Daozhou Gao
- Modeling and Simulation top 0.05%
- Infectious Diseases top 1%
- Public Health, Environmental and Occupational Health top 1%
- Economics and Econometrics top 1%
- Genetics top 5%
- Co-authors
- Daihai HeYijun LouShi ZhaoMaggie Haitian WangSalihu S. MusaLin YangWeiming WangShigui Ruan
- Topics
- COVID-19 epidemiological studies (47 papers)Mathematical and Theoretical Epidemiology and Ecology Models (27 papers)Evolution and Genetic Dynamics (22 papers)
- Cited by
- Modeling and SimulationInfectious DiseasesPublic Health, Environmental and Occupational Health
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEScientific Reports
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Daozhou Gao
63 papers receiving 3.7k citations
Hit Papers
Peers
Comparison fields: 5 of 162
- Modeling and Simulation 2.5k
- Infectious Diseases 1.5k
- Public Health, Environmental and Occupational Health 1.4k
- Economics and Econometrics 736
- Genetics 437
Countries citing papers authored by Daozhou Gao
This map shows the geographic impact of Daozhou Gao'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 Daozhou Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daozhou Gao more than expected).
Fields of papers citing papers by Daozhou Gao
This network shows the impact of papers produced by Daozhou Gao. 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 Daozhou Gao. The network helps show where Daozhou Gao may publish in the future.
Co-authorship network of co-authors of Daozhou Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Daozhou Gao. A scholar is included among the top collaborators of Daozhou Gao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daozhou Gao. Daozhou Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 7 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 7 | |
| 8 | 16 | |
| 9 | 15 | |
| 10 | 21 | |
| 11 | 1 | |
| 12 | 302 | |
| 13 | 66 | |
| 14 | 42 | |
| 15 | 58 | |
| 16 | Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreakbreakdown → | 1175 |
| 17 | 22 | |
| 18 | 32 | |
| 19 | 84 | |
| 20 | 6 |
About Daozhou Gao
Daozhou Gao is a scholar working on Modeling and Simulation, Infectious Diseases and Public Health, Environmental and Occupational Health, having authored 66 papers that have together received 3.9k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (47 papers), Mathematical and Theoretical Epidemiology and Ecology Models (27 papers) and Evolution and Genetic Dynamics (22 papers). The work is most often cited by research in Modeling and Simulation (2.5k citations), Infectious Diseases (1.5k citations) and Public Health, Environmental and Occupational Health (1.4k citations). Daozhou Gao has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Daihai He, Yijun Lou, Shi Zhao, Maggie Haitian Wang, Salihu S. Musa, Lin Yang, Weiming Wang, Shigui Ruan, Jinjun Ran and Guangpu Yang. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.
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.