Maya Moshe
Impact in
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
- Infectious Diseases top 10%
- SARS-CoV-2 and COVID-19 Research
- SARS-CoV-2 detection and testing
- COVID-19 Clinical Research Studies
Papers in
-
- SARS-CoV-2 and COVID-19 Research 7
- SARS-CoV-2 detection and testing 6
-
- Bacillus and Francisella bacterial research 1
- Co-authors
- William Barclay (9 shared papers)Jonathan C. Brown (4 shared papers)Christina Atchison (4 shared papers)Helen Ward (4 shared papers)Graham Cooke (4 shared papers)Ara Darzi (4 shared papers)Paul Elliott (4 shared papers)Saul Greenberg (1 shared paper)
- Journals
- Clinical Infectious Diseases (1 paper)PLoS Pathogens (1 paper)Clinical Microbiology and Infection (1 paper)Water Research (1 paper)The Lancet Microbe (1 paper)
- Partner nations
- United KingdomIsraelCanada
In The Last Decade
Maya Moshe
13 papers receiving 272 citations
Peers
Comparison fields: 5 of 76
- Modeling and Simulation 39
- Infectious Diseases 133
- Microbiology 25
- Critical Care and Intensive Care Medicine 13
- Health Informatics 3
Countries citing papers authored by Maya Moshe
This map shows the geographic impact of Maya Moshe'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 Maya Moshe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Moshe more than expected).
Fields of papers citing papers by Maya Moshe
This network shows the impact of papers produced by Maya Moshe. 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 Maya Moshe. The network helps show where Maya Moshe may publish in the future.
Co-authors
The 25 scholars most cited alongside Maya Moshe, 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 | 2021 | 63 | |
| 2 | 1987 | 57 | |
| 3 | 2023 | 35 | |
| 4 | 2014 | 29 | |
| 5 | 2022 | 22 | |
| 6 | 2017 | 21 | |
| 7 | 2021 | 17 | |
| 8 | 2021 | 13 | |
| 9 | 2022 | 9 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 4 | |
| 12 | 2022 | 2 | |
| 13 | 2017 | 1 |
About Maya Moshe
Maya Moshe is a scholar working on Infectious Diseases, Molecular Biology, Pulmonary and Respiratory Medicine, Oncology and Epidemiology, having authored 13 papers that have together received 282 indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (7 papers), SARS-CoV-2 detection and testing (6 papers), Infection Control and Ventilation (2 papers), Biosensors and Analytical Detection (2 papers), Cytomegalovirus and herpesvirus research (1 paper), Genetic and Kidney Cyst Diseases (1 paper), COVID-19 impact on air quality (1 paper) and Bacillus and Francisella bacterial research (1 paper). The work is most often cited by research in Modeling and Simulation (39 citations), Infectious Diseases (133 citations), Microbiology (25 citations), Critical Care and Intensive Care Medicine (13 citations) and Health Informatics (3 citations). Maya Moshe has collaborated with scholars based in United Kingdom, Israel and Canada. Frequent co-authors include William Barclay, Jonathan C. Brown, Christina Atchison, Helen Ward, Graham Cooke, Ara Darzi, Paul Elliott, Saul Greenberg, Morton Goldbach and Norman R. Saunders. Their work appears in journals such as Clinical Infectious Diseases, PLoS Pathogens, Clinical Microbiology and Infection, Water Research and The Lancet Microbe.
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.