Oliver Eales
Impact in
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
- Infectious Diseases top 5%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
Papers in
-
- COVID-19 epidemiological studies 16
-
- COVID-19 Clinical Research Studies 10
- SARS-CoV-2 and COVID-19 Research 6
- SARS-CoV-2 detection and testing 3
- Co-authors
- Helen Ward (13 shared papers)Graham Cooke (13 shared papers)Ara Darzi (13 shared papers)Paul Elliott (13 shared papers)Steven Riley (13 shared papers)William Barclay (12 shared papers)Haowei Wang (13 shared papers)Deborah Ashby (12 shared papers)
- Journals
- Science (3 papers)Epidemics (2 papers)PLoS Computational Biology (2 papers)PLoS Medicine (1 paper)Nature Communications (1 paper)
- Partner nations
- United KingdomAustraliaNetherlands
In The Last Decade
Oliver Eales
21 papers receiving 399 citations
Peers
Comparison fields: 5 of 71
- Modeling and Simulation 195
- Infectious Diseases 258
- Health 38
- Epidemiology 89
- Neurology 36
Countries citing papers authored by Oliver Eales
This map shows the geographic impact of Oliver Eales'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 Oliver Eales with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oliver Eales more than expected).
Fields of papers citing papers by Oliver Eales
This network shows the impact of papers produced by Oliver Eales. 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 Oliver Eales. The network helps show where Oliver Eales may publish in the future.
Co-authors
The 25 scholars most cited alongside Oliver Eales, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 80 | |
| 2 | 2022 | 67 | |
| 3 | 2021 | 65 | |
| 4 | 2021 | 52 | |
| 5 | 2022 | 24 | |
| 6 | 2022 | 17 | |
| 7 | 2023 | 17 | |
| 8 | 2024 | 16 | |
| 9 | 2023 | 16 | |
| 10 | 2022 | 14 | |
| 11 | 2022 | 10 | |
| 12 | 2020 | 8 | |
| 13 | 2019 | 7 | |
| 14 | 2024 | 3 | |
| 15 | 2020 | 2 | |
| 16 | 2024 | 2 | |
| 17 | 2024 | 2 | |
| 18 | 2024 | 2 | |
| 19 | 2025 | 1 | |
| 20 | 2020 | 1 |
About Oliver Eales
Oliver Eales is a scholar working on Modeling and Simulation, Infectious Diseases, Epidemiology, Clinical Psychology and Economics and Econometrics, having authored 22 papers that have together received 407 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (16 papers), COVID-19 Clinical Research Studies (10 papers), SARS-CoV-2 and COVID-19 Research (6 papers), Influenza Virus Research Studies (6 papers), Data-Driven Disease Surveillance (4 papers), COVID-19 Pandemic Impacts (3 papers), SARS-CoV-2 detection and testing (3 papers) and COVID-19 and Mental Health (3 papers). The work is most often cited by research in Modeling and Simulation (195 citations), Infectious Diseases (258 citations), Health (38 citations), Epidemiology (89 citations) and Neurology (36 citations). Oliver Eales has collaborated with scholars based in United Kingdom, Australia and Netherlands. Frequent co-authors include Helen Ward, Graham Cooke, Ara Darzi, Paul Elliott, Steven Riley, William Barclay, Haowei Wang, Deborah Ashby, Christl A. Donnelly and Christina Atchison. Their work appears in journals such as Science, Epidemics, PLoS Computational Biology, PLoS Medicine and Nature Communications.
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.