Sam Moore
Impact in
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies
- Infectious Diseases top 10%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
Papers in
-
- COVID-19 epidemiological studies 8
- Health 1
- Vaccine Coverage and Hesitancy 1
- Co-authors
- Matt J. Keeling (8 shared papers)Edward M. Hill (7 shared papers)Louise Dyson (5 shared papers)Michael J. Tildesley (5 shared papers)Tim Rogers (1 shared paper)Lorenzo Pellis (1 shared paper)Katrina Lythgoe (1 shared paper)Thomas House (1 shared paper)
- Journals
- Nature Communications (3 papers)PLoS Computational Biology (2 papers)Global Health Action (1 paper)The Lancet Infectious Diseases (1 paper)Nature Medicine (1 paper)
- Partner nations
- United KingdomUnited StatesSwitzerland
In The Last Decade
Sam Moore
10 papers receiving 808 citations
Sam Moore's Hit Papers
Peers
Comparison fields: 5 of 64
- Modeling and Simulation 519
- Infectious Diseases 161
- Health 74
- Public Health, Environmental and Occupational Health 169
- Statistical and Nonlinear Physics 31
Countries citing papers authored by Sam Moore
This map shows the geographic impact of Sam Moore'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 Sam Moore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Moore more than expected).
Fields of papers citing papers by Sam Moore
This network shows the impact of papers produced by Sam Moore. 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 Sam Moore. The network helps show where Sam Moore may publish in the future.
Co-authors
The 13 scholars most cited alongside Sam Moore, 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 | Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study Hit paper breakdown → | 2021 | 419 |
| 2 | 2021 | 143 | |
| 3 | 2021 | 81 | |
| 4 | 2022 | 74 | |
| 5 | 2020 | 40 | |
| 6 | 2022 | 34 | |
| 7 | 2023 | 18 | |
| 8 | 2022 | 10 | |
| 9 | 2024 | 5 | |
| 10 | 2025 | 1 |
About Sam Moore
Sam Moore is a scholar working on Modeling and Simulation, Health, Statistical and Nonlinear Physics, Infectious Diseases and Nuclear Energy and Engineering, having authored 10 papers that have together received 825 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (8 papers), Complex Network Analysis Techniques (1 paper), Vaccine Coverage and Hesitancy (1 paper) and SARS-CoV-2 and COVID-19 Research (1 paper). The work is most often cited by research in Modeling and Simulation (519 citations), Infectious Diseases (161 citations), Health (74 citations), Public Health, Environmental and Occupational Health (169 citations) and Statistical and Nonlinear Physics (31 citations). Sam Moore has collaborated with scholars based in United Kingdom, United States and Switzerland. Frequent co-authors include Matt J. Keeling, Edward M. Hill, Louise Dyson, Michael J. Tildesley, Tim Rogers, Lorenzo Pellis, Katrina Lythgoe, Thomas House, Jacob Liam Curran-Sebastian and Bridget S. Penman. Their work appears in journals such as Nature Communications, PLoS Computational Biology, Global Health Action, The Lancet Infectious Diseases and Nature Medicine.
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.