Jan Kulveit
Impact in
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies
- Infectious Diseases top 10%
- SARS-CoV-2 and COVID-19 Research
- Viral Infections and Outbreaks Research
Papers in
-
- COVID-19 epidemiological studies 3
-
- COVID-19 Clinical Research Studies 1
- SARS-CoV-2 and COVID-19 Research 1
- Viral Infections and Outbreaks Research 1
- Co-authors
- Mrinank Sharma (3 shared papers)Sören Mindermann (3 shared papers)Joshua Teperowski Monrad (3 shared papers)Tomáš Gavenčiak (3 shared papers)Gavin Leech (3 shared papers)Jan Brauner (3 shared papers)George Altman (2 shared papers)Leonid Chindelevitch (1 shared paper)
- Journals
- Scientific Data (1 paper)Science (1 paper)Proceedings of the National Academy of Sciences (1 paper)PLoS Computational Biology (1 paper)The Journal of Chemical Physics (1 paper)
- Partner nations
- United KingdomCzechiaAustralia
In The Last Decade
Jan Kulveit
5 papers receiving 677 citations
Jan Kulveit's Hit Papers
Peers
Comparison fields: 5 of 84
- Modeling and Simulation 412
- Infectious Diseases 145
- Health 58
- Economics and Econometrics 172
- Clinical Psychology 101
Countries citing papers authored by Jan Kulveit
This map shows the geographic impact of Jan Kulveit'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 Jan Kulveit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Kulveit more than expected).
Fields of papers citing papers by Jan Kulveit
This network shows the impact of papers produced by Jan Kulveit. 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 Jan Kulveit. The network helps show where Jan Kulveit may publish in the future.
Co-authors
The 25 scholars most cited alongside Jan Kulveit, 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 | Inferring the effectiveness of government interventions against COVID-19 Hit paper breakdown → | 2020 | 643 |
| 2 | 2022 | 36 | |
| 3 | 2022 | 8 | |
| 4 | 2025 | 5 | |
| 5 | 2011 | 1 |
About Jan Kulveit
Jan Kulveit is a scholar working on Modeling and Simulation, Infectious Diseases, Economics and Econometrics, Clinical Psychology and Sociology and Political Science, having authored 5 papers that have together received 693 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (3 papers), COVID-19 Pandemic Impacts (2 papers), Authorship Attribution and Profiling (1 paper), COVID-19 Clinical Research Studies (1 paper), Spectroscopy and Quantum Chemical Studies (1 paper), nanoparticles nucleation surface interactions (1 paper), SARS-CoV-2 and COVID-19 Research (1 paper) and Viral Infections and Outbreaks Research (1 paper). The work is most often cited by research in Modeling and Simulation (412 citations), Infectious Diseases (145 citations), Health (58 citations), Economics and Econometrics (172 citations) and Clinical Psychology (101 citations). Jan Kulveit has collaborated with scholars based in United Kingdom, Czechia and Australia. Frequent co-authors include Mrinank Sharma, Sören Mindermann, Joshua Teperowski Monrad, Tomáš Gavenčiak, Gavin Leech, Jan Brauner, George Altman, Leonid Chindelevitch, Yee Whye Teh and Yarin Gal. Their work appears in journals such as Scientific Data, Science, Proceedings of the National Academy of Sciences, PLoS Computational Biology and The Journal of Chemical Physics.
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.