Oliver Eales

3.0k total citations
22 papers, 407 citations indexed

About

Oliver Eales is a scholar working on Modeling and Simulation, Infectious Diseases and Epidemiology. According to data from OpenAlex, Oliver Eales has authored 22 papers receiving a total of 407 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Modeling and Simulation, 12 papers in Infectious Diseases and 8 papers in Epidemiology. Recurrent topics in Oliver Eales's work include COVID-19 epidemiological studies (16 papers), COVID-19 Clinical Research Studies (10 papers) and SARS-CoV-2 and COVID-19 Research (6 papers). Oliver Eales is often cited by papers focused on COVID-19 epidemiological studies (16 papers), COVID-19 Clinical Research Studies (10 papers) and SARS-CoV-2 and COVID-19 Research (6 papers). Oliver Eales collaborates with scholars based in United Kingdom, Australia and Netherlands. Oliver Eales's co-authors include Ara Darzi, Graham Cooke, Paul Elliott, Helen Ward, Steven Riley, Christl A. Donnelly, William Barclay, Deborah Ashby, Haowei Wang and Christina Atchison and has published in prestigious journals such as Science, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Oliver Eales

21 papers receiving 399 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Oliver Eales United Kingdom 11 258 195 89 44 38 22 407
Vernon J. M. Lee Singapore 8 224 0.9× 106 0.5× 92 1.0× 32 0.7× 35 0.9× 12 358
Haowei Wang United Kingdom 10 219 0.8× 172 0.9× 68 0.8× 34 0.8× 41 1.1× 21 336
Jessica E. Stockdale Canada 7 186 0.7× 231 1.2× 66 0.7× 42 1.0× 20 0.5× 15 364
Yifei Guo China 7 302 1.2× 134 0.7× 95 1.1× 43 1.0× 26 0.7× 20 491
Asad Zaidi United Kingdom 6 478 1.9× 172 0.9× 62 0.7× 50 1.1× 77 2.0× 13 588
Ali Nizar Latif Qatar 11 364 1.4× 87 0.4× 60 0.7× 36 0.8× 69 1.8× 23 437
Zihao Guo Hong Kong 10 160 0.6× 136 0.7× 69 0.8× 27 0.6× 31 0.8× 41 298
Jonggul Lee South Korea 10 278 1.1× 281 1.4× 75 0.8× 67 1.5× 21 0.6× 16 570
Shu Yang China 8 111 0.4× 136 0.7× 51 0.6× 18 0.4× 27 0.7× 17 321
Liu Runqing China 2 190 0.7× 92 0.5× 44 0.5× 37 0.8× 23 0.6× 4 296

Countries citing papers authored by Oliver Eales

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Oliver Eales

This figure shows the co-authorship network connecting the top 25 collaborators of Oliver Eales. A scholar is included among the top collaborators of Oliver Eales 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 Oliver Eales. Oliver Eales is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Eales, Oliver, David J. Price, Tianxiao Hao, et al.. (2025). Temporal trends in test-seeking behaviour during the COVID-19 pandemic. SHILAP Revista de lepidopterología. 2(1). 1 indexed citations
2.
Eales, Oliver, Freya M. Shearer, & James M. McCaw. (2025). How immunity shapes the long-term dynamics of influenza H3N2. PLoS Computational Biology. 21(3). e1012893–e1012893.
3.
Eales, Oliver & Steven Riley. (2024). Differences between the true reproduction number and the apparent reproduction number of an epidemic time series. Epidemics. 46. 100742–100742. 2 indexed citations
4.
Eales, Oliver, James M. McCaw, & Freya M. Shearer. (2024). Challenges in the case-based surveillance of infectious diseases. Royal Society Open Science. 11(8). 240202–240202. 2 indexed citations
5.
Shearer, Freya M., Martyn Kirk, Oliver Eales, et al.. (2024). Opportunities to strengthen respiratory virus surveillance systems in Australia: lessons learned from the COVID-19 response. Communicable Diseases Intelligence. 48. 3 indexed citations
6.
Eales, Oliver, James M. McCaw, & Freya M. Shearer. (2024). Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement. Influenza and Other Respiratory Viruses. 18(12). e70050–e70050. 2 indexed citations
7.
Eales, Oliver, Michael J. Plank, Benjamin J. Cowling, et al.. (2024). Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic. Emerging infectious diseases. 30(2). 16 indexed citations
8.
Eales, Oliver, David Haw, Haowei Wang, et al.. (2023). Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England. PLoS Biology. 21(5). e3002118–e3002118. 17 indexed citations
9.
Elliott, Paul, Oliver Eales, Nicholas Steyn, et al.. (2022). Twin peaks: The Omicron SARS-CoV-2 BA.1 and BA.2 epidemics in England. Science. 376(6600). eabq4411–eabq4411. 67 indexed citations
10.
Eales, Oliver, Kylie E. C. Ainslie, Caroline E. Walters, et al.. (2022). Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics. 40. 100604–100604. 17 indexed citations
11.
Eales, Oliver, Leonardo de Oliveira Martins, Andrew J. Page, et al.. (2022). Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England. Nature Communications. 13(1). 4375–4375. 24 indexed citations
12.
Eales, Oliver, Haowei Wang, David Haw, et al.. (2022). Trends in SARS-CoV-2 infection prevalence during England’s roadmap out of lockdown, January to July 2021. PLoS Computational Biology. 18(11). e1010724–e1010724. 14 indexed citations
13.
Chadeau‐Hyam, Marc, Oliver Eales, Barbara Bodinier, et al.. (2022). Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study. EClinicalMedicine. 48. 101419–101419. 10 indexed citations
14.
Elliott, Joshua, Matthew Whitaker, Barbara Bodinier, et al.. (2021). Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people. PLoS Medicine. 18(9). e1003777–e1003777. 65 indexed citations
15.
Elliott, Paul, David Haw, Haowei Wang, et al.. (2021). Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant. Science. 374(6574). eabl9551–eabl9551. 80 indexed citations
16.
Riley, Steven, Kylie E. C. Ainslie, Oliver Eales, et al.. (2021). Resurgence of SARS-CoV-2: Detection by community viral surveillance. Science. 372(6545). 990–995. 52 indexed citations
17.
Eales, Oliver, et al.. (2021). mrc-ide/reactidd: Journal resubmission. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
18.
Riley, Steven, Oliver Eales, Haowei Wang, et al.. (2020). REACT-1 round 7 interim report: fall in prevalence of swab-positivity in England during national lockdown. medRxiv. 2 indexed citations
19.
Riley, Steven, Oliver Eales, Haowei Wang, et al.. (2020). High prevalence of SARS-CoV-2 swab positivity and increasing R number in England during October 2020: REACT-1 round 6 interim report. medRxiv. 8 indexed citations
20.

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.

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