Jonathan M. Read
- Modeling and Simulation top 0.1%
- COVID-19 epidemiological studies 44
- Infectious Diseases top 1%
- Viral gastroenteritis research and epidemiology 7
- Epidemiology top 2%
- Influenza Virus Research Studies 22
- Respiratory viral infections research 19
- Data-Driven Disease Surveillance 8
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- Complex Network Analysis Techniques 8
- Health top 5%
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- Mathematical and Theoretical Epidemiology and Ecology Models 7
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- COVID-19 Pandemic Impacts 6
- Co-authors
- Matt J. KeelingDerek A. T. CummingsW. John EdmundsLeón DanonSteven RileyKen EamesJustin LesslerEllen Brooks‐Pollock
- Journals
- Scientific Reports (5 papers)Proceedings of the Royal Society B Biological Sciences (5 papers)Influenza and Other Respiratory Viruses (5 papers)
- Partner nations
- United KingdomUnited StatesHong Kong
In The Last Decade
Jonathan M. Read
91 papers receiving 3.4k citations
Hit Papers
Peers
Comparison fields: 5 of 159
- Modeling and Simulation 1.5k
- Infectious Diseases 1.2k
- Epidemiology 1.1k
- Statistical and Nonlinear Physics 336
- Health 230
Countries citing papers authored by Jonathan M. Read
This map shows the geographic impact of Jonathan M. Read'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 Jonathan M. Read with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan M. Read more than expected).
Fields of papers citing papers by Jonathan M. Read
This network shows the impact of papers produced by Jonathan M. Read. 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 Jonathan M. Read. The network helps show where Jonathan M. Read may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jonathan M. Read, 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 | 2024 | 3 | |
| 2 | 2024 | 2 | |
| 3 | 2023 | 2 | |
| 4 | 2023 | 1 | |
| 5 | 2023 | 0 | |
| 6 | 2023 | 8 | |
| 7 | 2022 | 3 | |
| 8 | 2021 | 10 | |
| 9 | 2021 | 1 | |
| 10 | Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimatesbreakdown → | 2021 | 188 |
| 11 | 2021 | 6 | |
| 12 | 2020 | 223 | |
| 13 | 2020 | 10 | |
| 14 | 2020 | 7 | |
| 15 | 2020 | 13 | |
| 16 | 2019 | 8 | |
| 17 | 2018 | 16 | |
| 18 | 2017 | 2 | |
| 19 | 2012 | 158 | |
| 20 | 2011 | 47 |
About Jonathan M. Read
Jonathan M. Read is a scholar working on Modeling and Simulation, Infectious Diseases, Epidemiology, Health and Statistical and Nonlinear Physics, having authored 93 papers that have together received 3.4k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (44 papers), Influenza Virus Research Studies (22 papers), Respiratory viral infections research (19 papers), Data-Driven Disease Surveillance (8 papers), Complex Network Analysis Techniques (8 papers), Viral gastroenteritis research and epidemiology (7 papers), Mathematical and Theoretical Epidemiology and Ecology Models (7 papers) and COVID-19 Pandemic Impacts (6 papers). The work is most often cited by research in Modeling and Simulation (1.5k citations), Infectious Diseases (1.2k citations), Epidemiology (1.1k citations), Statistical and Nonlinear Physics (336 citations) and Health (230 citations). Jonathan M. Read has collaborated with scholars based in United Kingdom, United States and Hong Kong. Frequent co-authors include Matt J. Keeling, Derek A. T. Cummings, W. John Edmunds, León Danon, Steven Riley, Ken Eames, Justin Lessler, Ellen Brooks‐Pollock, Louise Dyson and T. Déirdre Hollingsworth. Their work appears in journals such as Scientific Reports, Proceedings of the Royal Society B Biological Sciences, Influenza and Other Respiratory Viruses, International Journal of Infectious Diseases and BMJ Open.
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.