Ensheng Dong
- Modeling and Simulation top 0.5%
- Economics and Econometrics top 5%
- Epidemiology
- Transportation top 5%
- Infectious Diseases top 10%
- Co-authors
- Lauren GardnerHongru DuHamada S. BadrMaximilian MarshallMarietta M. SquireAaron J. KatzJeremy RatcliffSahotra Sarkar
- Topics
- Data-Driven Disease Surveillance (6 papers)COVID-19 epidemiological studies (5 papers)Vaccine Coverage and Hesitancy (5 papers)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Ensheng Dong
8 papers receiving 823 citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Modeling and Simulation 501
- Economics and Econometrics 224
- Epidemiology 215
- Transportation 158
- Infectious Diseases 152
Countries citing papers authored by Ensheng Dong
This map shows the geographic impact of Ensheng Dong'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 Ensheng Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ensheng Dong more than expected).
Fields of papers citing papers by Ensheng Dong
This network shows the impact of papers produced by Ensheng Dong. 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 Ensheng Dong. The network helps show where Ensheng Dong may publish in the future.
Co-authorship network of co-authors of Ensheng Dong
This figure shows the co-authorship network connecting the top 25 collaborators of Ensheng Dong. A scholar is included among the top collaborators of Ensheng Dong 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 Ensheng Dong. Ensheng Dong 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 | 0 | |
| 3 | 0 | |
| 4 | Trends in County-Level MMR Vaccination Coverage in Children in the United Statesbreakdown → | 14 |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 15 | |
| 8 | 14 | |
| 9 | The Johns Hopkins University Center for Systems Science and Engineering COVID-19 Dashboard: data collection process, challenges faced, and lessons learnedbreakdown → | 131 |
| 10 | 52 | |
| 11 | Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling studybreakdown → | 577 |
| 12 | 35 |
About Ensheng Dong
Ensheng Dong is a scholar working on Modeling and Simulation, Health and Microbiology, having authored 12 papers that have together received 841 indexed citations. Recurring topics across this work include Data-Driven Disease Surveillance (6 papers), COVID-19 epidemiological studies (5 papers) and Vaccine Coverage and Hesitancy (5 papers). The work is most often cited by research in Modeling and Simulation (501 citations), Transportation (158 citations) and Health (107 citations). Ensheng Dong has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Lauren Gardner, Hongru Du, Hamada S. Badr, Maximilian Marshall, Marietta M. Squire, Aaron J. Katz, Jeremy Ratcliff, Sahotra Sarkar, Kamran Khan and David Zhang. Their work appears in journals such as JAMA, Scientific Reports and The Lancet Infectious Diseases.
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.