Gavin Leech
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 4
-
- Viral Infections and Outbreaks Research 1
- Co-authors
- Sören Mindermann (4 shared papers)Jan Brauner (4 shared papers)Mrinank Sharma (4 shared papers)Joshua Teperowski Monrad (4 shared papers)Jan Kulveit (3 shared papers)Yarin Gal (3 shared papers)Tomáš Gavenčiak (2 shared papers)Alexander John Norman (2 shared papers)
- Journals
- Scientific Data (1 paper)PLoS Computational Biology (1 paper)Science (1 paper)Proceedings of the National Academy of Sciences (1 paper)Explore Bristol Research (1 paper)
- Partner nations
- United KingdomDenmarkUnited States
In The Last Decade
Gavin Leech
4 papers receiving 710 citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Modeling and Simulation 499
- Infectious Diseases 196
- Health 81
- Economics and Econometrics 226
- Clinical Psychology 142
Countries citing papers authored by Gavin Leech
This map shows the geographic impact of Gavin Leech'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 Gavin Leech with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gavin Leech more than expected).
Fields of papers citing papers by Gavin Leech
This network shows the impact of papers produced by Gavin Leech. 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 Gavin Leech. The network helps show where Gavin Leech may publish in the future.
Co-authors
The 25 scholars most cited alongside Gavin Leech, 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 | 630 |
| 2 | 2022 | 57 | |
| 3 | 2022 | 33 | |
| 4 | 2022 | 8 | |
| 5 | Safety Properties of Inductive Logic Programming | 2021 | 0 |
About Gavin Leech
Gavin Leech is a scholar working on Modeling and Simulation, Infectious Diseases, Clinical Psychology, Economics and Econometrics and Computational Theory and Mathematics, having authored 5 papers that have together received 728 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (4 papers), COVID-19 Pandemic Impacts (2 papers), COVID-19 and Mental Health (2 papers), Infection Control and Ventilation (1 paper), Viral Infections and Outbreaks Research (1 paper), Machine Learning and Algorithms (1 paper), Formal Methods in Verification (1 paper) and Logic, Reasoning, and Knowledge (1 paper). The work is most often cited by research in Modeling and Simulation (499 citations), Infectious Diseases (196 citations), Health (81 citations), Economics and Econometrics (226 citations) and Clinical Psychology (142 citations). Gavin Leech has collaborated with scholars based in United Kingdom, Denmark and United States. Frequent co-authors include Sören Mindermann, Jan Brauner, Mrinank Sharma, Joshua Teperowski Monrad, Jan Kulveit, Yarin Gal, Tomáš Gavenčiak, Alexander John Norman, George Altman and Yee Whye Teh. Their work appears in journals such as Scientific Data, PLoS Computational Biology, Science, Proceedings of the National Academy of Sciences and Explore Bristol Research.
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.