Sarah Cobey

7.0k total citations · 2 hit papers
58 papers, 3.2k citations indexed

About

Sarah Cobey is a scholar working on Epidemiology, Infectious Diseases and Genetics. According to data from OpenAlex, Sarah Cobey has authored 58 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Epidemiology, 21 papers in Infectious Diseases and 15 papers in Genetics. Recurrent topics in Sarah Cobey's work include Influenza Virus Research Studies (35 papers), Respiratory viral infections research (16 papers) and COVID-19 epidemiological studies (15 papers). Sarah Cobey is often cited by papers focused on Influenza Virus Research Studies (35 papers), Respiratory viral infections research (16 papers) and COVID-19 epidemiological studies (15 papers). Sarah Cobey collaborates with scholars based in United States, Hong Kong and United Kingdom. Sarah Cobey's co-authors include Marc Lipsitch, Mercedes Pascual, Scott E. Hensley, Katia Koelle, Bryan T. Grenfell, Yonatan H. Grad, Daniel B. Larremore, Joseph A. Lewnard, Stephen M. Kissler and Kate M. Bubar and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Clinical Investigation.

In The Last Decade

Sarah Cobey

56 papers receiving 3.2k citations

Hit Papers

Model-informed COVID-19 vaccine prioritization s... 2017 2026 2020 2023 2021 2017 100 200 300 400

Peers

Sarah Cobey
Jonathan M. Read United Kingdom
Julia R. Gog United Kingdom
Katia Koelle United States
Katja Höschler United Kingdom
Colin A. Russell United States
Sarah Cobey
Citations per year, relative to Sarah Cobey Sarah Cobey (= 1×) peers Kevin Fonseca

Countries citing papers authored by Sarah Cobey

Since Specialization
Citations

This map shows the geographic impact of Sarah Cobey'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 Sarah Cobey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarah Cobey more than expected).

Fields of papers citing papers by Sarah Cobey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sarah Cobey. 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 Sarah Cobey. The network helps show where Sarah Cobey may publish in the future.

Co-authorship network of co-authors of Sarah Cobey

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Cobey. A scholar is included among the top collaborators of Sarah Cobey based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sarah Cobey. Sarah Cobey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yang, Bingyi, Katelyn M. Gostic, Dillon C. Adam, et al.. (2025). Breadth of influenza A antibody cross-reactivity varies by virus isolation interval and subtype. Nature Microbiology. 10(7). 1711–1722.
2.
Kim, Kangchon, Marcos C. Vieira, Sigrid Gouma, et al.. (2024). Measures of Population Immunity Can Predict the Dominant Clade of Influenza A (H3N2) in the 2017–2018 Season and Reveal Age‐Associated Differences in Susceptibility and Antibody‐Binding Specificity. Influenza and Other Respiratory Viruses. 18(11). e70033–e70033. 4 indexed citations
3.
Cowling, Benjamin J., Sook‐San Wong, Jefferson Santos, et al.. (2024). Preliminary Findings From the Dynamics of the Immune Responses to Repeat Influenza Vaccination Exposures (DRIVE I) Study: A Randomized Controlled Trial. Clinical Infectious Diseases. 79(4). 901–909. 3 indexed citations
4.
Vieira, Marcos C., Anna-Karin E. Palm, Christopher T. Stamper, et al.. (2023). Germline-encoded specificities and the predictability of the B cell response. PLoS Pathogens. 19(8). e1011603–e1011603. 3 indexed citations
5.
Lin, Yun, Bingyi Yang, Sarah Cobey, et al.. (2022). Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission. Nature Communications. 13(1). 1155–1155. 18 indexed citations
6.
Arevalo, Philip, Katelyn M. Gostic, Massimo Pacilli, et al.. (2022). Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA. Nature Communications. 13(1). 5547–5547. 4 indexed citations
7.
Guthmiller, Jenna J., Henry A. Utset, Carole Henry, et al.. (2021). An Egg-Derived Sulfated N -Acetyllactosamine Glycan Is an Antigenic Decoy of Influenza Virus Vaccines. mBio. 12(3). e0083821–e0083821. 9 indexed citations
8.
Arevalo, Philip, Wayne A. Duffus, Manuela Runge, et al.. (2021). Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020. BMC Public Health. 21(1). 1105–1105. 17 indexed citations
9.
Dugan, Haley L., Jenna J. Guthmiller, Philip Arevalo, et al.. (2020). Preexisting immunity shapes distinct antibody landscapes after influenza virus infection and vaccination in humans. Science Translational Medicine. 12(573). 72 indexed citations
10.
Gouma, Sigrid, Kangchon Kim, Madison E. Weirick, et al.. (2020). Middle-aged individuals may be in a perpetual state of H3N2 influenza virus susceptibility. Nature Communications. 11(1). 4566–4566. 45 indexed citations
11.
Neu, Karlynn E., Jenna J. Guthmiller, Min Huang, et al.. (2018). Spec-seq unveils transcriptional subpopulations of antibody-secreting cells following influenza vaccination. Journal of Clinical Investigation. 129(1). 93–105. 26 indexed citations
12.
Cobey, Sarah, et al.. (2018). Use of an individual-based model of pneumococcal carriage for planning a randomized trial of a whole-cell vaccine. PLoS Computational Biology. 14(10). e1006333–e1006333. 6 indexed citations
13.
Zost, Seth J., Kaela Parkhouse, Megan E. Gumina, et al.. (2017). Contemporary H3N2 influenza viruses have a glycosylation site that alters binding of antibodies elicited by egg-adapted vaccine strains. Proceedings of the National Academy of Sciences. 114(47). 12578–12583. 399 indexed citations breakdown →
14.
Ranjeva, Sylvia, Edward B. Baskerville, Vanja Dukić, et al.. (2017). Recurring infection with ecologically distinct HPV types can explain high prevalence and diversity. Proceedings of the National Academy of Sciences. 114(51). 13573–13578. 49 indexed citations
15.
Cobey, Sarah, Edward B. Baskerville, Caroline Colijn, et al.. (2017). Host population structure and treatment frequency maintain balancing selection on drug resistance. Journal of The Royal Society Interface. 14(133). 20170295–20170295. 22 indexed citations
16.
Lipsitch, Marc, William Barclay, Rahul Raman, et al.. (2016). Viral factors in influenza pandemic risk assessment. eLife. 5. 66 indexed citations
17.
Cobey, Sarah & Marc Lipsitch. (2012). Niche and Neutral Effects of Acquired Immunity Permit Coexistence of Pneumococcal Serotypes. Science. 335(6074). 1376–1380. 121 indexed citations
18.
Bedford, Trevor, Sarah Cobey, & Mercedes Pascual. (2011). Strength and tempo of selection revealed in viral gene genealogies. BMC Evolutionary Biology. 11(1). 220–220. 51 indexed citations
19.
Bedford, Trevor, Sarah Cobey, Peter Beerli, & Mercedes Pascual. (2010). Global Migration Dynamics Underlie Evolution and Persistence of Human Influenza A (H3N2). PLoS Pathogens. 6(5). e1000918–e1000918. 127 indexed citations
20.
Cobey, Sarah & Katia Koelle. (2008). Capturing escape in infectious disease dynamics. Trends in Ecology & Evolution. 23(10). 572–577. 20 indexed citations

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.

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