Qifang Bi
- Infectious Diseases top 0.5%
- Modeling and Simulation top 0.05%
- Economics and Econometrics top 1%
- Public Health, Environmental and Occupational Health top 2%
- Epidemiology top 5%
- Co-authors
- Justin LesslerKyra H. GrantzNicholas G ReichAndrew S. AzmanStephen A. LauerForrest K. JonesQulu ZhengHannah R. Meredith
- Topics
- COVID-19 epidemiological studies (8 papers)Mosquito-borne diseases and control (5 papers)Viral Infections and Vectors (4 papers)
- Partner nations
- United StatesThailandChina
In The Last Decade
Qifang Bi
12 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 186
- Infectious Diseases 2.2k
- Modeling and Simulation 2.0k
- Economics and Econometrics 743
- Public Health, Environmental and Occupational Health 638
- Epidemiology 554
Countries citing papers authored by Qifang Bi
This map shows the geographic impact of Qifang Bi'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 Qifang Bi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qifang Bi more than expected).
Fields of papers citing papers by Qifang Bi
This network shows the impact of papers produced by Qifang Bi. 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 Qifang Bi. The network helps show where Qifang Bi may publish in the future.
Co-authorship network of co-authors of Qifang Bi
This figure shows the co-authorship network connecting the top 25 collaborators of Qifang Bi. A scholar is included among the top collaborators of Qifang Bi 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 Qifang Bi. Qifang Bi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 23 | |
| 3 | 55 | |
| 4 | 3 | |
| 5 | The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Applicationbreakdown → | 3773 |
| 6 | What is Machine Learning? A Primer for the Epidemiologistbreakdown → | 342 |
| 7 | 50 | |
| 8 | 10 | |
| 9 | 17 | |
| 10 | 16 | |
| 11 | 72 | |
| 12 | Assessing the global threat from Zika virusbreakdown → | 297 |
| 13 | 8 |
About Qifang Bi
Qifang Bi is a scholar working on Modeling and Simulation, Endocrinology and Infectious Diseases, having authored 13 papers that have together received 4.7k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (8 papers), Mosquito-borne diseases and control (5 papers) and Viral Infections and Vectors (4 papers). The work is most often cited by research in Modeling and Simulation (2.0k citations), Infectious Diseases (2.2k citations) and General Dentistry (90 citations). Qifang Bi has collaborated with scholars based in United States, Thailand and China. Frequent co-authors include Justin Lessler, Kyra H. Grantz, Nicholas G Reich, Andrew S. Azman, Stephen A. Lauer, Forrest K. Jones, Qulu Zheng, Hannah R. Meredith, Joshua Kaminsky and Katherine E Goodman. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Annals of Internal Medicine.
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.