen, Yingle Liu, Yuan Liu, Kin‐Fai Ho, Zhi Ning, Nirmal Kumar Gali, Li Sun, Yusen Duan and Haidong Kan. Their work appears in journals such as Emerging Microbes & Infections, Tuberculosis, Frontiers in Microbiology, Infection and Drug Resistance and Vaccine.
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.