Sam Moore
Impact in
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies
- Health top 5%
- Vaccine Coverage and Hesitancy
Papers in
-
- SARS-CoV-2 and COVID-19 Research 6
-
- COVID-19 epidemiological studies 6
- Co-authors
- Matt J. Keeling (7 shared papers)Edward M. Hill (6 shared papers)Michael J. Tildesley (5 shared papers)Louise Dyson (5 shared papers)Tim Rogers (1 shared paper)Katrina Lythgoe (1 shared paper)Thomas House (1 shared paper)Lorenzo Pellis (1 shared paper)
- Journals
- PLoS Computational Biology (2 papers)Nature Medicine (1 paper)The Lancet Infectious Diseases (1 paper)Physical Review Letters (1 paper)BMC Medicine (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Sam Moore
8 papers receiving 683 citations
Sam Moore's Hit Papers
Peers
Comparison fields: 5 of 89
- Modeling and Simulation 502
- Health 242
- Infectious Diseases 437
- Economics and Econometrics 107
- Public Health, Environmental and Occupational Health 97
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 10 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 | 388 |
| 2 | 2021 | 126 | |
| 3 | 2021 | 70 | |
| 4 | 2022 | 65 | |
| 5 | 2020 | 30 | |
| 6 | 2022 | 9 | |
| 7 | 2024 | 4 | |
| 8 | 2020 | 3 |
About Sam Moore
Sam Moore is a scholar working on Infectious Diseases, Modeling and Simulation, Health, General Health Professions and Clinical Psychology, having authored 8 papers that have together received 695 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (6 papers), SARS-CoV-2 and COVID-19 Research (6 papers), Vaccine Coverage and Hesitancy (5 papers), Complex Network Analysis Techniques (1 paper), Mental Health Research Topics (1 paper), COVID-19 Pandemic Impacts (1 paper), COVID-19 and Mental Health (1 paper) and Opinion Dynamics and Social Influence (1 paper). The work is most often cited by research in Modeling and Simulation (502 citations), Health (242 citations), Infectious Diseases (437 citations), Economics and Econometrics (107 citations) and Public Health, Environmental and Occupational Health (97 citations). Sam Moore has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Matt J. Keeling, Edward M. Hill, Michael J. Tildesley, Louise Dyson, Tim Rogers, Katrina Lythgoe, Thomas House, Lorenzo Pellis, Jacob Curran-Sebastian and Robin N. Thompson. Their work appears in journals such as PLoS Computational Biology, Nature Medicine, The Lancet Infectious Diseases, Physical Review Letters and BMC 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.