Génomique évolutive, modélisation et santé
Impact in
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies
- Infectious Diseases top 10%
- Antifungal resistance and susceptibility
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
Papers in
-
- COVID-19 epidemiological studies 53
-
- Antifungal resistance and susceptibility 78
- SARS-CoV-2 and COVID-19 Research 24
- Top scholars
- Simon CauchemezArnaud L FontanetLluís Quintana‐MurciHenrik SaljeJuliette PaireauAlexandre AlanioLin WangDerek A. T. Cummings
- Journals
- Nature Communications (27 papers)PLoS neglected tropical diseases (20 papers)Scientific Reports (18 papers)Medical Mycology (12 papers)Proceedings of the National Academy of Sciences (11 papers)
- Partner nations
- FranceUnited StatesUnited Kingdom
In The Last Decade
Génomique évolutive, modélisation et santé
530 papers receiving 17.3k citations
Peers
Comparison fields: 5 of 202
- Modeling and Simulation 3.2k
- Infectious Diseases 4.1k
- Public Health, Environmental and Occupational Health 3k
- Immunology 1.1k
- Epidemiology 1.7k
Countries citing scholars working at Génomique évolutive, modélisation et santé
This map shows the geographic impact of research produced by authors working at Génomique évolutive, modélisation et santé. 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 papers produced at Génomique évolutive, modélisation et santé with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Génomique évolutive, modélisation et santé more than expected).
Fields of papers published by authors at Génomique évolutive, modélisation et santé
This network shows the impact of papers affiliated with Génomique évolutive, modélisation et santé at the time of their publication. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers affiliated with Génomique évolutive, modélisation et santé at the time of their publication.
About Génomique évolutive, modélisation et santé
In recent decades, authors affiliated with Génomique évolutive, modélisation et santé have published 566 papers, which have received a total of 17.3k indexed citations . Scholars at this organization have produced 53 papers in Modeling and Simulation, 119 papers in Infectious Diseases, 153 papers in Public Health, Environmental and Occupational Health, 4 papers in Microbiology and 19 papers in Parasitology on the topics of Mosquito-borne diseases and control (123 papers), Antifungal resistance and susceptibility (78 papers), COVID-19 epidemiological studies (53 papers), Fungal Infections and Studies (40 papers), Forensic and Genetic Research (34 papers), Genetic Associations and Epidemiology (25 papers), SARS-CoV-2 and COVID-19 Research (24 papers) and Mathematical and Theoretical Epidemiology and Ecology Models (18 papers). Their work is cited by papers focused on Modeling and Simulation (3.2k citations), Infectious Diseases (4.1k citations), Public Health, Environmental and Occupational Health (3k citations), Immunology (1.1k citations) and Epidemiology (1.7k citations). Authors at Génomique évolutive, modélisation et santé collaborate with scholars in France, United States and United Kingdom and have published in prestigious journals including Nature Communications, PLoS neglected tropical diseases, Scientific Reports, Medical Mycology and Proceedings of the National Academy of Sciences. Some of Génomique évolutive, modélisation et santé's most productive authors include Simon Cauchemez, Arnaud L Fontanet, Lluís Quintana‐Murci, Henrik Salje, Juliette Paireau, Alexandre Alanio, Lin Wang, Derek A. T. Cummings, Moritz U. G. Kraemer and Maylis Layan.
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.